Acceldata launches autonomous platform as AI breaks cloud centralization model

Acceldata CEO: AI Is Breaking The Cloud Centralization Model

It's the next-generation data and AI platform which is built for running enterprise data which is primarily analytical machine learning workloads, but also AI workloads, because I think a couple of things are going to happen. The number of agents that are going to come in production will be probably 100x more than the number of humans who are accessing these data platforms, and we're building the next generation of the current evolution of the data platform. You said there will be 100 times more agents than humans accessing those data platforms? Yeah, because every person who is accessing databases today is going to have at least 10 more assistants or agents that they will themselves create, or somebody will create for them, and those agents will then be working on our behalf. That's the new world of agentic AI. We'll do things by ourselves, but we'll also have a lot of assistants doing things for us. What does Acceldata do to prepare businesses for the agentic world that differentiates from your competitors? What is really happening is that the centralization model that the cloud tried to create in the last decade is changing. AI is actually not waiting for all the enterprise data to be centralized. Data has to be computed and processed wherever it is, and in most of the cases, what you end up finding is that the Fortune 500 and the Global 2000 generally have multi-technology, multi-cloud setups. What it essentially means is that the operational systems are producing data, and third-party data partners are depositing data in different islands, locations, and infrastructure sources. And not all that data can be moved into a central location. Therefore, the cloud centralization model is breaking. The second thing that's happening is that people will have to deal with an xPU architecture, or an architecture which includes CPUs and GPUs, and potentially ASICs in the future. The third thing that's happening with that flexibility of architecture is flexibility of models. Enterprises will choose different models to do different things on different infrastructures. The fourth thing that's happening is, just imagine a world in which data was going to get accessed 100x more than the current utilization. The cost of the cloud is going to break the bank and break the IT budget from a public shareholder perspective, meaning the EPS (earnings per share) will start going down. Therefore, what will end up happening is customers will have to figure out a method and a way to go and process all of this data for analytical and AI purposes very differently. What did Acceldata do to address this? What we've come up with is called the xLake architecture. The xLake is essentially a fundamental acknowledgement of several facts. Number one, data will be in different places. Number two, data will have to be processed with different models, different processing frameworks. Number three, enterprises will have to proactively reduce their operational expenses that they are paying on the cloud. And when you put all of that together, it essentially signals the end of the lakehouse era, which was kind of centralizing all of that data, processing it all together. That's not going to happen in the future, which is why this is the right time for a platform like ours to come into the market. AI agents will have to run in an environment which is operable, governable, explainable, cost efficient, and reliable. Unless you have those capabilities built into the platform for this multi-technology, multi-cloud world, it's impossible to run agents reliably, because somebody has to take accountability for the outcomes that these agents will produce in the future, especially as it pertains to critical things like data analytics, machine learning, all the things that allow people to make business decisions. Acceldata has a little bit of news. What's going on? We're just launching the xLake platform this week. We've already worked with a bunch of customers in the last couple of years, and now we now feel this is the best time for the xLake platform to come into the world as companies are thinking about deploying hundreds of agents in the next couple of years. We find it very interesting that consumer agents and consumer AI have taken off. Prosumer AI has taken off. But enterprise AI is slightly behind because, as we already talked about, data is in different silos. There's no uniform, interoperable mechanism for computing all that data. And the right guardrails, explainability, observability, and identity is not yet uniform. So we're pretty excited about this, because as anybody who's following the news knows, the build time for applications is completely disrupted. But somebody still has to run it in a way that the company can stand behind it and say, 'Look, I'm taking responsibility and accountability for the outcomes that both these agents and humans will take." The human accountability is already in the supply chain. Companies have contracts, and will stand behind them, but there isn't enough of a guardrail for how agents should run. So the run time is also an important factor in our recent news. Prior to the xLake platform, how did Acceldata do this? We've always been sort of hybrid in our approach and behavior, unlike many companies which are either completely on the cloud or completely on-premises. We always had this as the architecture. What we found out was that the applicability of the architecture is much wider in the last couple of years, especially post the ChatGPT era, because the more you think about the situation, you find out that data is growing exponentially, and that data growth cannot be centralized for all the use cases. Also, as far as AI use cases are concerned, there is massive concern around security and privacy, and companies are not very comfortable in pushing and moving all of that data into a proprietary cloud data store and then sending all of that information into a model that they don't have complete control over. We've always had the architecture to do this, and the market is now recognizing that this is the architecture that they needed. Needless to say, the cloud builds from both ISVs and hyperscalers are getting to a place where CFOs are taking a very keen interest in trying to bring the OpEx down.

