MongoDB Reports Strong Q2 FY2026 Results, Highlighting AI-Driven Growth and Platform Expansion

MongoDB Q2 FY2025 Earnings Call Transcipt - MongoDB (NASDAQ:MDB)

MongoDB, Inc. MDB released its second-quarter financial results after Tuesday's closing bell. Below are the transcripts from the second quarter earnings call. This transcript is brought to you by Benzinga APIs. For real-time access to our entire catalog, please visit https://www.benzinga.com/apis/ for a consultation. MDB stock is up after hours on Tuesday. Get the details here. OPERATOR Good day everyone and welcome to MongoDB's second quarter fiscal year 2026 earnings call. @ this time all participants are in a listen only mode. After the presentation there will be a question and answer session. To participate you will need to press star 11 on your telephone. You will then hear a message advising your hand is raised to withdraw your question, simply press star 11 again. Please note this conference is being recorded now. It is my pleasure to turn the call over to Brian Deneau from ICR. Please go ahead. Moderator Thank you, Carmen. Good afternoon and thank you for joining us today to review MongoDB's second quarter. quarter fiscal 2026 financial results which we announced in our press release issued after. the close of market today. Joining me on the call today are: Dave Vidacharya, President and CEO of MongoDB. Mike Berry, CFO of MongoDB and Jess Lubert, MongoDB's new Vice President of Investor Relations. During this call we will make forward looking statements including statements related to our. Market and future growth opportunities, our opportunity. To win new business, our expectations regarding Atlas consumption growth, the impact of non-Atlas business and multi-year license revenue, the. Long term opportunity of AI, our financial. Guidance and underlying assumptions and our investments and growth opportunities in AI. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could. Cause actual results to differ materially from our expectations. For discussion of the material risks and uncertainties that could affect our actual results. Please refer to the risks described in our Quarterly report on Form 10Q for. The quarter ended April 30, 2025 filed with the SEC on June 4, 2025. Any forward looking statements made on this call reflect our views only as of today and we undertake no obligation to update them except as required by law. Additionally, we will discuss non GAAP financial. Measures on this conference call. Please refer to the tables in the earnings release on the Invest Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measure. With that, I'd like to turn the call over to Dave. Dev Ittycheria (President and CEO) Thank you Brian and thank you to everyone for joining us today. Before discussing our strong quarter, I want to remind everyone about our upcoming Investor Day which will take place on September 17th at the Javits center in New York City. During our DOT Local conference, we'll spend the day discussing the investments we're making to drive durable growth and margin expansion and our view of the future. I look forward to seeing you there. Now onto Q2 I'm pleased to report another strong quarter as we continue to execute against our large market opportunity. Let me start with our results before giving you a broader company update. We generated revenue of $591 million, up 24% year over year and above the high end of our guidance. Atlas revenue grew 29% year over year, representing 74% of total revenue. We delivered non GAAP operating income of $87 million for a 15% non GAAP operating margin and we ended the quarter with over 59,900 customers. Atlas performance was strong, accelerating, to 29% year over year growth, up from 26% in Q1. Our customer additions were also robust. We have added over 5,000 customers over the last two quarters. These results reflect the strength of MongoDB's platform, our flexible document model, expanded capabilities like search and vector search, enterprise readiness and the ability to run anywhere. Many of our recently added customers are building AI applications, underscoring how our value proposition is resonating for AI and why MongoDB is emerging as a key component of the AI infrastructure stack. At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while expanding profitably. In short, our results show that customers are choosing MongoDB. Let me tell you why. First, MongoDB is an enterprise ready database capable of meeting the most stringent enterprise requirements. Over 70% of the Fortune 500 as well as 7 of the 10 largest banks, 14 of the largest, 15 healthcare companies, 9 of the 10 largest manufacturers globally are MongoDB customers. MongoDB is a battle tested enterprise platform relied on by some of the most sophisticated and demanding organizations in the world, in part because of our strong enterprise posture across security, durability, availability and performance. Atlas enabled one of the world's largest automakers to overcome postgres scalability and flexibility limits while reducing complexity. The company's Magic console tracks over 8.5 million vehicles, requiring a modern schema to handle both structured and unstructured data, something postgres could not handle. Ultimately, Atlas consolidated infrastructure accelerate innovation and support the scale of millions of connected vehicles. Second, MongoDB is suitable for a broad range of use cases, including the most mission critical and transaction intensive applications. MongoDB has also supported full asset transactions for more than six years, ensuring strong consistency and data integrity at scale. This is why some of the world's most demanding transactional workloads run on MongoDB today. For example, Deutsche Telekom selected MongoDB Atlas as the foundation for its internal developer platform, which includes mission critical workloads like contract management, device purchases and billing for 30 million customers. With 90 Atlas clusters managing over 60 million customer records, Deutsche Telekom's customer data platform now handles 15 times the concurrent logins of legacy systems. By consolidating these high volume, transaction intensive applications on MongoDB, Deutsche Telekom has improved resiliency, accelerated innovation and delivered a step change in customer engagement. Third, MongoDB has redefined what's core for the database by natively including capabilities like search, vector search, embeddings and stream processing. Comparing MongoDB to another database like Postgres is not an apples to apples comparison. Take a global e commerce application that manages inventory and order data while enabling product discovery through sophisticated search across millions of SKUs. The choice for this application not between MongoDB or Postgres is between MongoDB or Postgres, plus other offerings like Pinecone Elastic and Cohere for embeddings. MongoDB's complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward. For example, Agibank, a Brazilian neobank with 2.7 million active customers, migrated their content management system storing customer records from postgres to Atlas. As data volumes grew, postgres inflexibility and task execution latency drove performance issues and the database lacked sophisticated secondary indexes and full text search, hurting sales of core offerings such as loans, insurance and card approvals. Agibank was constantly updating the database and manually scaling infrastructure, which is both time consuming and error prone. With Atlas, Agibank gained a resilient flexible system that handled rising demand and support new services delivering nearly five times better performance and 90% lower costs, all with no outages. Fourth, MongoDB is emerging as a standard for AI applications. Over the last few quarters we've seen a strength in our self serve channel, driven in part by AI native startups choosing Atlas as the foundation for their applications. In the enterprise segment, adoption is real but early Much of the activity today centers on employee productivity tools and packaged ISV solutions. Enterprises are still in the very early stages of building their own custom AI applications that will transform their business. We consistently hear from customers that when teams try to scale from vive coded prototypes built on relational back ends to enterprise grade deployments, these platforms quickly hit limits in flexibility, scalability and performance across startups and increasingly enterprises. Our unified platform is resonating strongly in the enterprise segment. A leading electric vehicle company chose Atlas and Vector Surge to power its autonomous driving platform after testing VectorSearch against Postgres PGVector for their in vehicle voice assistant. They selected Monodb for superior performance at scale and stronger ROI. They now rely on Atlas to handle over 1 billion vectors and expect 10 times growth in data usage by next year. Devred Devrev, a well funded AI native platform with proven founders disrupting the help desk market, built agent OS its complete agentI platform that autonomously handles billions of monthly requests on Atlas. DevRev Accelerated Development Velocity, lower cost and scale globally with low latency by using atlas. Agent OS also leverages Atlas vector search for semantic search, enriching its knowledge graph and LLMs with domain specific content Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost effectiveness and AI readiness they need all in one platform. As we look ahead, we remain confident in MongoDB's position to lead both the current wave of digital transformation and the next wave powered by AI. With that, here's Mike. Mike Berry (Chief Financial Officer) Thanks Dave. I'll begin with a detailed review of our second quarter results and then finish with our outlook for the third quarter and fiscal year 26. I will be discussing our results on a non GAAP basis unless otherwise noted. As Dave mentioned, we had a great quarter as we exceeded all of our guidance ranges and are increasing our full year guidance across the board. Now onto the Results. In the second quarter total revenue was $591 million, up 24% year over year and above the high end of our guidance. Shifting to our product mix, Atlas revenue outperformed our expectations and year over year growth accelerated to 29% in the quarter and now represents 74% of total revenue. This compares to 71% in the second quarter of fiscal 25 and 72% last quarter we had an impressive Atlas growth quarter which benefited in part from the strong start to consumption in May that we referenced on our last call, as well as broad based strength especially in larger customers in the us. Let me provide some context on Atlas consumption in the quarter in Q2. Atlas consumption growth was strong and relatively consistent with last year's growth rates. This drove the acceleration in revenue as well as the growth in absolute revenue dollars year to date for the first half of fiscal 26. Turning to non Atlas revenue came in ahead of our expectations in the quarter as we continue to have success selling incremental workloads into our existing Enterprise Agreement (EA) customer base. Non Atlas ARR, which reflects the underlying revenue growth of this product line without the impact of changes in duration, grew 7% year over year. In addition to the good underlying trends in non Atlas. In Q2 we also benefited from more multi year deals than expected reflecting our customers desire to commit to building with Mongodb long term. Approximately half of the non Atlas revenue outperformance versus Guidance was attributable to multi year outperformance. We had another strong quarter for customer adds in the second quarter as we grew our customer base by approximately 2,800 sequentially bring in the total customer count to 59,900 which is up from over 50,700 in the year ago period. This quarter we incorporated new customers added from the Voyage AI acquisition to our customer count representing 300 of the 2,800 added. The growth in our total customer count is being driven primarily by Atlas which had over 58,300 customers at the end of the quarter compared to over 49,200 in the year ago period. It is important to keep in mind the growth in our Atlas customer count reflects new customers to Mongo DB in addition to existing Enterprise Agreement (EA) customers deploying workloads on Atlas for the first time. Of our total customer count over 7,300 are direct sales customers, a decline of 200 customers sequentially and flat year over year. These metrics are largely due to our decision to reallocate a portion of our go to market resources from the mid market to the enterprise channel starting in the second half of last year. This does not impact our total customer count but is an output of fewer self serve originated customers being elevated to our direct sales team as we move upmarket. In Q2 our total company net AR expansion rate was approximately 119% which is consistent with recent quarters. We ended the quarter with 2,564 customers with at least $100,000 in ARR representing 17% growth versus the year ago period. Moving down the income statement, gross profit in the second quarter was $436 million representing a gross margin of 74% which is down from 75% in the year ago period. Our year over year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Our income from operations was $87 million for a 15% operating margin compared to 11% in the year ago period. We are very pleased with our stronger than expected margin result operating margin results which benefited mainly from our revenue outperformance. Additionally, I'd like to provide a little context on the modest restructuring we undertook in the quarter. It impacted less than 2% of employees and resulted in approximately $5 million of one time charges which we have excluded from our non GAAP financials. This action is consistent with the key priorities I outlined for you last quarter to identify ways to both reallocate existing spend to higher ROI opportunities and be more disciplined about incremental spending. We are focused on running an efficient, scalable business that supports growth in revenue and profitability to drive long term shareholder value. Net income in the second quarter was $87 million, or $1 per share, based on 87 million diluted shares outstanding. This compares to a net income of $59 million, or $0.70 per share, on 84 million diluted shares outstanding in the year ago period. Turning to the balance sheet and cash flow, we ended the second quarter with $2.3 billion in cash, cash equivalents, short term investments and restricted cash. During the quarter we spent $200 million to repurchase approximately 930,000 shares, which was under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $72 million and free cash flow was $70 million, which compares to negative 1 million and negative $4 million respectively, in the year ago period. Our strong cash flow results were driven primarily by strong operating profit and higher cash collections. Before turning to our outlook in greater detail, I'd like to share the key points driving how we are looking at the rest of fiscal year 26. Number one, we are raising our expectations for revenue based on our confidence in Atlas as well as a strong performance in the first half of the year, providing a higher starting point for Atlas heading into the second half. Number two, we are increasing our operating margin guidance by 150 basis points at the high end, reflecting our strong Q2 performance and continued focus on margin improvement. And number three, we are raising our operating margin guidance while still continuing to make incremental investments for growth with a focus on R and D and developer awareness. Now, moving on to our full year guidance, I'd like to provide some incremental comments on our expectations. First, as we discussed, we had a strong start to the year and are confident in our ability to drive continued revenue and profitability growth. We are raising our full year revenue guidance by $70 million, including the $38 million outperformance in Q2. This reflects. Excuse me, this reflects the strong Q2 consumption benefiting revenue in the second half and our continued confidence in Atlas growth All this implies mid-20s percentage growth for Atlas in the second half of the year. Second, incorporating our strong performance in the first half, we expect non Atlas subscription revenue will now be down in the middle single digits for the year compared to our prior expectation of high single digit decline. We also expect the headwind for multiyear license revenue for fiscal 26 to now be $40 million due to the Q2 outperformance compared to our prior expectation of approximately $50 million. Please note we expect non atlas ARR will continue to grow year over year. Finally, we are raising our expectations for operating margin to 14% at the high end up from 12.5% in our prior quarter guidance. This reflects the better than expected revenue performance, the impact of our more disciplined approach to investing for growth and our increased focus on efficiency for fiscal year 26. We now expect revenue to be in the range of 2.34 to $2.36 billion, an increase of $70 million from our prior guide. We are raising our non GAAP income from operations expectations by $44 million and are now targeting a range of 321 to $331 million and non GAAP net income per share to be in the range of $3.64 to $3.73 based on 87.4 million diluted shares outstanding. Note that the non GAAP net income per share guidance for the third quarter and fiscal year 26 assumes a non GAAP tax provision of 20%. Moving on to our Q3 guidance, a few things to keep in mind. First, we expect to see a low 20% year over year percentage decline in the non Atlas business and after the strong multiyear outperformance we experienced in Q3 of fiscal year 25. As a reminder, Q3 of last year was our strongest multi year revenue quarter and is the largest portion of the multi year headwind. Second, we expect operating margin will be lower than in Q2 primarily due to the expected sequential decline in non Atlas revenue which is very high margin revenue. In addition, it is also impacted by the timing of operating expenses, specifically R and D hiring and seasonality of our marketing investments. With that context, I will now turn to our outlook for the third quarter. For the third quarter we expect revenue to be in the range of 587 to 592 million dollars. We expect non GAAP income from operations to be in the range of 66 to 70 million dollars and non GAAP net income per share to be in the range of 76 to 79 cents based on 87.7 million diluted shares outstanding. To summarize, we had a very strong quarter. We are pleased with our ability to drive revenue growth across the business and increase our operating profit expectations we remain incredibly excited about the opportunity ahead and will continue to invest responsibly to drive long term shareholder value. I would also like to take a moment to extend a warm welcome to Jess Lubert, our new Vice President of Investor Relations, who started with us yesterday. Jess joins us from Juniper Networks where he led their investor relations effort, including most recently helping the company navigate the acquisition by Hewlett Packard Enterprise. We're excited to have him on board and eager to see the impact of his work. Last but not least, we look forward to seeing many of you in a few weeks at our Investor Day. Please reach out to our investor relations [email protected] with any questions with that. We'd like to open it up for questions. Carmen, take it away. OPERATOR Thank you so much. And as a reminder that it's to time to get in the queue and wait for your name to be announced. To withdraw the question, simply press star 11 again. Our first question is from Sanjit Singh with Morgan Stanley. Please proceed. Morgan Stanley Analyst Hi, thank you for taking that question. And congrats on a heck of a quarter in Q2. I wanted to dive into some of the drivers into Q2. When I look at the acceleration atlas. Which is now accelerated for two quarters. In a row and I kind of just look at the sequential dollar add I had that up more than 40 million in Q2, which is the strongest sequential dollar adds we've seen in quite some time in what's been a pretty sober cloud spending environment. I was wondering if you could give us some sense of the drivers. Of. The strong sequential adds this quarter. I know you pointed to May, but if anything you can give us from a workload perspective or any other new. Factors, maybe the workloads from last year. Are starting to ramp. I just love to understand that trajectory. Dev Ittycheria (President and CEO) Yeah, Sanjit, thanks for the question. So clearly we're really pleased by the quarter and really pleased by the accelerating growth in Atlas. I would say a lot of it was due to the workloads that we acquired over the past year, especially with our move up market that are growing faster and becoming bigger than previous workloads we've seen. So I think the move up market is really paying off and what we're also seeing is that there's a great uptick of some of the other capabilities we offer like search and vector search that are also adding to that growth of those workloads. And then as we mentioned, we also acquired a ton of new customers. Obviously the self serve customers tend to spend less on a per customer basis. But we also have added lots of customers over the last six months and I think that's also helping drive some of the growth. Morgan Stanley Analyst Yeah, that's great. Color. I wanted to follow up on the Go to market side. Over the last couple of years we've been sort of tinkering and optimizing the go to market organization across sort of territory investment but also sort of quotas and moving to incremental consumption. Could you give us an update on the state of operations for the salesforce today? And in some sense if I look at the customer ads, it seems like things are humming quite well. But just to get to understand how. Like what's the state of the organization today? That'd be really helpful. Dev Ittycheria (President and CEO) Yeah, sure. So nothing really has changed. We're just doubling down on what we said previously. We are moving up markets, we're focusing our high end sales force focused on the most sophisticated and