Amazon DocumentDB Serverless: Revolutionizing Database Management for AI Workloads

Amazon DocumentDB Serverless database looks to accelerate agentic AI, cut costs

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now The database industry has undergone a quiet revolution over the past decade. Traditional databases required administrators to provision fixed capacity, including both compute and storage resources. Even in the cloud, with database-as-a-service options, organizations were essentially paying for server capacity that sits idle most of the time but can handle peak loads. Serverless databases flip this model. They automatically scale compute resources up and down based on actual demand and charge only for what gets used. Amazon Web Services (AWS) pioneered this approach over a decade ago with its DynamoDB and has expanded it to relational databases with Aurora Serverless. Now, AWS is taking the next step in the serverless transformation of its database portfolio with the general availability of Amazon DocumentDB Serverless. This brings automatic scaling to MongoDB-compatible document databases. The timing reflects a fundamental shift in how applications consume database resources, particularly with the rise of AI agents. Serverless is ideal for unpredictable demand scenarios, which is precisely how agentic AI workloads behave. "We are seeing that more of the agentic AI workloads fall into the elastic and less-predictable end," Ganapathy (G2) Krishnamoorthy, VP of AWS Databases, told VentureBeat."So actually agents and serverless just really go hand in hand." Serverless vs Database-as-a-Service compared The economic case for serverless databases becomes compelling when examining how traditional provisioning works. Organizations typically provision database capacity for peak loads, then pay for that capacity 24/7 regardless of actual usage. This means paying for idle resources during off-peak hours, weekends and seasonal lulls. "If your workload demand is actually just more dynamic or less predictable, then serverless actually fits best because it gives you capacity and scale headroom, without actually having to pay for the peak at all times," Krishnamoorthy explained. AWS claims Amazon DocumentDB Serverless can reduce costs by up to 90% compared to traditional provisioned databases for variable workloads. The savings come from automatic scaling that matches capacity to actual demand in real-time. A potential risk with a serverless database, however, can be cost certainty. With a Database-as-a-Service option, organizations typically pay a fixed cost for a 'T-shirt-sized' small, medium or large database configuration. With serverless, there isn't the same specific cost structure in place. Krishnamoorthy noted that AWS has implemented the concept of cost guardrails for serverless databases through minimum and maximum thresholds, preventing runaway expenses. What DocumentDB is and why it matters DocumentDB serves as AWS's managed document database service with MongoDB API compatibility. Unlike relational databases that store data in rigid tables, document databases store information as JSON (JavaScript Object Notation) documents. This makes them ideal for applications that need flexible data structures. The service handles common use cases, including gaming applications that store player profile details, ecommerce platforms managing product catalogs with varying attributes and content management systems. The MongoDB compatibility creates a migration path for organizations currently running MongoDB. From a competitive perspective, MongoDB can run on any cloud, while Amazon DocumentDB is only on AWS. The risk of lock-in can potentially be a concern, but it is an issue that AWS is trying to address in different ways. One way is by enabling a federated query capability. Krishnamoorthy noted that it's possible to use an AWS database to query data that might be in another cloud provider. "It is a reality that most customers have their infrastructure spread across multiple clouds," Krishnamoorthy said. "We look at, essentially, just what problems are actually customers trying to solve." How DocumentDB serverless fits into the agentic AI landscape AI agents present a unique challenge for database administrators because their resource consumption patterns are difficult to predict. Unlike traditional web applications, which typically have relatively steady traffic patterns, agents can trigger cascading database interactions that administrators cannot predict. Traditional document databases require administrators to provision for peak capacity. This leaves resources idle during quiet periods. With AI agents, those peaks can be sudden and massive. The serverless approach eliminates this guesswork by automatically scaling compute resources based on actual demand rather than predicted capacity needs. Beyond just being a document database, Krishnamoorthy noted that Amazon DocumentDB Serverless will also support and work with MCP (Model Context Protocol), which is widely used to enable AI tools to work with data. As it turns out, MCP at its core foundation is a set of JSON APIs. As a JSON-based database this can make Amazon DocumentDB a more familiar experience for developers to work with, according to Krishnamoorthy. Why it matters for enterprises: Operational simplification beyond cost savings While cost reduction gets the headlines, the operational benefits of serverless may prove more significant for enterprise adoption. Serverless eliminates the need for capacity planning, one of the most time-consuming and error-prone aspects of database administration. "Serverless actually just scales just right to actually just fit your needs,"Krishnamoorthy said."The second thing is that it actually reduces the amount of operational burden you have, because you're not actually just capacity planning." This operational simplification becomes more valuable as organizations scale their AI initiatives. Instead of database administrators constantly adjusting capacity based on agent usage patterns, the system handles scaling automatically. This frees teams to focus on application development. For enterprises looking to lead the way in AI, this news means document databases in AWS can now scale seamlessly with unpredictable agent workloads while reducing both operational complexity and infrastructure costs. The serverless model provides a foundation for AI experiments that can scale automatically without upfront capacity planning. For enterprises looking to adopt AI later in the cycle, this means serverless architectures are becoming the baseline expectation for AI-ready database infrastructure. Waiting to adopt serverless document databases may put organizations at a competitive disadvantage when they eventually deploy AI agents and other dynamic workloads that benefit from automatic scaling.