Acceldata Launches Autonomous Data & AI Platform for the Agentic AI Era

Platform addresses growing enterprise demand for governed AI, hybrid and sovereign data infrastructure, and autonomous operations at scale Acceldata, the market leader in agentic data management, today announced its Autonomous Data & AI Platform, the industry's first platform that brings governed compute to wherever enterprise data lives. The platform enables enterprises to autonomously run data analytics and AI agents with trust across cloud, on-premises, hybrid, and sovereign environments, a critical challenge organisation increasingly face as enterprise AI adoption accelerates globally. The launch comes at a time when enterprises are struggling to scale AI on fragmented and distributed data architectures. Independent research conducted by GLG among Fortune 1000 and Global 2000 C-Level executives found that 80% of enterprises operate hybrid data architectures, while 75% manage four or more data platforms in production. Additionally, 40% identified governance fragmentation as their biggest cross-platform challenge, while 33% cited AI infrastructure readiness as a growing board-level concern. As organizations scale AI initiatives, traditional architectures built around centralized data movement are increasingly proving unsustainable. India is emerging as a key Global Capability Centre (GCC) hub, with enterprises increasingly driving AI, cloud, and data transformation initiatives from cities such as Bengaluru, Hyderabad, Pune, and Chennai. Today, India's GCC ecosystem plays a critical role in supporting global product engineering, enterprise data modernization, and AI innovation for Fortune 500 organizations. At the same time, as both GCCs and large Indian enterprises expand globally, organizations are increasingly facing the challenge of managing, governing, and securing distributed data environments across cloud, on-premises, hybrid, and sovereign infrastructures to support both business growth and regulatory requirements. For Acceldata, this shift carries particular significance as its engineering teams sit at the intersection of this transformation in Bengaluru, working closely with global customers and their GCCs to solve distributed enterprise data and AI challenges at scale -- making India not just a delivery hub, but a strategic center of gravity for some of the company's most critical technical innovations. At the same time, as large Indian enterprises continue expanding globally and GCC ecosystems rapidly grow, the need to manage, govern, and secure distributed datasets across hybrid and sovereign environments is becoming increasingly critical for both business continuity and regulatory compliance. The arrival of the Autonomous Data & AI Platform signals the end of the data lakehouse era. For years, enterprises prioritized migrating and centralizing data, but AI agents now need to operate across distributed datasets spanning multiple environments. Enterprise AI adoption is increasingly being constrained by expensive, incomplete, and multi-year data migration efforts. "The lakehouse architecture was built for human access. It broke in the agentic era,," said Rohit Choudhary, Co-founder and CEO, Acceldata. "We started Acceldata with the conviction that enterprise data would never consolidate, and that hybrid would be the durable reality. As AI adoption accelerates globally, especially across India's rapidly growing GCC ecosystem, enterprises are increasingly operating across cloud, on-premises, and sovereign environments with strict governance and compliance requirements. Data and AI platforms must evolve to support this shift. Our Fortune 500 and Global 2000 customers are increasingly looking for autonomous, hybrid-native architectures built for both analytics and agents." The next era belongs to autonomous, hybrid-native, cross-lake (xLake) platforms. Built on Acceldata's xLake architecture, the Autonomous Data & AI Platform introduces a new compute paradigm where analytics and AI agents securely operate on enterprise data to drive business outcomes. The platform is hybrid-native by design, operating autonomously to route workloads to the right infrastructure, augment data quality, optimize operational costs, and enforce governance at machine speed. As enterprises scale AI across increasingly distributed environments, many are struggling with fragmented governance, rising infrastructure costs, inconsistent data quality, and the operational complexity of managing data across multiple cloud and on-premises systems. Traditional architectures built around centralized data movement are proving difficult to scale for the speed and demands of the AI era. Therefore, the platform delivers: * xLake Compute for petabyte-scale enterprise data analytics and AI in hybrid-native environments with automated workload routing * Secure and governed runtimes with autonomous governance enforcement and data availability controls * Agentic runtime capabilities enabling AI-driven workflows across front, middle, and back-office enterprise systems * Intelligent infrastructure optimisation to improve efficiency and reduce operational cost * AI-ready data observability and quality management capabilities that continuously monitor pipelines, detect anomalies, and improve trust across distributed enterprise data environments "We're building the operating system for the AI-native enterprise, one runtime that spans every cloud, data center, and edge, so intelligence is no longer trapped by where the data happens to live. The world's largest organizations won't move to AI by lifting and shifting; they'll get there by making their hybrid reality, the warehouses, the lakes, the on-prem systems, the regulated workloads, work as one governed whole. That means analytics, pipelines, agents, metadata, quality, and AI observability have to live in one platform, not seven bolted together. Every analyst, application, and agent should reason over the same enterprise data- described, measured, monitored, and trusted in the same breath" said Ashwin Rajeeva, Co-founder and CTO, Acceldata. The Autonomous Data & AI Platform will be generally available worldwide starting May 19, 2026. Acceldata will showcase the Autonomous Data & AI Platform at Autonomous '26, the company's flagship Data and AI summit taking place in San Francisco, where global data, AI, and engineering leaders will discuss the future of autonomous enterprise systems and hybrid-native AI infrastructure.