demanding customers. These are typically enterprise customers all around the world. And then we're using our self serve channel to better serve the SMB market. I know there are a lot of questions about where we kind of abandoning the self serve the early stage market by this move and I think the results over the last couple of quarters have shown that we are not. I think we're just becoming much more effective in serving that market while also being very effective in growing our wallet share in these larger accounts. So we're really just continuing with the strategy that we articulated before and obviously we're pleased with the results. Appreciate the thoughts Dave. Morgan Stanley Analyst Thank you. OPERATOR Thank you Sanjit. Thank you. Our next question is from Raymond Lenchal with Barclays. Please proceed. Barclays Analyst Perfect. Thank you. First of all, congrats to Jess. All the best. Two quick questions for me. Staying on that theme of self service, that acceleration, Dave, obviously you changed things around but it's accelerated despite you actually moving up market. Can you help us understand then what's driving that a little bit and then have one follow up for Mike? Dev Ittycheria (President and CEO) Yeah, I mean clearly the output metrics look really good but I would say the work around self serve began, has been going on for a while. The team is really good at running experiments using a data driven approach to figure out what's working, to figure out what's not working. A new motion that we're also doing that's showing good results is going after SQL developers who don't really know MongoDB and attracting them to our platform, really helping them understand the value proposition of MongoDB. Even running things like office hours where we spend time with SQL developers to explain the benefits of modeling data on a document database. And all these experiments and tactics that we're doing, which are very data driven, are really paying off. And May Petrie used to run that group, is now our cmo and she has a strong team under her and we feel really good about what that self serve team has been doing and but again, we don't want to declare a victory too early, but obviously we're very pleased with the results. Barclays Analyst Yeah, no, that's really nice to see. And then Mike, thanks. First of all for all the extra disclosure. The ARR for the non ATLAS or EA part is kind of really helpful if you think about the I get the logic around the renewal cohorts, especially. Q3, but am I doing the RAF. Correctly that actually next year that part of the business looks more interesting because the cohort looks better? Like just trying to get your idea or and maybe you might not even give it to us because you just do. ARR. Thank you. Mike Berry (Chief Financial Officer) Sure. So thanks for the question. So I'm going to hold that answer until we get to Q3 of next year because it kind of depends on what happens in Q3 of this year. So the one thing is, as we talked about, the big impact in Q3 of this year is the multi year. We'll see how it comes back next year. But it really depends, Raymond, on how we do in Q3 this year. OPERATOR Thanks Raymond. Thank you so much. And our next question comes from Tyler Radke with Citi. Please proceed. Citi Analyst Hey, thanks for taking the question and nice job on the ATLAS growth. Wanted to dig into the AI commentary that you had, Dave. Obviously last quarter you talked about cursor, which obviously is ramping up significantly in terms of their ARR. And I think you called out many examples this quarter, including autonomous vehicle company. Sounds like expecting pretty significant growth there. But how much of that is playing into the Atlas strength that you're seeing here in the quarter? Any way to quantify that cohort or use cases, whether it's vector search or maybe even if you throw in voyage, just help us understand if that's starting to move the needle because it sounds like there's some pretty high profile wins in there. Dev Ittycheria (President and CEO) Yeah, thanks for the question, Tyler. While we're adding thousands of AI native customers, I will tell you that the growth that we delivered this quarter was not material to that growth. The growth was really driven by our core business and our core customer base. While we're very happy with the AI customers increasingly choosing MongoDB, it was not a material mover of the needle for our growth. Citi Analyst Great. And then follow up on the migration opportunity. I know you've been investing in Relational Migrator, you're working with companies like Cognition to accelerate the code migration opportunity and you've seen professional services ramp up a little bit. But where have you started to see sort of the time to migration or re platform improve a bit? Just anything you could share in terms of that migration opportunity, if that's started to improve in terms of velocity or size of workload, migration would be helpful. Thank you. Dev Ittycheria (President and CEO) So yes, we're super excited about what we call app modernization or legacy app monetization. You'll hear a lot more about this at Investor Day and September, Tyler. But what I will say is that the value proposition is very clear. Customers are very, very motivated to try and modernize these legacy systems for a wide variety of reasons. We are seeing a lot of progress. We've actually brought in a new leader, new product leader who brings a lot of depth and scale, especially around AI to help us build the tooling to leverage AI to really drive more automation in terms of how we analyze and refactor the code. We brought in a new leader last quarter to help really help drive the delivery and the go to market efforts around AppMod. So we're definitely beefing up resources and I would say that we're investing a lot in product and there's a lot more to do. And I would say this is something that we're very excited about but it'll drive more of our longer term growth. It won't be as pronounced in terms of this year, but we're very, very excited about the opportunity and we definitely will spend more time discussing this and what we're actually doing on the product side in September. Thank you. OPERATOR Thank you. One moment for our next question. It comes from Jason Ader with William Blair. Please proceed. William Blair Analyst Yeah, thank you. Dave. I was hoping you could talk about some of the kind of latest industry developments just on the technology side in particular, I'm thinking about Lake Base from DataBricks and then DocumentDB and the Linux Foundation. Can you just comment on both those things and how they might impact MongoDB and how you differentiate. Dave Ittycheria (President and CEO) Yeah, so let me tackle them one by one. Clearly what we are seeing is that the strategic high ground for AI, especially when it comes to inference, is OLTP. So we talked about this on the last call where some companies had acquired early stage OLTP startups and what it really spoke to when those companies had spoken about their organic efforts to build an OLTP platform. And I think what it spoke to was the fact that building an OLTP platform that's ready and mission critical and enterprise can serve the most demanding requirements of enterprises is not trivial. And I think they basically threw in the towel and decided to do these acquisitions. And what it just reinforces that OLTP is the strategic high ground for AI. And we believe that if now customers are going to be choosing what OLTP platform that they want for AI. Just given our architecture, just given the fact that we have a durable architectural advantage in terms of JSON support, which addresses messy, complicated and highly interdependent and constantly changing data structures, the fact that we integrated search and vector search I think really helps us position going after AI. With regards to your second question around the Linux Foundation, I think what this really also suggests shows is that real JSON is much more important now with AI than ever before and the clones and bolt ons that have traded off features and performance and developer experience have just not met customer expectations. And candidly what I see is that the hyperscalers are investing less and really handing off to the open source community to kind of really take on the bulk of the work in terms of product development. Our hyperscaler partnerships remain strong and I think we have the right open source model where we can balance the access to free software while preserving the ability to both generate and capture value. Great, thank you. William Blair Analyst And then just one quick follow up. Why do we hear so much about Postgres adoption for AI startups? You talked about the success you guys are having. But if postgres has the disadvantages that you've talked about multiple times, scalability, JSON support, how come we hear so much about that kind of at least in the early stages of AI? Dev Ittycheria (President and CEO) Yeah, that's a really good question and I think it's important to understand and we spend a lot of time, we have now invested in a team in the Bay Area that spends a lot of time with the startup community. What's become clear is a lot of these startup founders don't think that hard about their database choice. They kind of go with what they know. And what we are seeing is that as some of these startups are scaling, they're running to real scaling challenges with Postgres and we've talked about this in the past, like when you add a JSON when you use JSONB on Postgres, a 2 kilobyte document or bigger starts really creating performance problems because Postgres has to do something called off road storage, which creates enormous performance overheads. And so developers need a platform that can handle structured, semi structured, unstructured data. They need obviously a platform that performs well and they need a platform that can scale as they grow. And what we're hearing clearly from the startup community is that Postgres in many cases is not scaling for them and they're now coming to us and so we feel really good about our position. But the reality is that a lot of these AI founders kind of start with what they know or what they've used in the past. And only when the business starts scaling do they start recognizing the challenges. And we realize we need to do more developer education and do more work. And so we're investing a lot in the startup community. We're running a big event in October in San Francisco with a big hackathon and we're inviting lots of customers to participate. But that's just the start of a meaningful investment we're making in the Bay Area and the AI startup community to rethink their decisions around just going with what they know. Thank you. OPERATOR Thank you. One moment for our next question. That comes from Mike Cikos with Needham. Please proceed. Needham Analyst Hey, thanks for taking the questions guys. I just wanted to come back to Atlas specifically and Mike, appreciate last quarter you gave us some very granular color around Atlas Trends. Was hoping we could get an update on how Atlas Trends played out this quarter or just at the very least why we did see such broad based strength from large customers this quarter. Thank you. Mike Berry (Chief Financial Officer) Sure. Thanks for the question Mike. So when we talk about consumption in the second quarter for Atlas as we talked about it performed well, grew 29% year over year over year. As we talked about Mike, the consumption growth were relatively consistent with last year and as we talked about on the last call, we started out with a strong May and we saw broad based strength across most of the GEOs and segments. So nothing to call out there. But we did see notable strength in the larger customers in the US and if we dive deeper on that one, as Dave talked about, we are seeing some workloads from our larger customers grow for longer and expand more than we have seen in the past. So that's good. While there's many moving parts in the consumption business, we also expect that there is benefit from our go to market changes and given the preponderance of our strategic accounts being in the U.S. no surprise that we saw that growth mostly in the US. And then lastly Mike, there is some benefit from comparing it to a little slower growth in Q1. So that would be the detail on Q2 as it relates to consumption growth. Needham Analyst Thank you for that. And if I could just squeeze maybe one more in on the outperformance that we saw this quarter from the multi year deals. And maybe I'm just misunderstanding here, but my assumption was the reason we were facing this outperformance was really tied to the fact that in prior years we've had some pretty big deals on the multi year front. And so to see some of these deals come in this year, is that a function of customers renewing earlier which is helping fill that larger divot that we previously expected. Is that a fair assumption or can you help me think through that a little bit more? Thank you. Mike Berry (Chief Financial Officer) So thanks for the golf analogy. No, it did not fill the divot. So in Q2 it was really, it was good underlying strength in ARR growth and then greater than expected multi year. There were really no pull forwards Mike, and this is a hard business to forecast because sometimes even customers don't know whether they're going to opt for an annual renewal or a multi year. So there were no pull forwards and there was nothing out of the ordinary. Very importantly we left the non atlas assumptions consistent with our last guidance, hence pulling down the multi year headwind from 50 to 40. And again nothing to call out on Q2, no pull forwards and there were really no large multi years in there. It was just across a good subset of customers. Needham Analyst Thank you again. OPERATOR Yep. Thank you. Our next question comes from the line of Alex Sukin with Wolfe Research. Please proceed. Wolfe Research Analyst Thanks for squeezing me in and I'll echo the congrats on truly amazing quarter I guess Dave, when you think about the AI comments that you've talked about both in the press release and in the call, maybe just a little bit more nuance in the use cases, not necessarily that you're seeing kind of contribute materially today, but the differentiation of the platform that you're able to incrementally take market share as it becomes available, both in net new kind of AI native companies but also in some of your larger existing companies or customers that are starting to modernize for this kind of conversational or AI native era, where are you seeing the most momentum in terms of workload construction and scale and when do you think we should expect to kind of actually start seeing that contribute more materially to the growth in consumption? Dev Ittycheria (President and CEO) Yeah, so thanks for the question. Alex, a couple of points again we're very pleased with the results of this quarter but I would Say the AI cohort was not a material driver of the growth. That being said, what we are seeing is a lot of customers very, very interested in our architecture. Let me again walk through why. One, we're a JSON database. JSON is the best way to express and model the complicated and messy and highly interdependent and constantly evolving data structures that you have to deal with in the real world. So that's point number one. So it's much easier to do that on MongoDB than to do that on some kludgy, you know, kind of set up on top of a relational database. Second is that we integrate search and vector search so you can do very sophisticated things to what people call hybrid search and retrieval. You can do very sophisticated things in finding information quickly, which is a very unique differentiator for us. So what this means is that rather than stitching together multiple systems, you can do this all in MongoDB so it becomes less complexity and lower cost. The third thing is that we've now embedded VOYAGE models on our platform. So if you control the embedding layer, you sit at the gateway of meeting of AI. What the embedding models do is really are a bridge between a company's private data and the LLM. So that becomes really important because the better the quality of the embedding model, the better the quality of the signal of your own data. So that reduces things like hallucinations or just bad outputs. And so customers are now, as people start caring more and more about like higher stake use cases, they really want to ensure those outputs are high. And the fact that it's part of our platform, we can enable you to do auto embeddings. It becomes an incredibly compelling feature in terms of the market. What I would say is that the enterprise uptake of AI is still early. I've said this for a couple years now and I think a lot of people, we're a little skeptical of what I said, but it's proving to be true. As we predicted. Like, you know, the lack of skills and the lack of trust with AI systems is kind of slowing. You know, people are being very cautious by deploying AI. Where it is being deployed is really on end user productivity. Whether it's developers with Cogen tools or business users using tools to summarize documents, extract data or things like deflecting tickets from people to systems with like conversational AI. I think you are starting to see the first steps in people deploying agent based systems. And I can talk a little bit about that, but that is still very, very early. We're seeing small ISVs, some of them are taking off who are really driving most of the impact. But the real enduring value will come when you talk to a customer today. Most of them, when you ask them is AI really transforming your business? They'll say no. Yes, we're seeing some productivity gains here and there, but it's not really transforming my business. I think the real enduring value will come when they build custom AI solutions that truly transform their business, whether it's to drive new revenue opportunities or dramatically reduce their existing cost structure. But we're really pleased I mentioned this electric car company that's very tech savvy, that's using MongoDB. I should mention one of the fastest growing startups in the Bay Area has Bet big on MongoDB. DevRev, the company going after the help desk space has built their own agentic platform AmongDB. So we feel really good about what this all portends for the future. But as I said, it was a small part of our growth this quarter. Wolfe Research Analyst Very helpful. And then maybe if I could just sneak one in for Mike. You've been kind of saying from I think the first day you started about how the margin profile of this business, it's not an or, it's an and and it's clearly coming through in both the growth acceleration but also the meaningful margin outperformance as you think sustaining this kind of accelerating pace and investing in things like the Bay Area startup community. How are you finding that balance that and versus or balance that quite frankly is elusive to a lot of companies that are doing what you guys are doing. Mike Berry (Chief Financial Officer) Well, I think it's the funnest part of my job quite frankly. So I would give kudos to not only the management team but everybody at MongoDB to really jump in this. I think that this has been a company wide effort and as we look forward and as we talked about Alex, the number one driver of margin expansion for Mongo is the revenue growth. So those two are directly connected. It's a great business model where when we can grow atlas in the 20% plus range and then keep that ARR of EA in that single digit, it generates a ton of gross profit that funds a lot. And the team has done a, really, has done a great job of making sure that we are investing in growth, that we go back and look at what we're doing, making sure that it's driving growth. If it's not, then we have an open discussion about whether we should reallocate. So I felt good about it when I started. Candidly, I feel better about it 90 days later. Wolfe Research Analyst Excellent. Thank you guys. Congrats again. Thanks. OPERATOR Thank you Alex. Thank you. Our next question comes from Kash Rangan with Goldman Sachs. Please proceed. Goldman Sachs Analyst It's always tough to go after Alex because he asks such good questions, but that's not going to stop me. So Dave and Mike, congratulations on the quarter. It's super interesting you were talking about how some of the Silicon Valley AI startup founders don't have time to think about databases, but our good friend Dheeraj Devrav seems to have made a wise choice here. So as you set encampment up in the Bay Area and start to evangelize the need for a Atlas consumption AI-savvy database, how do you reconcile that with the fact that same time enterprise is where we really saw that the bread and butter value proposition of Mongo resonate. So could what is happening with Devrev be a leading indication of what's going to happen in the enterprise? Because we've all, much to your observation, not seen much of a productive impact from the enterprise because of AI at the business level. And so what could be that unlock is what are folks like Dheeraj doing correctly? That could be a precursor if it is for what is to come in the enterprise. Dev Ittycheria (President and CEO) Yeah. So Kash, thanks for the question. Obviously I have so much respect for Dhiraj. He built Nutanix into a real great business and he's going to do the same at Devrev. I will tell you that the AI cohort as I said earlier, was not really material to our growth. So I think these are all customers kind of earlier in their journey. So what we are seeing, what's driving the growth right now is these large enterprises with workloads that we acquired both last year and this year that are really driving the growth, especially the ATLAS growth that we saw this quarter. And what that really confirms is that our move up market made sense. The quality of those workloads, the durability of their growth, they become, you know, growing, grow for longer and become bigger than what we've seen in the past is really making us feel good about that decision. And to juxtapose that, we also obviously decided to double down on self serve to better serve the small and medium sized business market. And that's also become obviously becoming more and more effective given