Designed for hybrid environments, Acceldata xLake Platform marks the next step in enterprise AI

CAMPBELL, Calif. – May 19, 2026 – Acceldata, the market leader in agentic data management, today announced the general availability of its Autonomous Data & AI Platform, the industry’s first platform that brings governed compute to wherever enterprise data lives. 

This platform enables enterprises to autonomously run data analytics and AI agents with trust across their cloud, on-premises, hybrid, and sovereign environments – a critical challenge many organizations face as they deploy enterprise AI.

The arrival of the Autonomous Data & AI Platform signals the end of the data lakehouse era. For far too long, enterprises have prioritized migrating and centralizing data. The reality is that agents will have to operate on distributed datasets across the enterprise. Enterprise AI adoption is held hostage to an expensive and incomplete migration process. 

“The lakehouse architecture was built for human access. It broke in the agentic era,” said Acceldata founder and CEO Rohit Choudhary. “We started Acceldata with the conviction that enterprise data would never consolidate, that hybrid would be the durable reality. The data and AI platforms must evolve to support it. Our fortune 500 and global 2000 customers are aligned with our vision and direction.”

The next era belongs to autonomous, hybrid-native, cross-lake (xLake) platforms. 

Introducing: The xLake compute approach

Enterprise data management platforms such as those offered by Databricks and Snowflake consolidate data, compute and control in a single stack. However, this single-plane architecture was designed for an analytics world where data moved to the engine; it cannot support or keep pace with today’s agentic AI requirements. 

Enterprises need a modern platform for such dynamic compute environments fueled by AI. To address such complex, fluid environments, Acceldata created the Autonomous Data & AI Platform, an xLake compute paradigm where analytics and agents securely operate on enterprise data, regardless of where it resides, to deliver business outcomes. 

The platform is hybrid by default, operates autonomously, routes workloads to the right infrastructure, augments data quality, optimizes operational cost and enforces governance at machine speed. This architecture supports thousands of agents across hundreds of data sources. 

Enterprise agents will have access to richer context across the data supply chain to automate business processes and workflows with predictability.

Addressing customer pain points

Independent research conducted by GLG of C-level executives from Fortune 1000/Global 2000 companies in April 2026 provides more insight into the problem enterprises are having managing data in the AI era.  

Hybrid is the reality of AI, not the exception. Eighty percent of $5 billion-plus enterprises now run hybrid (warehouse + lakehouse) architectures, and 75% operate four or more data platforms in production, the GLG survey found. Yet the platforms most enterprises rely on were built on the assumption that data would consolidate. It hasn’t, and it won’t. Enterprise AI is stalling, with data migration projects taking multiple years. 

Governance is fracturing under that reality. Forty percent of executives cite governance fragmentation across platforms as their No. 1 cross-platform challenge ahead of data duplication, identity fragmentation and lineage gaps. 

AI initiatives are stalling at the data layer. Supporting AI initiatives is the No. 1 source of board-level pressure on data infrastructure (33%), but enterprises report AI/ML operationalization friction, AI/ML integration gaps and skills shortages as top pain points. Quite simply, the data foundation isn’t agent-ready.

The cost structure is unsustainable. Compute cost volatility, poor cost governance and DBU/credit unpredictability are top concerns across the board. 

These issues outline four symptoms of one underlying problem: Enterprises are running an AI mandate on a data architecture that wasn’t designed for hybrid reality, agent-scale governance, or the cost discipline AI economics now demands. 

Acceldata’s Autonomous Data & AI Platform fills that gap. The xLake platform delivers:

• Petabyte-scale compute for enterprise analytics and AI in a hybrid native environment with automated routing to cost efficient infrastructure locations
• A secure and governed runtime with autonomous identification of governance boundaries and data availability 
• Agentic runtime for business applications to solve problems across front, back and middle offices with secure access to all enterprise data

The platform will be generally available on May 19.

The Autonomous Data & AI Platform is Acceldata’s next step in a rapid evolution toward an autonomous business platform: An operational substrate where business logic, data and AI agents run together, autonomously, without bespoke implementation. 

Additional Resources

• Acceldata Launches xLake Benchmark for AI-Ready Data Quality at Scale 
• Pipeline Observability at Scale: Detail View & Copilot 
• Best Enterprise Data Lineage Tools for AI-Driven Success 

About Acceldata

Founded in Campbell, Calif. in 2018, Acceldata is the leader in data observability and agentic data management. The company’s platform unifies data quality, governance and observability into an intelligent, AI-driven fabric. Trusted by Fortune 500 companies worldwide, Acceldata empowers businesses to unlock the full potential of their data in the AI era. Customers include Dun & Bradstreet, PubMatic, PhonePe (Walmart), HCSC and more. Acceldata investors include Insight Partners, March Capital, Industry Ventures, Lightspeed, Sorenson Ventures, Sanabil, Prosperity7, and Emergent Ventures.