Amazon DocumentDB goes serverless with automatic scaling to support agentic AI workloads - SiliconANGLE

Amazon DocumentDB goes serverless with automatic scaling to support agentic AI workloads Amazon Web Services Inc. is adding to its serverless database offerings with the launch of Amazon DocumentDB Serverless. Launched in general availability today, Amazon DocumentDB Serverless is a fully managed database service that's designed specifically for storing, querying and indexing document data structures. Because it's serverless, it can adjust its capacity automatically, based on the customer's demand, without any need for customers to make any alterations or manage the underlying infrastructure resources. According to Amazon, this makes it ideal for new "agentic" artificial intelligence workloads. Amazon DocumentDB Serverless stores data in JSON-like formats, which means it can support flexible data structures including single-field, compound and multi-key indexes -- essentially, any kind of enterprise document. It's built on the Aurora storage engine, which separates compute and storage to allow these resources to scale independently. The serverless aspect means that customers can reduce their expenditures by up to 90% compared to a traditional, managed database that needs to be provisioned and constantly maintained. It eliminates the need for complex resource planning, scaling automatically in step with application demand, all the way up to millions of requests per second, Amazon said. Amazon said DocumentDB Serverless can support a wide range of applications, such as storing player profiles in a global, online video game played by millions of people, or managing a catalog for an e-commerce store that indexes hundreds of thousands of different products. The beauty of the service is that it constantly monitors its own activity, scaling up when demand increases to provide just the right amount of capacity. That means customers aren't wasting money by provisioning too many resources, while also saving on infrastructure management costs. When demand drops, the database will reduce the resources it uses accordingly, so customers don't need to pay for the servers and storage capacity they're no longer using. One major advantage of Amazon DocumentDB is that it's compatible with the popular MongoDB document-oriented database, so developers can easily migrate workloads between the two. It also has similar features, including global clusters, vector search capabilities for AI applications and support for multi-availability zone deployments. The company was keen to stress that Amazon DocumentDB Serverless is an ideal platform for certain kinds of AI agents, which are advanced AI systems that can perform many kinds of tasks on behalf of humans, with minimal supervision. That's thanks to the unpredictable nature of AI agent workflows, which can suddenly become extremely busy after a long period of inactivity. Amazon gave the example of a travel company that offers an AI agent for planning trips. Each time customers use its app, they'll interact with this agent, and with each request they could potentially trigger multiple interactions. Now let's imagine this travel company is running a promotion. Its website might suddenly face an explosion of demand, with hundreds of thousands of individuals all triggering AI agents to search for hotels, car rentals and holiday activities. Because the travel agent doesn't really know how many customers are going to interact with its AI agents, it's almost impossible to forecast demand accurately and optimize resource allocation. With Amazon DocumentDB, they don't have to worry about that, since the database will automatically scale up and down based on how many people are currently using its AI agents to plan their trips. AWS Vice President of Databases Ganapathy Krishnamoorthy said the company's serverless offerings have made database management almost effortless for hundreds of customers already. "The rise of agents has put unprecedented demands on databases, magnifying the importance of data as a differentiator for customers," he explained. "Our serverless databases provide customers with a foundation capable of supporting these more dynamic and unpredictable workloads, so they can seamlessly scale to meet demands, reduce operational costs by always deploying the right amount of capacity, and simplify their operations." The video game development platform AccelByte Inc. has been using Amazon DocumentDB Serverless for some time already while the service was in beta. It uses the service for operations such as messaging, matchmaking and storage, and it often faces unpredictable surges in player numbers, as a result of video games hosting special events or launching new features. "Amazon DocumentDB Serverless is exactly what our team needs to adapt to dramatic shifts in player usage, ensuring we can deliver reliable, cost-effective scaling to meet the needs of our customers," said AccelByte Vice President of Engineering Tony Fu. "Now we can eliminate capacity planning for our database workloads and allow our engineers to focus on feature development." Amazon DocumentDB is just the latest in a growing list of serverless databases offered by Amazon. The cloud infrastructure giant launched its first serverless offering, Amazon DynamoDB, back in 2012, and has since expanded to Amazon Aurora DSQL, Amazon Neptune, Amazon ElastiCache, Amazon EMR, Amazon MSK and Amazon Aurora Postgres.