the number of customers that we've added over the last six months. So we feel like those motions are working well in concert together and we feel like this allows us to be much more efficient about how we go to market. And there's also going to be continued more work to continue to drive that efficiency even better. But we also are investing for the long term and so we're just constantly debating those decisions internally. But we feel good about what's working and we feel good that someone like Adiraj is betting early on MongoDB because that's a good signal for other founders who are thinking about doing this the same. Awesome. We'll drill into this more in a couple of weeks when we see you in San Francisco. Absolutely. OPERATOR Thank you. A moment for our next question is Brad Reback with Stifel. Please proceed. Great. Analyst Thanks very much. The 7% EA ARR growth seems fine. I'm assuming you're not satisfied with single digit growth there, Dave. Any sense of where we should think about that longer term? Thanks. Dev Ittycheria (President and CEO) Clearly EA is a large enterprise motion and what we've seen is that it's typically less new customers choose EA and it's more of our existing customer base who have a mix of en and sometimes they they then also start deploying Atlas. I think one thing that's becoming more and more clear is that customers are becoming much more thoughtful about like how to think about using deployments on premise versus using the cloud. I think four or five years ago there was a belief that everything was going to move to the cloud. I think large enterprises become much more sophisticated and nuanced in their thinking and they believe that some workloads make sense to run on prem and some workloads make sense to run in the cloud. And I think that's where the MongoDB story becomes really attractive because the same code base can be used and so it also gives them optionality for the future where they can move from on Prem to the cloud. And a lot of our EA customers have done that either with new workloads and some existing workloads and then they can also move from cloud to cloud and they can also move back to on prem if they choose to do so. So that optionality becomes a very powerful value proposition for our customers. Analyst Great. Thank you very much. Dev Ittycheria (President and CEO) Thank you Brad. OPERATOR Thank you. Our next question is from the line of Ittai Kidron with Oppenheimer. Please proceed. Oppenheimer Analyst Thanks. Great numbers and congrats to Jess and good luck in the new role dev. I wanted to dig into the AI opportunity again but take it from the perspective of a go to market motion. Clearly it can power a lot of AI use cases that are embedded with bigger platforms through a self serve motion. But it sounds like to really capture the big workload opportunities. It's going to have to be more of an enterprise pool. So I'm wondering how do you think about targeting the AI opportunity from go to market Motion that doesn't just fall into if you're a big enterprise, I'm going to send you to an enterprise salesperson and all the rest call our self serve and do it. Your Is it something a little bit more, you think target perhaps that you need to take here in order to capitalize on this opportunity? Dev Ittycheria (President and CEO) Yes. What I would say Itai is that we've seen this movie before with the cloud where some early stage customers are growing very, very quickly and then we then put dedicated sales focus on those accounts and they grew then even faster. So we're clearly watching the market and when sales serve customers are to a point where they really need a higher touch kind of engagement model, then we're more than happy to do that and we have a team that kind of helps transition customers from self serve to more of a direct sales approach and that has worked for us. I think what we have learned is that line by which we actually engage a high touch model can move higher because we've become so sophisticated with self serve that we can really serve customers for early stage customers for a long period of time. In terms of the enterprise, what I would say is, what I've said earlier is that the enterprise is still quite early in their journey to AI. Most of the investments right now are more on end user productivity like developers using CodeGen tools and what I call low stakes use cases. In fact, I had two meetings today with two different leaders of two different financial institutions here in New York and they both talked about what they're doing in AI. They both admitted that they've kind of started with low stakes use cases, but their appetite to start doing more is increasing as they get more and more comfortable with the technology and they're quite excited to leverage MongoDB as part of that journey. But again, I think that's kind of a microcosm into the enterprise market where I think they're still quite early in their AI journey. And if you remember, this is something I've been saying for a while, that most customers, most people overestimate the impact of a new technology like AI in the short term, but underestimated in the long term. And I think we're just in that classic journey right now. Oppenheimer Analyst Appreciate that. And maybe as a follow up, Mike, I just want to make sure digging a little bit into the non athletes business, the EA predominantly EA business. Can you tell us roughly what percent of your customers here are in multi year deals versus just annual deals and just kind of curious how where we are now and what was it say a year or two ago and where do you think that mix is going to be a year or two from now? Mike Berry (Chief Financial Officer) Yeah, thanks for the question. We don't break out the percentage of customers on multi year versus one year. What I would say is in fiscal 25 obviously we saw a lot of larger multi year deals and you see that in the numbers this year. We will always see multi year deals. They haven't been I would call it as large. So it's more widespread. So that's really the change that we've seen. We haven't broken that out. I don't think that it has changed much especially over the years. As Dave talked about, it's going to be a mix of ATLAS and on prem and that mix has stayed relatively consistent.When you look at the customers that are choosing multiyear deals, has anything changed in the way they think about the reasoning behind doing that versus. No reasons are the same. It's typically if it aligns with their long term strategy they want to be able to lock in the pricing and as everybody knows, hey data has gravity. Moving data around is not fun for everybody. So they want to be able to lock in and guarantee their prices for that period of time. Oppenheimer Analyst Appreciate it. Mike Berry (Chief Financial Officer) You bet. Thank you. OPERATOR Our next question comes from the line of Siti Panigrahi with Mizuho. Please proceed. Mizuho Analyst Thanks for taking my question. I think some of the comments you were talking about AI slowdown and you heard about recent MIT report about 95% AI implementation not getting any kind of return. How do you see what kind of do you think the inflection point? When do we think we'll start seeing some of the adoption of this AI? Like you said they're testing but what can trigger. I know you have been talking about a year ago, probably we are a few years out but it's good to see some of the traction. So how do you first of all what will be your view on that report and how should we think about in terms of revenue contribution, material contribution from AI? Dev Ittycheria (President and CEO) Yeah. So I think it just comes down to the fundamental principles. I think customers need to feel one, that the quality of the output of these AI systems is high. Obviously AI systems are probabilistic in nature, not deterministic in nature. So you can't always guarantee the output. You can hope that you've trained the models well. You hope that you've given it the right information but you can't always guarantee the output. So as I mentioned, I had meetings with two financial services customers earlier today and both of them are still hesitant to roll out end user facing AI applications for those specific reasons. So it's going to take a little bit of time for people to really get comfortable that they can really deal with the last mile issues and make sure that they don't have any errors that potentially could be impacting the brand or really cause a lot of customer problems. So that's point number one. Then there's issues around obviously the security of these systems, the stability and reliability of these systems, the scalability of these systems. As I mentioned, some of these early stage companies are running into scaling issues with existing architecture, which is why they're coming to us. So I think we're just in that learning journey. I don't know if there's going to be some massive tipping point. I think what we are seeing with the frontier models is that all these frontier models are kind of clustering around the same ballpark in terms of performance and the efficacy of their models. So I think what's going to start happening is how people start leveraging these insights to build what I call scaffolding around these frontier models to address the needs of their business. Obviously everyone's talking about agents and people are very, very focused on essentially using agents to drive a lot of work agents require. If you think about if you're using agents, agents will use your systems much more intensely than humans will because they can do things much more quickly. So you need platforms that can massively scale up and down, which is again a good sign and support indicator for MongoDB. So I think it's going to take a little bit of time. It's going to take time being comfortable with technology. It's going to take time where people start with low stakes use cases and start gravitating to higher stake use cases. So I don't think there's going to be some seminal inflection point. I think it's just going to take time. But I think that time is coming. OPERATOR Our next question is from Brad Sills with Bank of America. Please proceed. Bank of America Analyst Oh, great. Thank you so much. I wanted to ask about some of the investments that you alluded to earlier that you're making in R and D. How are you thinking about that? Is it incremental investments in some of these newer offerings like Vector and streaming? Are there new workloads that you're thinking of addressing here. We'd love to get some color on just where you're investing in the stack. Thank you. Dev Ittycheria (President and CEO) Yeah, sure. So we talked about the fact that R and D is a big part of our investment focus for this year. One, we came out with 8.0, which was the most performant release ever. So we are already starting to see dividends of our investments in our platform. 8.1 is even better. And then we're also making investments in the expansion parts of our platform. What I will say is we're going to go into a lot more detail around this investor day, so if you can hold until September 17th, we'll go into a lot of things that we're doing on the R and D side as well as what we're doing on application modernization and the tooling that we're building there that will really speak to those investments that we're making and will give you a lot more color. Got it. Great. Thanks for that, Dave. And one more if I may, please. I know there's been an effort to focus on driving higher quality workloads in that larger account base. To what extent would you attribute some of this upside to that effort and maybe just an update on that effort as you. I would attribute a lot to that effort. I would say a big part of this growth is the fact that we're acquiring higher quality workloads that are growing faster and for longer than the workloads required, say in earlier years. And I think that's a big part for why you're seeing this growth happen now. Great, thank you, Carmen. OPERATOR I think we have time for one more question. All right, One moment please. And we have the line of Rishi Jaluria with RBC. Please proceed. Oh, wonderful. RBC Capital Markets Analyst Thanks for squeezing me in at the deadline. I'll keep myself to one question, Dave. Really nice to see the early traction with AI native companies. It's always made sense to us, especially given your scalability and your ability to work with unstructured data. If we were to fast forward five, 10 years and we start to see a real paradigm shift where instead of agents built on kind of the traditional GUI mobile interface that we've been in for the past 30 years, we actually enter kind of a multi agentic world where maybe the interaction vector may move away from what we've been used to into more natural language. Can you talk about why MongoDB still has a strong role and some of the investments that you might be making to position yourself well for that world? Understanding that's at the very least several years away. Thanks. Dev Ittycheria (President and CEO) Yeah, sure. So again, just to make sure we're all talking the same language, we believe that agents essentially do three things. One, they perceive or understand the state of things. So you need essentially a way to understand the state of what's happening in your business. Then you need to decide what to do or plan. So basically you have to come up with a plan saying, I want to take this action or these sets of actions, and then you have to act. You actually have to go execute those actions. Right. So why is MongoDB good for for agents? One is, as I said before, the JSON document database is the best at being able to model the real world. The messiness, the complicated nature. The real world does not fit easily in rows and columns. And that's why our document database, I think, is the best way to do that. Two, we obviously support search and vector search. So you can do very sophisticated hybrid search. So that becomes super important. And then with memory, if agents didn't have memory, they would act like goldfish. They could only react to the last thing, last piece of information that they saw. So memory lets agents connect the dots across time and situation. So you have different kinds of memory, things like short term context, past experiences, knowledge skills, et cetera, that you need to be able to share quickly. You need to be able to orchestrate those agents because you may have multiple agents doing a certain task. You need to register and have governance policies around those agents. We think that the underlying platform needs to be able to support those things. While there's a lot more work that needs to be done, the underlying architecture that we have in mongodb is well suited to address those needs. And we think that we'll be positioned to be a winner as people deploy more and more agents in their enterprise. OPERATOR Thank you. Thank you so much. And with that, we conclude the Q and A session and I will pass it back to Dev Itisharya for his final comments. Sure. Dev Ittycheria (President and CEO) Thank you again for joining us today. In summary, I think it's clear that we delivered another strong quarter, highlighted by the accelerating atlas growth, the continued adoption for AI applications, and our expanding profitability. We are raising our revenue and operating margin guidance for the full year fiscal year 2026. And these results really reinforce that MongoDB is well positioned to capture the next wave of AI applications application development while driving durable and efficient growth. So with that, thank you and we'll talk to you soon. Take care. OPERATOR Thank you. And this concludes our conference thank you for participating. And you may now disconnect. This transcript is to be used for informational purposes only. Though Benzinga believes the content to be substantially and directionally correct, Benzinga cannot and does not guarantee 100% accuracy of the content herein. Audio quality, accents, and technical issues could impact the exactness and we advise you to refer to source audio files before making any decisions based upon the above. * MongoDB Stock Soars On Strong Q2 Earnings Beat, Raised Guidance MDBMongoDB Inc $276.0026.4% Stock Score Locked: Edge Members Only Benzinga Rankings give you vital metrics on any stock - anytime. Unlock Rankings Edge Rankings Momentum 19.56 Growth 51.05 Quality N/A Value 50.91 Price Trend Short Medium Long Overview Market News and Data brought to you by Benzinga APIs

Box (BOX) Q2 2026 Earnings Call Transcript | The Motley Fool

Vice President, Investor Relations -- Cynthia Hiponia Need a quote from a Motley Fool analyst? Email [email protected] Revenue-- $294 million (non-GAAP) for fiscal Q2 2026, up 9% year over year and 7% in constant currency, surpassing guidance. (Fiscal period ended July 31, 2025.) Remaining Performance Obligations (RPO)-- $1.5 billion in remaining performance obligations (non-GAAP) for fiscal Q2 2026, with 16% year-over-year growth. Short-term RPO-- Increased 12% year over year, with the company expecting to recognize about 55% of total RPO in the next twelve months (as of fiscal Q2 2026, non-GAAP). Billings-- $265 million, up 3% year over year, and up 6% in constant currency, exceeding flat growth expectations despite a 320 basis point foreign exchange headwind (non-GAAP) in fiscal Q2 2026. Gross Margin-- 81.4% non-GAAP gross margin for fiscal Q2 2026, reflecting a 40 basis point year-over-year improvement in non-GAAP gross margin when excluding the prior year's data center equipment sales tailwind. Operating Margin-- 28.6% non-GAAP operating margin for fiscal Q2 2026, an improvement from the prior year, and above guidance. Net Retention Rate-- 103%, an increase from 102% in Q1 and the year-ago period, driven by seat growth and premium suite adoption in fiscal Q2 2026 (non-GAAP). Suite Revenue Mix-- Suite customers accounted for 63% of revenue (non-GAAP) for fiscal Q2 2026, up from 58% in the prior year. Number of Large Customers-- Nearly 2,000 customers each paying at least $100,000 annually as of fiscal Q2 2026, up 8% year over year. Free Cash Flow-- $36 million in free cash flow. Share Repurchases-- 1.2 million shares repurchased for approximately $40 million in fiscal Q2 2026; $112 million in buyback capacity remained as of July 31, 2025. Enterprise Advanced Deals-- The number of Enterprise Advanced deals closed nearly doubled sequentially from Q1 to fiscal Q2 2026, exceeding internal goals. Pricing Uplift-- Stated price improvements for Enterprise Advanced over Enterprise Plus met or exceeded the 20%-40% target in fiscal Q2 2026. AI Solutions Impact-- Adoption of Box AI and AI-powered workflow features cited as main drivers for upgrades, larger deal sizes, and new use cases across industries. Product Announcements-- General availability of enhanced Box AI extract agent and beta launch of the MCP server were introduced, alongside updates to the admin console, Box AI Studio, and AI units. Product Partnerships-- Box AI Studio added support forOpenAI(PRIVATE: OPENAI)'s GPT-5, Anthropix's Claude 4.1, and xAI's Grok 4; new integrations and partnerships launched withOpenAI(PRIVATE: OPENAI) (ChatGPT),Amazon Bedrock(NASDAQ: AMZN),Salesforce(NYSE: CRM),Snowflake(NYSE: SNOW),IBM(NYSE: IBM), andDeloitte(PRIVATE: DELOITTE). Guidance Raised-- Full-year revenue guidance (non-GAAP, fiscal 2026) increased by $5 million to a new range of $1.17 billion to $1.175 billion (about 8% year-over-year growth, or 7% in constant currency (non-GAAP)). Fiscal 2026 EPS Guidance-- Non-GAAP EPS raised to $1.26-$1.28 for fiscal 2026, including a $0.04 positive impact from foreign exchange on non-GAAP EPS; guidance for non-GAAP EPS reflects a $0.03 increase over prior expectations, or $0.06 when adjusting for currency movements. Leadership Changes-- Mark Whalen retiring as CRO; Jeff Newsom, formerly ofGoogle Cloud(NASDAQ: GOOGL), appointed CRO and head of global sales organization. Management highlighted a broad expansion of use cases forBox(BOX -0.46%)'s AI-powered automation and workflow capabilities, citing customer adoption across legal, industrial, and hospitality sectors. The shift toward larger, more valuable deals in segments previously unaddressed was directly attributed to Enterprise Advanced and Box AI, with increased price per seat and net seat growth called out as key contributors (non-GAAP) in fiscal Q2 2026. New product features, such as the MCP server, were introduced to enable cross-platform AI agent workflows and integrations with leading model providers, positioning Box for increased adoption in complex enterprise environments. Customer commitments to multiyear contracts were described as strengthening, coinciding with record revenue contribution from suite customers in fiscal Q2 2026. Box outlined ongoing investments in security, compliance, and governance enhancements, emphasizing their role as differentiators for customers entrusting sensitive enterprise data. The call underscored a market-facing strategy centered on deepening AI partnerships, accelerating product development, and expanding API-driven workflow automation into customer and partner ecosystems. Levie said, "Our pricing improvements for Enterprise Advanced over Enterprise Plus remain at or above our target of 20 to 40%." Smith noted, "Q2 billings of $265 million were up 3% year over year and up 6% in constant currency, exceeding the company's prior outlook for flat billings (non-GAAP)." Demand from new and upgraded customers was supported by a doubling of Enterprise Advanced deals sequentially in fiscal Q2 2026 and a steady 3% annualized churn rate. Box's CFO confirmed a $0.58 non-cash deferred tax expense would be a non-GAAP EPS headwind. Suite customers now contribute the majority of revenue, a structural shift up from 58% in the prior year to 63%. Remaining Performance Obligations (RPO): The sum of billed and unbilled revenue contractually committed by customers but not yet recognized, indicating future revenue visibility for SaaS companies. MCP Server: Box's Managed Content Platform server product enabling external AI agent systems to access unstructured data and content within the Box platform via APIs, supporting federated workflows across third-party AI environments. FedRAMP High: The Federal Risk and Authorization Management Program's highest security authorization, required for U.S. federal agencies managing highly sensitive data in the cloud. AI Agent: Autonomous software powered by generative AI models tasked with performing or automating complex enterprise business workflows involving unstructured data. Aaron Levie, Box Co-Founder and CEO, and Dylan Smith, Box Co-Founder and CFO. Following our prepared remarks, we will take your questions. Today's call is being webcast and will also be available for replay on our Investor Relations website. Our webcast will be audio only. However, supplemental slides are now available for download from our website. On this call, we will be making forward-looking statements including our third quarter and full year fiscal 2026 financial guidance, and our expectations regarding our financial performance for fiscal 2026 and future periods, including gross margins, operating margins, and operating leverage, future profitability, net retention rates, remaining performance obligations, revenue and billing, and the impact of foreign currency exchange rates and deferred tax expenses, and our expectations regarding the size of our market opportunity, our planned investments, future product offerings, growth strategies, our ability to achieve our revenue, operating margins and other operating model targets, the timing and market adoption of and benefits from our new products, pricing models and partnerships, our ability to address enterprise challenges and deliver cost savings for our customers, the impact of the macro environment on our business and operating results, and our capital allocation strategies, including potential repurchase of our common stock. These statements reflect our best judgment based on factors currently known to us, and actual events or results may differ materially. Please refer to our earnings press release filed today and the risk factors and documents we filed with the Securities and Exchange Commission, including our most recent quarterly report on Form 10-Q for information on risk and uncertainties that may cause actual results to differ materially from statements made on this earnings call. These forward-looking statements are being made as of today, August 26, 2025, and we disclaim any obligation to update or revise them should they change or cease to be up to date. In addition, during today's call, we will discuss non-GAAP financial measures. These non-GAAP financial measures should be considered in addition to and not as a substitute for, in isolation from, our GAAP results. You can find additional disclosures regarding these GAAP measures, including reconciliations with comparable GAAP results in our earnings press release and in the related supplemental slides, which can be found on the IR page of our website. Unless otherwise indicated, all references to financial measures are on a non-GAAP basis. Thank you. With that, let me turn the call over to Aaron. Aaron Levie: Thank you, Cynthia, and thanks, everyone, for joining us today. We delivered a strong second quarter with results above our guidance. Reflecting continued growth customer adoption of 9%, or 7% in constant currency, and RPO growth of 16%. Operating margins in the quarter were 29% with EPS of $0.33, $0.02 above the high end of our outlook. We had strong momentum in Q2 in customer adoption of Enterprise Advanced, which brings together our most powerful intelligent workflow capabilities in one plan. Examples include a prominent US law firm that became a new customer to Box, driven by enterprise advanced AI-powered metadata extraction capabilities and intelligent no-code apps to power its business processes. This is an enterprise-wide agreement replacing both an existing cloud-based platform vendor and an eSignature company. In partnership with a systems integrator, a Fortune 500 hospitality chain upgraded from a non-suite plan into Enterprise Advanced as they move away from a manual process with multiple systems to manage global projects. The company is looking to use AI-powered metadata extraction, Box Hubs, DocGen, and Relay, in design and planning workflows to scale projects and streamline execution. And a global industrial automation company upgraded from Enterprise Plus to Enterprise Advanced and expanded seats as they look to centralize their contract management solutions, automate quote generations, and enhance cross-entity document searchability. The company will use AI-powered metadata extraction to capture contract renewal dates and legal obligations to inform decision-making and ensure compliance. In addition to the accelerating momentum in Enterprise Advanced, Enterprise Plus continues to drive customer demand and remains a strong revenue growth driver for Box. In the second quarter, we saw customer upgrades and new logo wins, driven by our enhanced Box AI solutions, such as AI-driven multidot queries, Box AI content generation using advanced models, AI-powered content portals with intelligent hubs, and automated controls and protections against threats and data leaks. These Q2 wins demonstrate what I have heard from the hundreds of customer engagements that we had in the quarter. Enterprises know that AI agents are going to bring a new level of automation and deliver deeper business insights to their businesses. Software has historically been good for automating that deal with structured data. Think payroll, CRM systems, accounting, HRIS, or supply chain workflows. This is where data fits neatly into rows and columns in a database. But the vast majority of enterprise workflows revolve around unstructured data, which actually represents about 90% of our corporate information. These are the workflows that drive client onboarding at a bank, M&A deals that get closed, contracts getting agreed on, clinical research advances, moving getting made, and so much more. We've never been able to bring automation to these areas of work because they've been human-based, manual processes dealing primarily with unstructured data. Now for the first time ever, we can bring automation to this work with AI agents. With AI agents operating on unstructured data, enterprises can now accelerate product development processes, automate end-to-end hiring and training workflows, service insights, automate clinical studies, and speed up loan applications for better client engagement. We can imagine a future where there are over 100x more agents than people inside of an organization. Where any task you want done in a company is only a matter of how much compute you want to throw at the problem. You'll have agents running in the background and in parallel, for any workflow around content that you can imagine. However, most companies can't tap into the full power of AI agents on their unstructured data because their enterprise content is fragmented or stuck in legacy repositories. And with this fragmentation, means that AI agents have no core source of truth from which to answer questions about critical topics. It also means there's a risk that access controls are unmaintained, which can lead to AI agents leaking data to the wrong users asking a question. And finally, it becomes a massive nightmare integrating systems that don't play nice with one another in the AI era. With the Box intelligent content management platform, customers have a single source of truth to power the critical workflows for their most important content. And with an AI platform that delivers agents built right in and integrated all of our customers' agent ecosystem. Importantly, Box AI agents work directly on top of the workflows that customers have already built on Box. And we're only accelerating what these combined capabilities can deliver going forward. We're already seeing the power of AI agents with customers building Box AI agents that can review and summarize documents, answer questions from a large dataset, and extract critical details from enterprise documents like contracts or invoices. To orchestrate processes in legal, finance, healthcare, and more. Now to build on this continued momentum, in Q2, we announced all new updates to Box AI capabilities. Including the general availability of Box's new enhanced extract agent, and the beta launch of Box's MCP server. These releases along with key updates to the Box AI admin console, the Box AI Studio, and AI units empower users to operationalize AI with the confidence and control that businesses demand. Our flexible and interoperable platform has been a major differentiator for Box and is just as important, if not more critical, in the age of AI. We partner with the broader AI model ecosystem to ensure customers have the choice of any model provider they want to work with. Being neutral to the AI models means that our customers get access to the best AI capabilities applied directly to their content. We have announced support for OpenAI's GPT-5, Anthropix's Claude 4.1, and xAI's Grok 4 in the BoxAI Studio. Often on the day of the launch of this new model. In addition to supporting these new models on our platform, we've integrated with the broader product and partner ecosystems, OpenAI has integrated Box directly into ChatGPT for content access, Box partnered with Anthropics Financial Analysis Solution, We served as a launch partner for Snowflake's OpenFlow capability. Collaborated with AWS Bedrock agent core runtime, and partnered with Salesforce as a part of their MCP partner network. We had a strong quarter of execution on our product roadmap and technology partnerships. But what I am most excited about is our journey ahead. We have quite the roadmap in store for the second half of the year. First, we will be delivering all new workflow and no-code app capabilities to help customers automate their most critical workflows around content enhanced by the power of AI agents. We are making it easier than ever for companies to leverage Box to power their business processes, whether that is automating how they work with their contracts and digital assets, or leases clinical research. Next, we are continuing to enhance productivity by bringing the full power of Box AI to Box's core collaboration features. We will introduce all new AI features within Box Notes, continued improvements for leveraging Box Hubs, as an intelligent knowledge portal, and all new core Box AI experiences to make it easy for customers to interact with AI agents and find information across their Box accounts no matter what they're looking for. And all of these AI agent capabilities will be available via our API, so customers can take full advantage of summarizing, analyzing, and extracting data from their content any partner application. Like Salesforce Agent Force, ServiceNow Agent Fabric, Google's agent space, ChatGPT, Claude, Copilot, IBM's Watson x Orchestrate, and more. And with our newly GA'd remote MCP server, customers can interact with the full Box API and AI agents as tools within their own AI-oriented applications. Finally, all of this is only possible because customers entrust Box with their most sensitive and important enterprise data. Especially in a world where AI agents can accidentally leak corporate data, when security permissions are not maintained, Box's security functionality will become even more important for our customers. To continue to maintain and build that trust, we will advance our powerful security, governance, compliance, and data protection capabilities with all new features, core security improvements, archive updates, and more. We'll be sharing much more at this year's BoxWorks in San Francisco. And it's gearing up to deliver our biggest set of launches as a company. Now turning to go to market. We will be continuing to focus on driving the adoption of Enterprise Advanced, In Q2, we nearly doubled the amount of deals we closed over the prior quarter. Exceeding our internal goals. And our pipeline continues to build nicely. Our pricing improvements for Enterprise Advanced over Enterprise Plus remains at or above our target of 20 to 40%. As we've discussed, going to market with partners remains a critical of our go-to-market strategy. As we power more advanced solutions for customers. We continue to see notable partner-led wins with enterprise advanced as we go deeper into our customers' critical business processes. As we continue to grow our relationship with important partners worldwide, we are pleased to announce that Deloitte will be a title sponsor for BoxWorks 2025. Other notable sponsors include AWS, Google Cloud, IBM, Salesforce, Swalom, and more. Finally, I want to share that our current CRO, Mark Whalen, has announced his retirement. We are incredibly grateful for Mark's role in scaling Box to over $1 billion in annual revenue during his tenure. Helping us navigate the launch of suites, enterprise advanced, and much more. I'd like to share my deepest thanks to Mark for his incredible contributions over the past six years to Box, and for leading a smooth transition of the CRO role. With that, we are excited to welcome Jeff Newsom to Box as our new chief revenue officer. Heading our global sales org. Jeff is a highly regarded go-to-market executive with over two decades of experience leading sales organizations and enterprise software, cloud infrastructure, and AI. Jeff is joining us from Google Cloud where he spent over six years as a key leader driving the business's rapid growth and scale. Driving new logo growth, and significant customer expansions of their portfolio, of cloud infrastructure and AI services. Jeff has also held various senior leadership roles at Oracle SAP, and Workday. Jeff is a perfect fit for the next chapter of Box's growth to $2 billion in revenue and beyond. He is joining us at a foundational moment for Box. As our platform evolves to deliver intelligent content management into our customers' most important workflows and processes all powered by AI. Now before I turn it over to Dylan, I want to share how we're operating as an AI-first company. The objective of going AI-first is simple, move faster and deliver more value customers. We want to make decisions more effectively and quickly, drive more output, accelerate our roadmap, and better serve customers. To that end, we're equipping every boxer with the skills and tools to be productive with AI. Encouraging experimentation, scaling best practices across the company, and adding AI-first expectations in our hiring process. Across all of Box, we are using Box AI agents to augment our work in every area of the business. From how we train and enable new sales or support reps to how we write product requirements or generate rapid account research industry insights for each customer we sell to. AI agents are being used all across Box, to help accelerate our workflows and drive increased productivity. We are incredibly excited about the opportunity ahead of us. And we will be discussing many more of our advanced features and the future of Box and our intelligent content management platform at our upcoming customer conference, BoxWorks, 2025 on September. In San Francisco. With that, let me turn the call over to Dylan. Dylan Smith: Thanks, Aaron. Good afternoon, everyone. Q2 marked another quarter of strong execution as we exceeded guidance for all metrics, and delivered both double-digit short-term RPO growth and a sequential improvement in our net retention rate. We also made significant progress against our FY '26 priorities. We advanced our leading intelligent content management platform by enhancing our AI and agentic capabilities while investing in key go-to-market initiatives to drive enterprise advanced momentum. Finally, we're generating efficiencies across the business, and we continue to on our disciplined capital allocation strategy. As Aaron discussed, we have a significant opportunity to transform how enterprises work with their content and our Q2 results demonstrate the power of our balanced financial model. We delivered Q2 revenue of $294 million above the high end of our guidance. This accelerating growth was up 9% year over year and up 7% in constant currency. We now have nearly 2,000 customers paying us at least $100,000 annually up 8% year over year. Suite customers now account for 63% of our revenue, up from 58% a year ago. This improvement was driven by momentum in Box AI and Enterprise Advanced, which enable more of our customers to adopt Box for higher value use cases. We ended Q2 with remaining performance obligations or RPO of $1.5 billion, a 16% year-over-year increase both as reported and in constant currency. Short-term RPO grew 12% year over year as reported and in constant currency. These results reflect the impact of Box AI adoption on our business which is driving strong underlying business momentum and giving our customers the confidence to increasingly commit to multiyear contracts. We expect to recognize roughly 55% of our RPO over the next twelve months. Q2 billings of $265 million were up 3% year over year and up 6% in constant currency. This growth exceeded our expectations of flat year-over-year billings even as we absorbed an FX headwind of approximately 320 basis points versus our prior expectations. Q2 billing strength was driven by a combination of Q2 bookings, early renewals, and outperformance in our Box Consulting business. We ended Q2 with a net retention rate of 103%, an improvement from 102% in Q1 and in the year-ago period. Our annualized full churn rate remained steady at 3%. We've been pleased to see customers upgrade and expand into our Enterprise Plus and Enterprise Advanced plans to gain access to our enterprise-grade AI and advanced workflow capabilities. As a result, our net retention rate continues to benefit from consistent price per seat increases and we're now seeing net seat growth contribute more materially as well. We continue to expect a net retention rate of 103% exiting FY 2026. Q2 gross margin was 81.4%. Excluding the tailwind from data center equipment sales in Q2 of last year, this represents an increase of 40 basis points year over year. Q2 gross profit of $239 million was up 9% year over year, consistent with our revenue growth rate. We delivered Q2 operating income of $84 million and operating margin of 28.6% both above guidance and an improvement year over year, despite the tougher comparison due to data center equipment sales. In Q2, we delivered EPS of 33¢, $0.02 above the high end of our guidance. I'll now turn to our cash flow and balance sheet. In Q2, we generated free cash flow of $36 million and cash flow from operations of $46 million up 927% year over year, respectively. We ended Q2 with $760 million in cash, cash equivalents, restricted cash, and short-term investments. In Q2, we repurchased 1.2 million shares for approximately $40 million. As of July 31, 2025, we had approximately $112 million of remaining buyback capacity under our current share repurchase plan. We remain committed to opportunistically returning capital to our shareholders through our ongoing stock repurchase program. With that, let me now turn to our Q3 and FY 2026 guidance. As a reminder, approximately one-third of our revenue is generated outside of the U.S. With roughly 65% of our international revenue coming from Japan. Before providing guidance, I wanted to remind you of tax impacts we mentioned on our last call. We expect that the noncash deferred tax expenses will be a non-GAAP EPS headwind of $0.58 in FY '26. For the 2026, we expect Q3 revenue to be in the range of $298 million to $299 million representing approximately 8% year-over-year growth. This includes an expected tailwind from FX of approximately 80 basis points. We anticipate our Q3 billings growth to be approximately 10%, This includes an expected tailwind from FX of approximately 200 basis points. We expect Q3 gross margin to be 81%. Anticipate our Q3 non-GAAP operating margin to be approximately 28% versus 29.1% a year ago. Note that in Q3 of last year, operating margin included a 70 basis point benefit from data center equipment sales. As a reminder, this year, our annual customer conference, BoxWorks, has moved from Q4 to Q3. This shifts approximately $3 million in expenses into Q3 representing an additional 100 basis point headwinds to operating margin when comparing to the year-ago period. We expect our Q3 non-GAAP EPS to be in the range of $0.31 to $0.32 which includes an expected tailwind of approximately $0.01 from FX. Weighted average diluted shares are expected to be approximately 150 million. For the full fiscal year ending January 31, 2026. We are proud to have delivered strong first-half results driven by customer demand for our enterprise-grade AI capabilities, translating into the momentum we're seeing in Enterprise Plus and Enterprise Advanced. As a result, we are raising our revenue expectations for the full year by $5 million to $1.17 to $1.175 billion, an increase of approximately $8 million adjusting for currency movements since our prior guidance. This represents approximately 8% year-over-year growth or 7% in constant currency. We now expect a tailwind of approximately 90 basis points from FX, 30 basis points lower than our previous expectations. We still expect our FY '26 billings growth rate to be approximately 9%. This includes a tailwind of approximately 230 basis points from FX down from our previous expectations of a 340 basis point tailwind. We expect FY '26 gross margin to be approximately 81%. When adjusting for the impact from data center equipment sales last year, which also flows through to operating margin, this represents a year-over-year improvement of 40 basis points. We expect our FY '26 non-GAAP operating margin to be approximately 28% including a tailwind of approximately 10 basis points from FX. We now expect FY 2026 non-GAAP EPS to be in the range of $1.26 to $1.28 including an expected tailwind of approximately 4¢ from FX. This represents an increase of 3¢ for our prior expectations and an increase of 6¢ adjusting for currency movements since our previous guidance. Weighted average diluted shares are expected to be approximately 150 million. Our Q2 results demonstrate the strong business momentum we're seeing driven by customer demand for Box AI and Enterprise Advanced. This year, we will continue to invest in our intelligent content platform, key go-to-market initiatives, and our balanced financial model positions Box to capitalize on the AI-driven ahead in enterprise content. With that, Aaron and I will be happy to take your questions. Operator? Operator: At this time, I would like to remind everyone, in order to ask a question, please press star then the number one on your telephone keypad. Your first question comes from the line of Steve Enders with Citi. Your line is open. Steve Enders: Okay. Great. Thanks, Jerome. Thanks for taking the questions here. I guess maybe just to start on the momentum you're seeing in enterprise advance. I mean, how much of the billings upside should be kind of attributed to that or is this deal environment getting better? Can you just help us think through what actually drove the upper outperformance here? Dylan Smith: Yeah. So I would say it's hard to parse out how much is coming from Enterprise Advanced and Enterprise Plus, as those are really the core drivers given the demand for AI around our overall business momentum. And has an impact on really all of the factors that we called out as what's driving the outperformance. So for billings in particular, came down to a combination of strong bookings overall, strong outcome in our Box Consulting professional services business, as well as some impact from early renewals. And all three of those factors are really influenced by the types of deals that we're increasingly selling because of our AI capabilities and Enterprise Advanced. And so we'd really point to that momentum as the biggest change in what we're seeing around the business and really not a function of anything that we're seeing for an overall macroeconomic or deal environment standpoint. Steve Enders: Okay. That's helpful. And then I guess, maybe thinking through some of the pipeline dynamics and thinking through the enterprise advanced opportunities you're seeing, just how is it maybe expanding the kinds of use cases? Or if you look through the pipeline and what's coming through, how are maybe the sizes of the opportunities different from what you've seen historically here? Aaron Levie: Yeah. So I think the unique thing that we're seeing kind of across all of the Enterprise Advanced deals is really a core focus on being able to use some combination of AI agents and workflow automation together. And the first big use cases are really around things like data extraction. So you want to be able to take in a large amount of documents, invoices, contracts, lease agreements, and extract critical metadata from that and then be able to run some kind of workflow or have dashboards that let you go in and look at or analyze that data. So that's been a big use case. There's been another kind of increasing use case around using the AI Studio to create custom agents for employees to be able to interact with knowledge or be able to interact with their data with those agents. And then what those have in combination or as an effect of those two capabilities is really things like the deals are now getting bigger in segments maybe where we wouldn't have even seen as larger deals. So we had some great examples of deal sizes that were multiples of what they could have been kind of pre-Enterprise Advanced because the customer wouldn't have had the types of use cases be solved in a prior plan. So talk a lot about, obviously, the 20 to 40% price per seat uplift, but that doesn't fully even capture the fact that we might be doing deals that capture more users or that we wouldn't have even sold previously without Enterprise Advanced's functionality. So customers buying Box to be able to power again a contract management life cycle, digital asset management. Being able to process medical information and extract critical data from that. It's really going to get us into, I think, a much broader set of use cases. Where Box obviously traditionally has been for secure collaboration and document management. Now we can drive much more into intelligent workflows and automation as well. Steve Enders: Okay. Perfect. That's great to hear. Thanks for taking the questions. Operator: Your next question comes from the line of Lucky Schreiner with DA Davidson. Your line is open. Lucky Schreiner: Great. Thanks for taking my questions, and congrats on the quarter. Maybe to start, it was interesting to hear that NetSeek growth is starting to contribute more materially, especially in this environment. Dylan Smith: Is that really just because Enterprise Advanced is more relevant to more users across your customers, or help me understand what's driving that seat growth here this quarter? Dylan Smith: Yeah. That's exactly right. Just as Aaron hit on, it really is the use cases and types of users and departments now that have really high-value use cases on Box because of both Enterprise Advanced as well as Enterprise Plus, both of which have pretty robust AI capabilities. So that's really the biggest dynamic we've been seeing recently that is causing a bit of a rebound in that net seat growth metric. Lucky Schreiner: Awesome. And then the upgrade straight to Enterprise Advanced was also interesting to hear. Is that better than you expected? Like, how common are you seeing that? And are you able to provide any color on maybe the pricing uplift there when that happens? Dylan Smith: Yeah. So when you have a straight upgrade to Enterprise Advanced, you know, we tend to see relative to just using the core service non-suites, call that rough doubling, sometimes a little more, a little less based on relative to what they'd be paying versus that 20 to 40% uplift when going from Enterprise Plus to Enterprise Advanced. And we have been pretty pleased with the momentum there, especially given how early we are in the overall rollout of Enterprise Advanced. Aaron Levie: Having just made that generally available back in January. So we'd certainly expected and had seen the significant majority of those deals to be with existing successful Box customers who already had a lot of data and the sense of the types of workflows they put on the Box but certainly pleased with the momentum that we're seeing from customers who are going straight into Enterprise Advanced. Lucky Schreiner: Great. For taking my questions. Operator: Your next question comes from the line of Taylor McGinnis with UBS. Your line is open. Taylor McGinnis: Yeah. Hi. Thanks so much for taking my questions. Maybe just when we think about the outperformance in 2Q, can you comment on how much of that might have been related to some of these early renewals? Because if I'm doing some of the math right, it looks like the implied constant currency guide assumes a bit lower of billings growth on a constant currency basis in the second half. So just given the momentum that we've seen in the first half of this year and some of the strength you guys are seeing on the AI side. Maybe you could just walk us through then how we should think about that momentum as we head into the second half and what's implied in the guide especially in four keywords. Seems like there's a little bit more of a drop-off. Thanks. Dylan Smith: Sure. So, looking at those three factors that we had mentioned in terms of what's driving the outperformance, the good thing about them is that all having a roughly similar size impact. You know, a few million dollars each, in terms of the outperformance. And then as it relates to the back half, I would say, you know, as always, we want to be prudent with respect to the expectations we set as much as we're really pleased with the momentum that we're seeing in the business. And you see some of that flowing through to our increased expectations for the full year, there's still a lot of moving pieces out there and a pretty challenging environment. So I wanted to be prudent there, especially as we're always going to see some quarter-to-quarter variability with respect to overall billings. Taylor McGinnis: Perfect. Thanks. And then just a follow-up would just be on the point uptick in NRR and the comments that you made earlier about seeing it sounds like a little bit of recovery on the seat expansion side. So just curious, like, you know, you think through the momentum that you're seeing on that front and how that could build as we move throughout the year and impact NRR, like any color you can give there. Like, when you look at going from 102 to 103, was that largely driven by an uptick in fee expansions and how do we think about that as we move throughout the year? Thanks so much. Dylan Smith: Yeah. So the change, as mentioned, was driven by the seat growth and the recovery we're seeing there. We continue to see steady improvements in our pricing. Especially given the momentum that we're seeing with customers upgrading to our premium suites. And then over time, we do expect once we get through this year, for that net retention rate to continue to improve as we march down the path toward that double-digit overall growth. Operator: Thank you so much. Your next question comes from the line of Matt Balik with Bank of America. Your line is open. Matt Balik: Great. Excellent. Thanks for the question. It sounds like the metadata extraction capabilities are really resonating well. Some of those Enterprise Advanced early adopters. But curious if you could comment a little bit more understanding it's early. How are the use cases evolving as users of Enterprise Advanced get more comfortable? What do you see as the next natural step as customers get comfortable with the metadata extraction, etcetera? Aaron Levie: Yeah. So some of this, we're going to have some and share some major announcements at Box so I'll have to keep it a little bit high level. But if you think about all of the unstructured data that an enterprise has, you can kind of almost just think about every job function in a business as a way to quickly understand the scale that we're talking about. It's in the legal team, it's contracts. In finance, it could be invoices. And any collections data and financial documents in product management and engineering, it's product specifications, code. In sales and marketing, it's marketing assets and sales pitches. Well, all of that data has an immense amount of underlying value to the enterprise, but they can really only tap into it over and over again if they understand what's inside that information. And so many customers are coming to us and saying, okay. We'd like to be able to run AI agents on that data to extract the critical details from those contracts or those invoices or healthcare data that might be coming in. And then we want to be able to automate some of the workflow or business process that's tied to that data. So this could be a client onboarding process. Could be a lease agreement review process. It could be a loan origination process. The first step is to get and extract the metadata from those documents. And put that into a structured database or data store, which is something that Box has had for many years. And then be able to go and automate a workflow. So the first step of that workflow automation is usually things like building a Box app, being able to view all that data, and then have users that can go and consume and analyze the information through the Box app. But more and more, you're going to expect to actually run and automate the full workflow with agents running in the background moving documents through the various steps in that workflow, reviewing documents, probably making recommendations of what's the next step or what's the next best action for that document. And those are the next set of capabilities that we'll be sharing a bit more about later. But you can see how it's all coming together within this full ecosystem of AI agents plus workflow automation around all of your unstructured data. Matt Balik: Really helpful. And then one just quick follow-up if I could here. It seems like you're doing a lot of great work on the MCP server side. Maybe just help us understand the broader for that in the medium term. Aaron Levie: Yeah. So we kind of imagine a future where you might have dozens, if not on the upper end of a large enterprise, hundreds of different AI systems that people are going to be working from. We obviously want to be the absolute best place to have you work with agents and unstructured data, but there's going to be just a tremendous number of other AI systems. You might have some users in ChatGPT. You might have some users in Claude. You might have some users in Copilot. Some users might be in IBM Watson x Orchestrate. And so there's a very real chance of, again, dozens or hundreds of these systems inside of organizations. So then you, as an enterprise, have a decision. Do you replicate your data, your unstructured data across all of those systems, which is not only an incredibly costly problem, but it's also one that would lead to security risks and you have outdated information across those technologies. Or do you have a central repository that has your most important information and unstructured data that people can tap into from across all of those other environments? And so MCP server is basically this really compelling abstraction layer that makes it easy for the AI agents or AI systems on those external products to tap into the data that's within Box or the agents that are within Box. And so what we just launched it, GA'd in August. But the core idea is that you can be inside of Claude, and you could say, please summarize my meeting note from that one meeting or a contract that I was working on. And, again, instead of you having to upload your data to Claude, it will just tap into the BoxMC server, find the information you're looking for, and then right in line where you were doing your work, you can access your data. And so this really just reinforces the power of your unstructured data. And highlights how many different platforms you're going to want to access that information from. So we are just in the very early stages of what this looks like, but super excited about MCP and making sure that it's available to all developers. Matt Balik: Fantastic. Thank you. Operator: Again, if you would like to ask a question, press 1 and your telephone keypad. Your next question comes from the line of Josh Baer with Morgan Stanley. Your line is open. Chris Candero: Aaron. Hey, Dylan. This is Chris Candero on for Josh here. There was a controversial report that came out last week from MIT that said about 95% of Gen AI pilots at companies are failing due to flawed enterprise integration and misalignment in resource allocation. But it seems like you all are having some early success here with Box AI and clearly some good momentum with Enterprise Advanced adoption. So curious if you have a take on that and maybe what are some of the early lessons you all have learned at Box as you've driven this adoption of Box AI in advance so far? Aaron Levie: Yeah. So a couple interesting things. So I think one of the it was actually interesting in that same report. It actually called out the delta between when customers adopted a sort of a best of breed or prebuilt solution versus when they tried to build their own homegrown AI system from scratch. And that's sort of one thing that we've been trying to politely educate the market on for a year or two now, which is the idea that an enterprise with all of their data is going to get their data in a storage environment, do the vector embeddings on all of that data, put that into a vector data store, manage the permissions across every single user that needs access to that information, then have a user interface that is incredibly modern and up to speed with all of the latest breakthroughs in different UX paradigms. Then be able to stay on top of all of the different AI model breakthroughs across the four or five top model vendors. You're talking about a very small number of enterprises that have the technology teams to be able to do that. And be able to justify the underlying ROI of making that work. Whereas with something like the Box AI platform, we just handle every single one of those capabilities in our platform. We obviously handle all the storage. We handle all of the getting the documents ready for AI. Putting them into a vector data store, doing the vector embeddings, working with every major lab for the latest AI model breakthroughs. And then we make that all available to you as an API. Or even more importantly as a simple end-user interface that anybody can interact with. So you can just think about all of the different projects that are going on in enterprises. Where you have so many where people are trying to build up a lot of that infrastructure themselves. Or where you're deploying AI, potentially at not particularly high ROI use cases. Where then the adoption might not be there and people stop using it. We have been very focused on being hyper-targeted on things where we can either make end users just immediately more productive. So asking questions across your data in a Box Hub, being able to summarize information very easily, or increasingly importantly, being able to extract that metadata at scale where we have customers obviously that are now beginning to do that at massive scale. So we've been very targeted. And, again, our solution out of the box really kind of derisks most of the reasons why AI projects will fail in an enterprise. I think that's led to certainly better, healthier conversations and early adoption rates on the platform. But I do think that enterprises are going to spend quite a bit of time trying to figure out what is the right AI architecture, what are the AI solutions that are working, which ones actually are driving ROI? And our core focus is to make sure that we're continuing to partner with our customers on all of that. Chris Candero: That's super helpful. Thanks, Aaron. And I also want to ask on the federal side. You all got the high authorization somewhat recently, and you had a federal summit. So I'm curious kind of what you're seeing within the public sector, the opportunities, how the pipeline is looking like? Aaron Levie: Yeah. So I think things have our feeling is that things have, let's say, kind of settled down for maybe the first quarter or so of all of that broader transformation that we tended to hear about in the federal government. I think things are now aligned more toward a path of federal agencies being focused on IT modernization. You saw with the AI action plan from the federal government that there's a huge focus on bringing AI into the government. Box AI, as an approved service with FedRAMP high support working with all of the major model providers. To be able to bring those models to work with enterprise content in the federal government, I think, is going to be extremely key. We're happy about the momentum and the conversations that we're having. We partner with the GSA to support their mission even further and make sure that we can make BoxAI and the Box platform available. Across our Enterprise Plus and Enterprise Advanced plans really specifically tailored to the federal government. And so I think we're in a good spot from a momentum standpoint from here. And we'll keep folks updated as that continues. Chris Candero: Excellent. Thank you so much. Operator: Our next question comes from the line of Brian Peterson with Raymond James. Your line is still open. Brian Peterson: Thanks for taking the question and congrats on the strong performance this quarter. Aaron, I think you said that deals doubled sequentially. I'm curious, how does that normally compare second quarter to first quarter? And if we think about that step up, any perspective on how much of that was net new versus expansion, partner versus direct? Any perspective there? Aaron Levie: Yeah. And just to clarify, that was the number of Enterprise Advanced deals that doubled. Ah. So early innings, but just the fact that we're seeing a nice compounding rate of growth, we're super happy about. And, again, it's across new logos and upsells, but we're just driving as much focus on Enterprise Advanced as possible. Brian Peterson: Understood. I'll grab a coffee. Sorry about that. It's just all one, you know, as you think about adding to the platform, in AI, we're seeing all this adoption. I'm just curious if there's anything that's changed about your appetite for M&A. Thanks, guys. Aaron Levie: Yeah. You know, we continue to always be super thoughtful about the product roadmap and where are the opportunities for additional M&A. As you know and everybody else on the call knows, we're very focused on being product-led as we think about our overall corporate strategy, but then even, especially our M&A strategy. So we think about what's our product roadmap, where do we believe we're better off with kind of organic development doubling down on our core architecture versus where do we really have a time to market requirement that necessitates doing M&A. And at the moment, I think we've largely been focused on that core doubling down. We've got a great AI platform architecture that we're building off of. Even when we look at maybe startups in the space, we tend to find that our approach to the architecture is as modern as a startup that would be well-funded or getting started just today. So we have a very modern architecture for our AI agents. We're obviously partnered with all the major AI labs. We're building on a set of core workflow and automation scaffolding that will only get better and better. So we're pretty happy about the core platform that we're building on, and M&A would just be in areas, again, that we think we need to double down on or need extra support in. So no change in strategy or appetite, and we'll keep you posted as things become relevant there. Brian Peterson: Thanks, Aaron. Operator: This concludes our Q&A session. I will turn the call over to Cynthia Hiponia for closing remarks. Cynthia Hiponia: Great. Thank you, everyone. As a reminder, in conjunction with BoxWorks, our annual user conference on September 11, we are hosting an IR virtual investor product briefing from 1 to 2 PM Pacific time. This will feature Aaron and our senior product exec doing a deep dive on our product announcements from the day. And then we're hosting a live Q&A session directly after the presentation. Just go ahead and email Elaine or myself at [email protected] for further details, but we look forward to hearing from you there and talking again on our next call. Thank you. Operator: Ladies and gentlemen, that concludes today's call. Thank you all for joining. You may now disconnect.

MongoDB (MDB) Q2 2026 Earnings Call Transcript | The Motley Fool

President and Chief Executive Officer -- Dev Ittycheria Chief Financial Officer -- Mike Gordon Vice President of Investor Relations -- Jess Lubert Need a quote from a Motley Fool analyst? Email [email protected] Revenue-- $591 million for fiscal second quarter ended April 30, 2025, reflecting 24% year-over-year growth (non-GAAP) and surpassing the high end of guidance. Atlas revenue-- 29% year-over-year growth for Atlas revenue in the fiscal second quarter, now comprising 74% of total revenue, up from 72% last quarter. Non-GAAP operating income-- $87 million (non-GAAP) for the fiscal second quarter, resulting in a 15% non-GAAP operating margin, versus 11% in the year-ago period. Customer count-- Increased to over 59,900, with sequential growth of approximately 2,800 customers. Atlas customers-- Exceeded 58,300 Atlas customers, up from over 49,200 in the previous year. Non-Atlas ARR growth-- 7% year-over-year growth in non-Atlas annual recurring revenue for the fiscal second quarter, with half of non-Atlas revenue outperformance attributed to more multiyear deals. Customers with $100,000+ ARR-- 2,564 customers with at least $100,000 in annual recurring revenue for the fiscal second quarter, marking 17% growth from the prior year. Net ARR expansion rate-- Approximately 119% for the fiscal second quarter, consistent with recent quarters. Gross profit-- $436 million gross profit for the fiscal second quarter, translating to a 74% gross margin, down from 75% in the prior-year period due to Atlas mix shift. Share repurchases-- 930,000 shares repurchased for $200 million during the fiscal second quarter under the $1 billion authorization. Operating cash flow-- $72 million operating cash flow for the fiscal second quarter, with free cash flow of $70 million, versus negative results ($1 million operating, $4 million free cash flow) in the prior-year period. Cash balance-- $2.3 billion in cash, cash equivalents, short-term investments, and restricted cash at the end of the fiscal second quarter. Fiscal year revenue guidance update-- Increased by $70 million for fiscal year ending Jan. 31, 2026, now ranging from $2.34 billion to $2.36 billion. Fiscal year operating margin guidance update-- Raised non-GAAP operating margin guidance for fiscal year ending Jan. 31, 2026 to 14% at the high end, up from 12.5% previously. Non-GAAP income from operations guidance-- Non-GAAP income from operations guidance for fiscal year ending Jan. 31, 2026 updated to $321 million-$331 million. Third quarter revenue guidance-- Projected revenue between $587 million and $592 million for the fiscal third quarter ending July 31, 2025. Third quarter non-GAAP operating income guidance-- Set in the $66 million-$70 million range. Restructuring charges-- Less than 2% of employees affected, incurring approximately $5 million in one-time costs, excluded from non-GAAP results. Mid-single digit non-Atlas subscription revenue decline expected-- Managers now anticipate a mid-single digit decrease for fiscal year ending Jan. 31, 2026, compared to the previous expectation of a high single-digit decline. Multiyear license revenue headwind update-- Now estimated at $40 million for fiscal year ending Jan. 31, 2026, reduced from the earlier $50 million projection due to fiscal second quarter outperformance. MongoDB(MDB -1.76%) reported accelerated Atlas revenue growth and raised both annual revenue and margin guidance for the fiscal year ending Jan. 31, 2026. The company attributed operational and cash flow outperformance to large customer workloads, a continued shift toward Atlas, and increased multiyear deal activity. Management highlighted ongoing platform expansion, including integrated search and vector search, and noted that AI-native customer traction has not yet produced material revenue impact. CEO Dev Ittycheria stated that over 70% of the Fortune 500, as well as seven of the 10 largest banks, 14 of the 15 largest healthcare companies, and nine of the 10 largest manufacturers globally, are customers. Management is increasing R&D and developer engagement investments, with further details to be unveiled at Investor Day. Non-Atlas revenue remains partially supported by broad-based, smaller multiyear deals, with no pull-forwards or outsized transactions. The company cited a "modest restructuring" to enhance operating efficiency, affecting less than 2% of staff. AI-related workloads and customer wins, while growing, have not yet been material contributors to overall revenue growth according to management. CFO Mike Gordon clarified that fiscal third quarter non-Atlas revenue is expected to decline in the low-20% range year-over-year due to the prior year's multiyear deal concentration. Management stated that Atlas penetration and durability in large enterprise accounts are the key current growth drivers, rather than early-stage AI adoption, as discussed on the fiscal second quarter earnings call. The company's broad feature integration -- search, vector search, and embeddings -- was positioned as a differentiator for workload consolidation and future agent-based applications. Platform optionality for cloud, on-premise, and cross-cloud deployments continues to be emphasized as critical for enterprise customers making long-term database decisions. Atlas: MongoDB's cloud-based database-as-a-service offering, supporting multi-cloud deployments and representing a large and growing proportion of company revenue. EA (Enterprise Advanced): The commercial, enterprise-grade, self-managed version of MongoDB sold for on-premise or hybrid cloud deployment. ARR (Annual Recurring Revenue): Metric indicating predictable, normalized recurring revenue generated from active subscriptions within a year. Vector search: Feature allowing database queries over high-dimensional vector embeddings, important for AI and semantic search applications. PG Vector: The vector search extension for the PostgreSQL database, used for AI workloads and compared as a competitor to MongoDB Vector Search. Brian Denyeau: Good afternoon, and thank you for joining us today to review MongoDB, Inc.'s second quarter fiscal 2026 financial results, which we announced in our press release issued after the close of market today. Joining me on the call today are Dev Ittycheria, President and CEO of MongoDB, Inc., Mike Gordon, CFO of MongoDB, Inc., and Jess Lubert, MongoDB, Inc.'s new Vice President of Investor Relations. During this call, we will make forward-looking statements including statements related to our market and future growth opportunities, our opportunity to win new business, our expectations regarding Atlas consumption growth, the impact of non-Atlas business and multiyear license revenue, the long-term opportunity of AI, our financial guidance and underlying assumptions, and our investments and growth opportunities in AI. These statements are subject to a variety of risks and uncertainties including the results of operations and financial conditions, that could cause actual results to differ materially from our expectations. For discussion of material risks and uncertainties that could affect our actual results, please refer to risks described in our quarterly report on Form 10-Q for the quarter ended April 30, 2025, filed with the SEC on June 4, 2025. Any forward-looking statements made on this call reflect our views only as of today, we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in the earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measure. With that, I'd like to turn the call over to Dev. Dev Ittycheria: Thank you, Brian, and thank you to everyone for joining us today. Before discussing our strong quarter, I want to remind everyone about our upcoming Investor Day, which will take place on September 17, at the Javits Center in New York City during our .local conference. We'll spend the day discussing the investments we're making to drive durable growth and margin expansion and our view of the future. I look forward to seeing you then. Now on to Q2. I'm pleased to report another strong quarter as we continue to execute against our large market opportunity. Let me start with our results before giving you a broader company update. We generated revenue of $591 million, up 24% year over year and above the high end of our guidance. Atlas revenue grew 29% year over year, representing 74% of total revenue. We delivered non-GAAP operating income of $87 million for a 15% non-GAAP operating margin. And we ended the quarter with over 59,900 customers. Atlas performance was strong, accelerating to 29% year over year growth up from 26% in Q1. Our customer additions were also robust. We have added over 5,000 customers over the last two quarters. These results reflect the strength of MongoDB, Inc.'s platform. Our flexible document model, expanded capabilities like search and vector search, enterprise readiness, and the ability to run anywhere. Many of our recently added customers are building AI applications underscoring how our value proposition is resonating for AI, and why MongoDB, Inc. is emerging as a key component of the AI infrastructure stack. At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while expanding profitably. In short, our results show that customers are choosing MongoDB, Inc. Let me tell you why. First, MongoDB, Inc. is an enterprise-ready database capable of meeting the most stringent enterprise requirements. Over 70% of the Fortune 500 as well as seven of the 10 largest banks, 14 of the largest 15 healthcare companies, nine of the 10 largest manufacturers globally are MongoDB, Inc. customers. MongoDB, Inc. is a battle-tested enterprise platform relied on by some of the sophisticated and demanding organizations in the world. In part because of our strong enterprise posture across security, durability, availability, and performance. Atlas enabled one of the world's largest automakers to overcome Postgres' scalability and flexibility limits while reducing complexity. The company's management console tracks over 8.5 million vehicles requiring a modern schema to handle both structured and unstructured data. Something Postgres could not handle. Ultimately, Atlas consolidated infrastructure accelerated innovation, and support the scale of millions of connected vehicles. Second, MongoDB, Inc. is suitable for a broad range of use cases, including the most mission-critical and transaction-intensive applications. MongoDB, Inc. has also supported full asset transactions for more than six years, ensuring strong consistency and data integrity at scale. This is why some of the world's most demanding transactional workloads run on MongoDB, Inc. today. For example, Deutsche Telekom selected MongoDB Atlas as the foundation for its internal developer platform which includes mission-critical workloads like contract management, device purchases, and billing for 30 million customers. With 90 Atlas clusters managing over 60 million customer records, Deutsche Telekom's customer data platform now handles 15 times the concurrent logins of legacy systems. By consolidating these high-volume transaction-intensive applications on MongoDB, Inc., Deutsche Telekom has improved resiliency, accelerated innovation, and delivered a step change in customer engagement. Third, MongoDB, Inc. has redefined what's core for the database by natively including capabilities like search, vector search, embeddings, and stream processing. Comparing MongoDB, Inc. to another database like Postgres is not an apples-to-apples comparison. Take a global e-commerce application that manages inventory and order data while enabling product discovery through sophisticated search across millions of SKUs. The choice for this application, not between MongoDB, Inc. or Postgres, is between MongoDB, Inc. or Postgres plus other offerings like Pinecone, Elastic, and Cohere, for embeddings. MongoDB, Inc.'s complete solution allows developers to spend less time stitching together and maintaining a patchwork of disparate systems and more time building differentiated functionality that drives the business forward. For example, Agibank, a Brazilian neobank with 2.7 million active customers migrated their content management system storing customer records from Postgres to Atlas. As data volumes grew, Postgres' inflexibility and task execution latency drove performance issues and the database lacked sophisticated secondary indexes and full-text search. Earning sales of core offerings such as loans, insurance, and card approvals. Agibank was constantly updating the database and manually scaling infrastructure. Which is both time-consuming and error-prone. With Atlas, Agibank gained a resilient flexible system that handles rising demand and supports new services delivering nearly five times better performance and 90% lower cost, all with no outages. Fourth, MongoDB, Inc. is emerging as a standard for AI applications. Over the last few quarters, we've seen a strength in our self-serve channel, driven in part by AI-native startups choosing Atlas as the foundation for their applications. In the enterprise segment, adoption is real but early. Much of the activity today centers on employee productivity tools and packaged IoT solutions. Enterprises are still in the very early stages of building their own custom AI applications that will transform their business. We consistently hear from customers that when teams try to scale from five prototypes built on relational back ends, to enterprise-grade deployments, these platforms quickly hit limits in flexibility, scalability, and performance. Across startups and increasingly enterprises, our unified platform is resonating strongly. In the enterprise segment, a leading electric vehicle company chose Atlas and Vectrus Service to power its autonomous driving platform. After testing VectorSearch against Postgres PG Vector for their in-vehicle voice assistance, they selected MongoDB, Inc. for superior performance at scale and stronger ROI. They now rely on Atlas to handle over 1 billion vectors and expect 10 times growth in data usage by next year. DevRev, a well-funded AI native with proven founders disrupting the help desk market, built AgentOS. It's a complete agentic platform that autonomously handles billions of monthly requests on Atlas. DevRev accelerated development velocity, lowered cost, and scaled globally with low latency by using Atlas. AgentOS also leverages Atlas Vector Search for semantic search enriching its knowledge graph and LLMs with domain-specific content. Companies in nearly every industry and across every geography are choosing MongoDB, Inc. because we deliver the features, performance, cost-effectiveness, and AI readiness they need. All in one platform. As we look ahead, we remain confident MongoDB, Inc.'s position to lead both the current wave of digital transformation and the next wave powered by AI. With that, here's Mike. Mike Gordon: Thanks, Dev. I'll begin with a detailed review of our second quarter results. And then finish with our outlook for the third quarter and fiscal year 2026. I will be discussing our results on a non-GAAP basis unless otherwise noted. As Dev mentioned, we had a great quarter. As we exceeded all of our guidance ranges and are increasing our full-year guidance across the board. Now onto the results. In the second quarter, total revenue was $591 million, up 24% year over year and above the high end of our guidance. Shifting to our product mix, Atlas grew 29% in the quarter and now represents 74% of total revenue. This compares to 71% in the prior year and 72% last quarter. We had an impressive Atlas growth quarter which benefited in part from the strong start to consumption in May that we referenced on our last call as well as broad-based strength, especially in larger customers in the US. Let me provide some context on Atlas consumption in the quarter. In Q2, Atlas consumption growth was strong and relatively consistent with last year's growth rates. This drove the acceleration in revenue as well as the growth in absolute revenue dollars year to date for fiscal 2026. Turning to non-Atlas, revenue came in ahead of our expectations in the quarter as we continue to have success selling incremental workloads into our existing EA customer base. Non-Atlas ARR, which reflects the underlying revenue growth of this product line without the impact of changes in duration, grew 7% year over year. In addition to the good underlying trends in non-Atlas, in Q2, we also benefited from more multiyear deals than expected, reflecting our customers' desire to commit to building with MongoDB, Inc. long term. Approximately half of the non-Atlas revenue outperformance versus guidance was attributable to multiyear outperformance. We had another strong quarter for customer adds in the second quarter as we grew our customer base by approximately 2,800 sequentially. Bringing the total customer count to 59,900 which is up from over 50,700 in the year-ago period. This quarter, we incorporated new customers added from the 300 of the 2,800 added. The growth in our total customer count is being driven primarily by Atlas, which had over 58,300 customers at the end of the quarter compared to over 49,200 in the year-ago period. It is important to keep in mind the growth in our Atlas customer count reflects new customers to MongoDB, Inc. In addition, to existing EA customers deploying workloads on Atlas for the first time. Of our total customer count, over 7,300 are direct sales customers a decline of 200 customers sequentially and flat year over year. These metrics are largely due to our decision to reallocate a portion of our go-to-market resources from the mid-market to the enterprise channel starting in the second half of last year. This does not impact our total customer count but is an output of fewer self-serve originated customers being elevated to our direct sales team as we move upmarket. In Q2, our total company net ARR expansion rate was approximately 119%, which is consistent with recent quarters. We ended the quarter with 2,564 customers with at least $100,000 in ARR, representing 17% growth versus the year-ago period. Moving down the income statement, gross profit in the second quarter was $436 million representing a gross margin of 74% which is down from 75% in the year-ago period. Our year-over-year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Our income from operations was $87 million for a 15% operating margin, compared to 11% in the year-ago period. We are very pleased with our stronger than expected margin results, which benefited mainly from our revenue outperformance. Additionally, I'd like to provide a little context on the modest restructuring we undertook in the quarter. It impacted less than 2% of employees and resulted in approximately $5 million of one-time charges which we have excluded from our non-GAAP financials. This action is consistent with the key priorities I outlined for you last quarter to identify ways to both reallocate existing spend to higher ROI opportunities and be more disciplined about incremental spending. We are focused on running an efficient, scalable business that supports growth in revenue and profitability to drive long-term shareholder value. Net income in the second quarter was $87 million or $1 per share, based on 87 million diluted shares outstanding. This compares to a net income of $59 million or 70¢ per share on 84 million diluted shares outstanding in the year-ago period. Turning to the balance sheet and cash flow. We ended the second quarter with $2.3 billion in cash, cash equivalents, short-term investments, and restricted cash. During the quarter, we spent $200 million to repurchase 930,000 shares which was under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $72 million and free cash flow was $70 million which compares to negative $1 million and negative $4 million respectively in the year-ago period. Our strong cash flow results were driven primarily by strong operating profit and higher cash collections. Before turning to our outlook in greater detail, I'd like to share the key points driving how we are looking at the rest of fiscal year 2026. Number one, we are raising our expectations for revenue based on our confidence in Atlas. As well as a strong performance in the first half of the year providing a higher starting point for Atlas heading into the second half. Number two, we are increasing our operating margin guidance by 150 basis points at the high end, reflecting our strong Q2 performance and continued focus on margin improvement. And number three, we are raising our operating margin guidance while still continuing to make incremental investments for growth with a focus on R&D and developer awareness. Now moving on to our full-year guidance. I'd like to provide some incremental comments on our expectations. First, as we discussed, we had a strong start to the year and are confident in our ability to drive continued revenue and profitability growth. We are raising our full-year revenue guidance by $70 million including the $38 million outperformance in Q2. This reflects the strong Q2 consumption benefiting revenue in the second half and our continued confidence in Atlas growth. All in, this implies mid-twenties percentage growth for Atlas in the second half of the year. Second, incorporating our strong performance in the first half, we expect non-Atlas subscription revenue will now be down in the mid-single digits for the year. Compared to our prior expectation of high single-digit decline. We also expect the headwind from multiyear license revenue for fiscal 2026 to now be $40 million due to the Q2 outperformance compared to our prior expectation of approximately $50 million. Please note, we expect non-Atlas ARR will continue to grow year over year. Finally, we are raising our expectations for operating margin to 14% at the high end, up from 12.5% in our prior quarter guidance. This reflects the better than expected revenue performance, the impact of our more disciplined approach to investing for growth, and our increased focus on efficiency. For fiscal year 2026, we now expect revenue to be in the range of $2.34 to $2.36 billion, an increase of $70 million from our prior guide. We are raising our non-GAAP income from operations expectations by $44 million and are now targeting a range of $321 to $331 million and non-GAAP net income per share to be in the range of $3.64 to $3.73 based on 87.4 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the third quarter and fiscal year 2026 assumes a non-GAAP tax provision of 20%. Moving on to our Q3 guidance, a few things to keep in mind. First, we expect to see a low 20% year-over-year percentage decline in the non-Atlas business after the strong multiyear outperformance we experienced in 2025. As a reminder, Q3 of last year was our strongest multiyear revenue quarter and is the largest portion of the multiyear headwind. Second, we expect operating margin will be lower than in Q2, primarily due to the expected sequential decline in non-Atlas revenue, which is very high-margin revenue. In addition, it is also impacted by the timing of operating expenses specifically R&D hiring, and seasonality of our marketing investments. With that context, I will now turn to our outlook for the third quarter. For the third quarter, we expect revenue to be in the range of $587 to $592 million. We expect non-GAAP income from operations to be in the range of $66 to $70 million and non-GAAP net income per share to be in the range of 76 to 79¢ based on 87.7 million diluted shares outstanding. To summarize, we had a very strong quarter. We are pleased with our ability to drive revenue growth across the business and increase our operating profit expectations. We remain incredibly excited about the opportunity ahead and will continue to invest responsibly to drive long-term shareholder value. I would also like to take a moment to extend a warm welcome to Jess Lubert, our new Vice President of Investor Relations who started with us yesterday. Jess joins us from Juniper Networks where he led their investor relations effort including most recently helping the company navigate the acquisition by Hewlett Packard Enterprise. We're excited to have him on board and eager to see the impact of his work. Last but not least, look forward to seeing many of you in a few weeks at our Investor Day. Please reach out to our investor relations team at [email protected] with any questions. With that, we'd like to open it up for questions. Carmen, take it away. Operator: Thank you so much. And as a reminder, that is star one to get in the queue. And wait for your name to be announced. To withdraw the question, simply press star one again. Our first question is from Sanjit Singh with Morgan Stanley. Please proceed. Sanjit Singh: Hi. Thank you for taking the question and congrats on a heck of a quarter in Q2. I wanted to dive into some of the drivers into Q2. When I look at the acceleration in Atlas, which is now accelerated for two quarters in a row, kinda just look at the sequential dollar adds. I had that up, you know, more than $40 million in Q2, which is kind of the strongest sequential dollar adds we've seen in quite some time in what's been a pretty sober sort of cloud spending environment. So I was wondering if you could you know, give us some sense of the drivers of you know, of the strong sequential adds of this quarter. I know you pointed to May. But if anything you can give us from a, like, a workload perspective, or any other new factors, maybe the workloads from last year are starting to ramp. I'd just love to understand that trajectory a little bit better. Dev Ittycheria: Yeah. Sanjit, thank you. Thanks for the question. So clearly, we're really pleased by the quarter and really pleased by the accelerating growth in Atlas. I would say a lot of it was due to the workloads that we acquired over the past year, especially with a move upmarket. That are growing faster and becoming bigger than previous workloads we've seen. So I think the move upmarket is really paying off. And what we're also seeing is that there's a great uptick of some of the other capabilities they offer like search and vector search that are also adding to that growth of those workloads. And then as we mentioned, we also acquired a ton of new customers. Obviously, self-serve customers tend to spend less on a per customer basis, but we also have added lots of customers over the last six months. And I think that's also helping drive some of the growth. Sanjit Singh: Yeah. That's a that's a that's great color. I wanted to follow-up on the go-to-market side. You know, over the last couple of years, we've been sort of tinkering and optimizing the go-to-market organization across you know, sort of, you know, territory investment, but also sort of quotas and moving to incremental consumption. Could you give us an update on the state of operations for the Salesforce today and in some sense, you know, if I look at the customer adds, it seems like things are humming quite well. But just to get to understand, you know, how like, what's the state of the organization That'd be really helpful. Dev Ittycheria: Yeah. Sure. So nothing really has changed. We're just doubling down on what we said previously. We are moving up markets. We're focusing our high-end, you know, sales force, focus on the most sophisticated and demanding customers. You know, these are typically enterprise customers all around the world. And then, we're using our self-serve channel to better serve the SMB market. I know there are a lot of questions about where we kind of abandoning the self-serve the early stage market. By this move. And I think the results over the last couple of quarters have shown that we are not. I think we're just becoming much more effective in serving that market while also being very effective in growing you know, our wallet share in these larger accounts. So we're really just continuing with the strategy that we articulated before, and, obviously, we're pleased with the results. Operator: Thank you. Our next question is from Raimo Lenschow with Barclays. Please proceed. Raimo Lenschow: Perfect. Thank you. First of all, congrats to Jess. All the best. Two quick questions from me. Staying on that theme of self-service, that acceleration, Dev, obviously, you know, you changed things around, but it kind of it's accelerated despite kind of you actually moving upmarket. Like, can you help us understand then what's driving that a little bit? And then I have one follow-up for Mike. Dev Ittycheria: Yeah. I mean clearly, the output metrics look really good, but I would say the work around self-serve began, you know, has been going on for a while. The team is really good at running experiments using a data-driven approach to figure out what's working, to figure out what's not working, a new motion that we're also doing that's showing good results is going after SQL developers who don't really know MongoDB, Inc., attract them to our platform, really, you know, helping them understand the value props of MongoDB, Inc. Even running, like, things like office hours where we spend time with, you know, SQL developers to explain the benefits of modeling data in a document database. And all these experiments and tactics that we're doing, which are very data-driven, are really paying off. And, May Petrie used to run that group, is now our CMO. And she has a strong team under her, and we feel really good about what that self-serve team has been doing. But, again, we don't want to declare a victory too early, but, obviously, we're very pleased with the results. Raimo Lenschow: Yeah. No. That's really nice to see. And then, Mike, the things first of all, for all the access disclosure, the ARR for the non-Atlas or EA part is kind of really helpful. If you think about the I get the logic around the renewal cohorts, especially Q3. But in am I doing the graph correctly that actually next year that part of the business looks more interesting because the cohort looks better. Like, just trying to get your idea or maybe you might not even give it to us because you just do ARR. Thank you. Mike Gordon: Sure. So thanks for the question. So I'm gonna hold that answer till we get to Q3 of next year because it kinda depends on what happens in Q3 of this year. So the one thing is, as we've talked about, the big impact in Q3 of this year is the multiyear. We'll see how it how it comes back next year, but it really depends, Raimo, on how we do in Q3 this year. Raimo Lenschow: Yeah. Okay. Perfect. Thank you, Ben. Thanks for the disclosure. Really helpful. Mike Gordon: You're welcome. Thanks, Raimo. Operator: Thank you so much. And our next question comes from Tyler Radke with Citi. Please proceed. Tyler Radke: Hey, thanks for taking the question. And nice job on the Atlas growth. Wanted to dig into the AI commentary that you had, Dev. Obviously, last quarter, you talked about Cursor. Which obviously is ramping up significantly in terms of their ARR, and I think you called out many examples this quarter, including an autonomous vehicle company. It sounds like, you know, expecting pretty significant growth there. But how much of that is playing into the Atlas strength that you're seeing here in the quarter? Any way to quantify you know, that cohort or use cases, whether it's you know, vector search or maybe even if you throw in voyage, just help us understand if that's starting to move the needle because it sounds like there's some pretty high-profile wins in there. Dev Ittycheria: Yeah. So thanks for the question, Tyler. While we're adding thousands of AI-native customers, I will tell you that the growth that we delivered this quarter was not material to that growth. The growth was really driven by our core business and our core customer base. And so and, you know, while we're very happy with the, you know, the AI customers increasingly choosing MongoDB, Inc., it was not a material mover of the needle for our growth. Tyler Radke: Great. And then follow-up on the migration opportunity. I know you know, you've been investing in Relational Migrator. You know, you're working with companies like Cognition to accelerate the code migration opportunity. And you've seen professional services ramp up a little bit, but where have you started to see sort of the time to migration or replatform improve a bit just anything you could share in terms of that migration opportunity if that's started to improve in terms of velocity or size of workload migration would be helpful. Thank you. Dev Ittycheria: Yeah. Sure. So, yes, we're super excited about what we call app modernization or legacy app modernization. You'll hear a lot more about this at Investor Day in September, Tyler. But what I will say to you is that the value proposition is very clear. Customers are very, very motivated to try and modernize these legacy systems for a wide variety of reasons. We are seeing a lot of progress. We've actually brought in a new leader, new product leader who brings a lot of depth and scale, especially around AI to help us build the tooling to leverage AI to really, you know, drive more automation in terms of how we analyze and refactor the code. We brought in a new leader last quarter to help really help drive the delivery and the go-to-market efforts around AppMod. So we're definitely beefing up resources and I would say that we're investing a lot in product, and there's a lot more to do. And I would say this is something that we're very excited about, but it'll drive more of our longer-term growth less it'll the it won't be as pronounced in terms of this year but we're very, very excited about the opportunity, and we're definitely we'll spend more time discussing this and what we're actually doing on the product side in September. Thank you. Operator: Thank you. One moment for our next question. It comes from Jason Ader with William Blair. Please proceed. Jason Ader: Yeah. Thank you. Dev, I was hoping you could talk about some of the kind of latest industry developments just on the technology side. In particular, I'm thinking about Lake Base from Databricks and then DocumentDB and the Linux Foundation. Can you just comment on both those things and know, how they might impact MongoDB, Inc. and how you differentiate? Dev Ittycheria: Yeah. So let me tackle them one by one. Clearly, what we are seeing is that the strategic high ground for AI, when it comes to inference, is OLTP. So we talked about this on the last call where some companies that acquired early-stage OLTP startups. And what it really spoke to and those companies had spoken about their organic efforts to build an OLTP platform. And I think what it spoke to was the fact that they building an OLTP platform that's ready and mission-critical and enterprise can serve the most demanding requirements of enterprises is not trivial. And I think they basically threw in the towel and decided to do these acquisitions. And what it just reinforces that OLTP is the strategic high ground for AI, and we believe that if now customers are gonna be choosing what OLTP platform to that they want for AI, just given our architecture just given the fact that we have a durable architectural advantage in terms of JSON support, which addresses messy complicated and highly interdependent and costly changing data structures, The fact that we integrated search and vector search, I think, really helps us position going after AI. With regards to your second question around the Linux Foundation, I think what this really also suggests shows is that, you know, real JSON is much more important now with AI than ever before. And the clones and bolt-ons and, you know, that have traded off features and performance and developer experience have just not met customer expectations. And, candidly, what I see this is that the hyperscalers are investing less and really handing off to the open-source community to kind of really take on the bulk of the work in terms of product development. Our hyperscaler partnerships remain strong. And I think we have the right open-source model where we can balance the access to free software while preserving the ability to both generate and capture value. Jason Ader: Great. Thank you. And then just one quick follow-up. Why do we hear so much about Postgres adoption for AI startups? You talked about the success you guys are having. But if Postgres has the disadvantages that you've talked about, you know, multiple times, scalability, JSON support, How come we hear so much about that? You know, kind of at least in the early stages of AI? Dev Ittycheria: Yeah. That's a really good question. And I think it's important to understand. And we spent a lot of time we have now invested in the team in the Bay Area that spends a lot of time with the startup community. What's become clear is a lot of these startup founders don't think that hard about their database choice. They kinda go with what they know. And what we are seeing is that as some of these startups are scaling, they're running into real scaling challenges with Postgres. And what you know, and we've talked about this in the past. Like, when you add a JSON when you use JSONB on Postgres, a two-kilobyte document or bigger starts really creating performance problems because Postgres has to do something called off-road storage, which creates enormous performance overheads. And so the, you know, developers need a platform that can handle structured, semi-structured, unstructured data. They need a obviously, a platform that performs well. And they need a platform that can scale as they grow. And what we're hearing clearly from the startup communities Postgres, in many cases, is not scaling for them. And they're now coming to us. And so we feel really good about our position, but the reality is that a lot of, you know, these AI founders kinda struggle with what they know. What they've used in the past. And only when the business starts scaling do they start recognizing the challenges. And we realized we need to do more developer education and do more work, and so we're investing a lot in the startup community. We're running a big event in October in San Francisco with a big hackathon, and we're inviting a lot of customers to participate. But that's just the start of a meaningful investment we're making in the Bay Area and the AI startup community to rethink their decisions around just going with what they know. Operator: One moment for our next question. That comes from Mike Cikos with Needham. Please proceed. Mike Cikos: Hey, thanks for taking the questions, guys. Just wanted to come back to Atlas specifically. And, Mike, I appreciate last quarter, you gave us some very granular color around Atlas trends. Was hoping we could get an update on how Atlas trends played out this quarter. Or just at the very least why we did see such broad-based strength from large customers this quarter? Thank you. Mike Gordon: Sure. Thanks for the question, Mike. So when we talk about consumption in the second quarter for Atlas, as we talked about, it performed well, grew 29% year over year. As we talked about, Mike, the consumption growth was relatively consistent with last year. And as we talked about on the last call, we started out with a strong May, we saw broad-based strength across most of the geos and segments, so nothing to call out there. But we did see notable strength in the larger customers in the US. And if we dive deeper on that one, as Dev talked about, we are seeing some workloads from our larger customers grow for longer. And expand more than we have seen in the past, so that's good. While there's many moving parts in the consumption business, we also expect that there is benefit from our go-to-market changes. And given the preponderance of our strategic accounts being in the US, no surprise that we saw that growth mostly in the US. And then lastly, Mike, there is some benefit from comparing it to a little slower growth in Q1. So that would be the detail on Q2. As it relates to consumption growth. Mike Cikos: Thank you for that. And if I could just squeeze maybe one more in. On the outperformance that we saw this quarter from the multiyear deals. And maybe I'm just misunderstanding here. But my assumption was the reason we were facing this outperformance was really tied to the fact that in prior years, we've had some pretty big deals on the multiyear front. And so to see some of these deals come in this year, is that a function of customers renewing earlier, which is helping fill that larger divot that we previously expected? Is that a fair assumption? Or can you help me think through that a little bit more? Thank you. Mike Gordon: So thanks for the golf analogy. No. It did not fill the divot. So in Q2, it was really it was good underlying strength in ARR growth. And then greater than expected multiyear. There were really no pull-forwards, Mike. And this is a hard business to forecast because sometimes even customers don't know whether they're gonna opt for an annual renewal or a multiyear. So there were no pull-forwards. And there was nothing out of the ordinary. Very importantly, we left the net the non-Atlas assumptions consistent with our last guidance. Hence, pulling down the multiyear headwind from 50 to 40. And, again, nothing to call out on Q2. No pull-forwards, and there were really no large multiyears in there. Operator: Thank you. Our next question comes from the line of Alex Zukin with Wolfe Research. Please proceed. Alex Zukin: Yes. Thanks for squeezing me in and I'll echo the congrats, on truly, truly amazing quarter. I guess Dev, when you think about the AI comments that you've talked about both in the press release and in the call, maybe just a little bit more nuance in the use cases, not necessarily that you're seeing kinda contribute materially today, but the differentiation of the platform that you're able to incrementally take market share as it becomes available both in net new kind of AI-native companies, but also in some of your larger existing companies or customers that are starting to modernize for this kinda conversational or AI-native era where are you seeing the most momentum in terms of workload construction and scale? And when do you think we should expect to kinda actually start seeing that contribute more materially to the growth, in consumption? Dev Ittycheria: Yes. So thanks for the question, Alex. Couple of points. Again, we're very pleased with the results of this quarter, but I would say the AI cohort was not a material driver of the growth. That being said, what we are seeing is a lot of customers very, very interested in our architecture. Let me again walk through why. You know, one, we're a JSON database. JSON is the best way to express and model the complicated and messy and highly interdependent and constantly evolving data structures that you have to deal with in the real world. So that's point number one. So it's much easier to do that on MongoDB, Inc. than to do that on some Kluge you know, kind of setup on top of a relational database. Second is that we integrate search and vector search so you can do some very sophisticated things to people call hybrid search and retrieval can do very sophisticated things in finding information quickly. Which is a very unique differentiator for us. So what this means is that rather than stitching together multiple systems, you can do this all on MongoDB, Inc., so it becomes less complexity and lower cost. The third thing is that we've now embedded voyage models on our platform. Right? So the you know, if you control the embedding layer, you sit at the gateway of meeting. Of AI. Right? What the embedding models do is really are a bridge between a company's private data and the LLM. So that becomes really important because the better the quality of the embedding model, the better the quality of the signal of your own data. So that reduces things like hallucinations or just bad outputs. And so customers are now people start caring more and more about, like, you know, high higher stake use cases, they really wanna ensure those outputs are high. And the fact that it's part of our platform we can enable you to do auto embeddings, becomes an incredibly you know, compelling feature. In terms of the market, what I would say is that know, the enterprise uptake of AI is still early. I've said this for a couple of years now, and I think a lot of people were a little skeptical of what I said, but it's proving to be true. As you predicted, like, you know, the lack of skills and the lack of trust with AI systems, is kind of slowing, you know, people are being very cautious about deploying AI. Where it is being deployed is really on end-user productivity, whether it's developers with code gen tools, or business users using tools to summarize documents extract data, or things like deflecting tickets from people to systems with, like, conversational AI. I think you are starting to see the first steps in people deploying agent-based systems. And I can talk a little bit about that. But that's still very, very early. We're seeing small ISVs. Some of them are taking off who are really driving most of the impact. But the real enduring value will come. You know, when you talk to a customer today, most of them, when you ask them, is AI really transforming your business? They'll say no. Yes. We're seeing some productivity gains here and there, but it's not really transforming my business. I think the real enduring value will come when they build custom AI solutions that truly transform their business, whether it's to drive you know, new revenue opportunities or dramatically reduce their existing cost structure. But we're really pleased. We you know, I mentioned this electric car company that's very tech-savvy that's using MongoDB, Inc. I should mention one of the fastest-growing startups in the Bay Area has bet big on MongoDB, Inc. DevRev, the company going after the help desk space, has built their own agentic platform of MongoDB, Inc. So we feel really good about you know, what this all portends for the future. But as I said, it was a small part of our growth this quarter. Alex Zukin: Very helpful. And then maybe if I could just sneak one in for Mike. Yeah. You've been kinda saying from, I think, the first day you started about how the margin profile of this business, it's not an or, it's an and, and it's clearly coming through in both the growth acceleration, but also the meaningful margin outperformance. As you think about sustaining this kinda accelerating pace and investing in things like the you know, the Bay Area startup community, how are you finding that balance, that and versus or balance that quite frankly, is elusive to a lot of companies that are doing what you guys are doing. Mike Gordon: Well, I think it's the funnest part of my job, quite frankly. So I would give kudos to not only the management team, but everybody at MongoDB, Inc. to really jump in. I think that this has been a company-wide effort. And as we look forward and as we talked about, Alex, the number one driver of margin expansion for MongoDB, Inc. is the revenue growth. So those two are directly connected. It's a great business model where when we can grow Atlas in the 20% plus range, and then, keep that ARR or EA in that single digit. It generates a ton of gross profit that funds a lot. And the team has done a really has done a great job of making sure that we are investing in growth that we go back and look at what we're doing, making sure that it's driving growth. If it's not, then we have an open discussion about whether we should reallocate. So I felt good about it when I started. Candidly, I feel better about it. Ninety days later. Operator: Thank you. Our next question comes from Kash Rangan with Goldman Sachs. Please proceed. Kash Rangan: It's always tough to go after Alex because he has such good questions, but that's not gonna stop me. So, Dev and Mike, congratulations on the quarter. You know, it's super interesting. You were talking about how it's with Silicon Valley. AI startup founders don't have the have time to think about databases, but our good friend Dheeraj at DevRev, seems to have made a wise choice here. So as you set encampment up in the Bay Area, and start to evangelize the need for an Atlas consumption AI-savvy database. How do you reconcile type with the fact that same time enterprise is where we really saw the bread and butter value proposition of Mongo resonate. So could what is happening with DevRev be a leading indication of what's gonna happen in the enterprise? Because we've all much to your observation, not seeing much of a productivity impact from the enterprise because of AI at the business level. And so what could be that unlock is one of a what are folks like Dheeraj doing correctly that is a precursor, if it is, for what is to come in the enterprise. Dev Ittycheria: Yeah. So, Kash, thanks for the question. You know, obviously, I have so much respect for Dheeraj. He built Nutanix into a real great business. And he's gonna do the same at DevRev. I will tell you that the AI cohort, as I said earlier, was not really material to our growth. So I think you know, these are all customers kind of earlier in their journey. So I you know, what we are seeing, what's driving the growth right now is these you know, large enterprises with workloads that we acquired both last year and this year that are really driving the growth, especially the Atlas growth that we saw this quarter. And what that really confirms is that our move upmarket made sense. The quality of those workloads, the durability of their growth, they become you know, grow for grow for longer and become bigger. What we've seen in the past is really making us feel good about that decision and come and to juxtapose that, we also obviously decided to double down on self-serve to better serve the small and medium-sized business market, and that's also become you know, you know, obviously becoming more and more effective and gets us given the number of customers that we've added over the last six months. So we feel like those motions are working well in concert together. And we feel like this allows us to, you know, be much more efficient about how we go to market. And there's also gonna be continued more work to, you know, continue to drive that efficiency even better, but we also are investing for the long term. And so we're just constantly, you know, you know, debating those decisions internally, but we feel good about what's working. And we feel good that, like, someone like Dheeraj is know, is betting early on MongoDB, Inc. because that's a good signal for other founders who are thinking about doing the same. Kash Rangan: Awesome. We'll drill into this more in a couple of weeks when you we see you in San Francisco. Dev Ittycheria: Absolutely. Operator: Thank you. One moment for our next question. Is Brad Reback with Stifel? Please proceed. Brad Reback: Great. Thanks very much. The 7% EA ARR growth seems fine. I'm assuming you're not satisfied with single-digit growth there. Dave, any sense of where we should think about that longer term? Thanks. Dev Ittycheria: You know, clearly, EA is a large enterprise motion, and what we've seen is that it's typically, you know, less new customers choose EA and it's more of our existing customer base who have a mix of EA and then sometimes they then also start deploying Atlas. I think one thing that's becoming more and more clear is that customers are becoming much more thoughtful about, like, how to think about using, you know, deployments on-premise versus using the cloud. I think four or five years ago, there's a belief that everything was gonna move to the cloud. I think large enterprises have become much more sophisticated and nuanced in their thinking, and they believe that some workloads make sense to run on-prem and some workloads make sense to run in the cloud. And I think that's where the MongoDB, Inc. story becomes really attractive because the same code base can be used. And so it also gives them optionality for the future where they can move from on-prem to the cloud, and a lot of our EA customers have done that. Either with new workloads and some existing workloads and then they can also move from cloud to cloud. And they can also move back to on-prem if they choose to do so. So that optionality becomes a very powerful value proposition. For our customers. Operator: Thank you. Our next question is from the line of Ittai Kidron with Oppenheimer. Please proceed. Ittai Kidron: Thanks. I've had great numbers and congrats to Jess, and good luck in the new role. Dev, I wanted to dig into the AI opportunity again, but take it from a perspective of a go-to-market motion. Clearly, you can power a lot of AI use cases that are embedded with bigger platforms through a self-serve motion, but it sounds like to really capture the big workload opportunities, it's gonna have to be more of an enterprise push. So I'm kinda wondering how do you think about targeting the AI opportunity from go-to-market motion? Does that doesn't just fall into if you're a big enterprise, I'm gonna send you to an enterprise salesperson. And all the rest call our self-serve and do it yourself. Is it something a little bit more you think targeted perhaps that you need to take here in order to capitalize on this opportunity? Dev Ittycheria: Yeah. What I would say, Ittai, is that, you know, we've seen this movie before with the cloud where some early-stage customers started growing very, very quickly, and then we just we then put, you know, dedicated sales you know, focus on those accounts, and they grew then even faster. So we're clearly watching the market. And when self-serve customers are to a point where you know, they really need a higher touch kind of engagement model then we're more than happy to do that. And we have a team that kinda helps transition customers from self-serve to more of a direct sales approach. And that has worked for us. I think what we have learned is that line by which we actually engage a high-touch model can move higher because we've become so sophisticated with self-serve that we can really serve customers for early-stage customers for a long period of time. In terms of the enterprise, what I would say is what I've said earlier is that the enterprise is still quite early in their journey to AI. Most of the investments right now are more on end-user productivity, like, you know, developers using codegen tools, and, you know, what I call low stakes use cases. In fact, I had two meetings today with two different leaders of two different financial institutions here in New York and they both talked about what they're doing in AI. They've both admitted that they've kind of, you know, started with low stakes use cases. But their appetite to start doing more is increasing as they get more and more comfortable with the technology, and they're quite excited to leverage MongoDB, Inc. as part of that journey. But, again, I think that's kind of a microcosm into the enterprise market where I think there's still, you know, quite early in their AI journey. If you remember, this is something I've been saying for a while that you know, most customers you know, most people overestimate the impact of a new technology AI in the short term, but underestimate in the long term. And I think we're just in that classic journey right now. Ittai Kidron: Appreciate that. And maybe as a follow-up, Mike, I just wanna make sure to dig in a little bit into the non-Atlas business, the EA the predominantly EA business. Can you tell us roughly what of your customers here are on multiyear deals versus just annual deals? And just kinda curious how where we are now and what was it say, a year or two ago, and where do you think that mix is gonna be a year or two from now? Mike Gordon: Yeah. Thanks for the question. We don't break out the percentage of customers on multiyear versus one year. What I would say is in fiscal 2025, obviously, we saw a lot of larger multiyear deals, and you see that in the numbers. This year, we will always see multiyear deals. They haven't been at I would call it, as large. So it's more widespread. So we that's really the change that we've seen. We haven't broken that out. I don't think that it has changed much, especially over the year. As Dev talked about, it's gonna be a mix of Atlas and on-prem, and that mix has stayed relatively consistent. Ittai Kidron: When you look at the customers that are choosing multiyear deals, has anything changed in the way they think about the reasoning behind doing that versus not? Mike Gordon: No. Reasons are the same. It's typically they're if it aligns with their long-term strategy, they wanna be able to lock in that the pricing and as everybody knows, hey, data has gravity. Moving data around is not fun for everybody. So they wanna be able to lock in and guarantee their prices for that period of time. Operator: Our next question comes from the line of Siti Panigrahi with Mizuho. Please proceed. Siti Panigrahi: Thanks for taking my question. And, Dev, I think some of the comments you were talking about AI slowdown and you heard about recent MIT report about 95% AI implementation not getting any kind of you know, return. How do you see what's kind of do you think the inflection point? When do we think we'll start seeing some of the adoption of this AI? Like you said, they're testing, but what can trigger know you have been talking about a year ago, you know, probably we are a few years out. But it's good to see some of the traction. So how do you, first of all, characterize what will be your view on that report, and how should we think about the you know, in terms of revenue contribution material contribution from AI. Dev Ittycheria: Yeah. So I think it just comes down to, you know, the fundamental principles. I think customers need to feel, one, that the quality of the output of these AI systems is high. Obviously, AI systems are probabilistic in nature, not deterministic in nature, so you can't always guarantee the output. You can hope that you've trained the models well. You hope that you've given it the right information. But you can't always guarantee the output. So as I mentioned, I had meetings with two financial service customers earlier today, and both of them are still hesitant to roll out an end-user facing AI applications for those specific reasons. So it's gonna take a little bit of time for people to really get comfortable that they can really know, deal with the last mile issues and make sure that they don't have any errors that potentially could be know, impacting the brand or really call cause a lot of customer problems. So that's point number one. Then there's issues around, obviously, the security of these systems, the stability and reliability of these systems, the scalability of these systems that I mentioned some of these early-stage companies are running into scaling issues with existing which is why they're coming to us. So I think we're just in that learning journey. I mean, I don't know if there's gonna be some massive tipping point. I think what we are seeing with the frontier models is that every all these frontier models are kinda clustering around the same ballpark in terms of performance and the efficacy of their models. So I think what's gonna start happening is how people start leveraging these insights to build what I call a scaffolding around these frontier models to address the needs of their business. Obviously, everyone's talking about agents. And people are very, very focused on essentially, you know, using agents to drive a lot of work. Agents require you know, if you think about if you're using agents, agents will use your systems much more intensely than humans will because they can do things much more quickly. So you need platforms that can massively scale up and down which is again a good sign and support indicator for MongoDB, Inc. So I think it's gonna take a little bit of time. It's gonna take, you know, time being comfortable with technology. It's gonna take time where people start with low stakes use cases and start gravitating to higher stakes use cases. So I don't think there's gonna be some seminal inflection point. I think it's just gonna take time. But I think that time is coming. Operator: Our next question is from Brad Sills with Bank of America. Please proceed. Brad Sills: Great. Thank you so much. I wanted to ask about some of the investments that you alluded to earlier that you're making in R&D. How are you thinking about that? Is it incremental investments in some of these newer offerings, you know, like vector and streaming? Are there new workloads? You're thinking of addressing here? Would love to get some color on just where you're investing in the stack. Thank you. Dev Ittycheria: Yeah. Sure. So we talked about the fact that R&D is a big part of our investment focus for this year. One, you know, we came out with 8.0, which is the most performing release ever. So we're already starting to see dividends of our investments in our platform. 8.1 is even better. And then we're also making investments, you know, in the expansion parts of our platform. What I will say is we're gonna go into a lot more detail around this investor day. So if you can hold until September 17, we'll go into a lot of things that we're doing on the R&D side as well as what we're doing on, you know, application modernization and the tooling that we're building there. That will really speak to those investments that we're making a lot, and it will give you a lot more color. Brad Sills: Got it. Great. Thanks for that, Dev. And one more if I may, please. I know there's been an effort to focus on driving, you know, higher quality workloads in that larger account base. I mean, to what extent would you attribute some of this upside to that effort? And maybe just an update on that effort? As you've made. Dev Ittycheria: I would attribute a lot to that effort. I would say a big part of this growth is the fact that we're acquiring higher quality workloads, that are growing faster and for longer than the workloads required, say, in earlier years. And I think that's a big part of why you're seeing this growth happen now. Operator: Alright. One moment, please. And we have the line of Rishi Jaluria with RBC. Please proceed. Rishi Jaluria: Oh, wonderful. Thanks for squeezing me in at the deadline. I'll keep myself to one question. Dev, really nice to see the early traction with AI-native companies. You know, it's always made sense to us especially given your scalability and your ability to work with unstructured data. If we were to fast forward five, ten years, and we start to see a real paradigm shift where instead of agents built on kind of the traditional GUI mobile interface that we've been in for the past thirty years, we actually entered kind of a multi-agentic world where maybe the interaction vector may move away from what we've been used to into more natural language. Can you talk about why MongoDB, Inc. still has a strong role and some of the investments that you might be making to position yourself well for that world, understanding that's, know, at the very least several years away. Thanks. Dev Ittycheria: Yeah, sure. So again, just to make sure we're all talking in the same language, you know, we believe that agents, do three things. One, they perceive or understand the state of things. So you need a per essentially, a way to understand the state of what's happening in your business. Then you need to decide what to do or plan. So, basically, you have to come up with a plan saying, I wanna take this action or these sets of actions, and then you have to act. You actually have to go execute those actions. Right? So why is MongoDB, Inc. good for agents? One is, as I said before, the JSON document database is the best of being able to model the real world. The messiness, the complicated, nature. The real world does not, you know, fit in easily in rows and columns. And that's why the you know, our document database, I think, is the best way to do that. Two, we obviously support search and vector search. So you can do very sophisticated hybrid search. So that becomes super important. And then with memory, you know, if agents didn't have memory, they would act like goldfish. They could only react to the last thing last piece of information that they saw. So memory lets agents connect the dots across and situation. So you have different kinds of memory, things like short-term context, past experiences, knowledge, skills, etcetera, that you need to be able to share quickly. You need to be able to orchestrate those agents because you may have multiple agents doing a certain task. You need to register and have governance policies around those agents. You know, we think that the underlying platform needs to be able to support those things. While there's a lot more work, you know, needs to be done, the underlying architecture that we have in MongoDB, Inc. is well suited to address those needs. And we think that, you know, we'll be positioned to be a winner as people deploy more and more agents in their enterprise. Rishi Jaluria: Alright. Very helpful. Thank you so much. Dev Ittycheria: Thank you. Thank you so much. And with that, we conclude the Q&A session, and I will pass it back to Dev Ittycheria for his final comments. Dev Ittycheria: Sure. Thank you again for joining us today. In summary, I think it's clear that we delivered another strong quarter highlighted by the accelerating Atlas growth, the continued adoption of for AI applications, and our expanding profitability. We are raising our revenue and operating margin guidance for the full fiscal year 2026. And these results really reinforce that MongoDB, Inc. is well positioned to capture the next wave of AI application development. While driving durable and efficient growth. So with that, thank you, and we'll talk to you soon. Take care. Operator: Thank you. And this concludes our conference. Thank you for participating and you may now disconnect.