Databricks has unveiled Lakebase, a fully managed Postgres database tailored for artificial intelligence (AI) applications, now accessible in Public Preview. This innovative offering integrates an operational database layer into Databricks’ Data Intelligence Platform, aiming to streamline the development of data applications and AI agents within a unified multi-cloud environment.
Purpose-built for AI workloads
Operational databases, often referred to as Online Transaction Processing (OLTP) systems, are essential for application development across various sectors. The market for these databases exceeds USD 0 billion. However, many existing OLTP systems are built on outdated architectures, leading to management challenges, inflexibility, and high costs. The rise of AI-driven applications has introduced new technical demands, such as real-time data processing and scalable architectures that can handle AI workloads efficiently.
Lakebase utilizes Neon technology to deliver operational data within a lakehouse architecture, merging cost-effective data storage with computing resources that automatically adjust to workload requirements. This architecture facilitates the convergence of operational and analytical systems, minimizing latency for AI processes and providing enterprises with up-to-date data for real-time decision-making.
“We’ve spent the past few years helping enterprises build AI apps and agents that can reason on their proprietary data with the Databricks Data Intelligence Platform,” said Ali Ghodsi, Co-founder and CEO of Databricks. “Now, with Lakebase, we’re creating a new category in the database market: a modern Postgres database, deeply integrated with the lakehouse and today’s development stacks. As AI agents reshape how businesses operate, Fortune 500 companies are ready to replace outdated systems. With Lakebase, we’re giving them a database built for the demands of the AI era.”
Key features
Lakebase distinguishes itself by separating compute and storage, allowing for independent scaling tailored to various workloads. Its cloud-native architecture ensures low latency (under 10 milliseconds) and high concurrency (over 10,000 queries per second), all while being designed for high-availability transactional operations. Built on the widely adopted open-source Postgres database engine, Lakebase benefits from a robust ecosystem of support.
For AI workloads, Lakebase boasts rapid launch times of under a second and operates on a consumption-based payment model, meaning users only incur costs for the resources they utilize. The database also features branching capabilities that enable developers to create copy-on-write clones, facilitating safe testing and experimentation by both human users and AI agents.
Lakebase automatically synchronizes data with lakehouse tables and includes an online feature store for machine learning model serving. It seamlessly integrates with other Databricks services, such as Databricks Apps and Unity Catalog. Managed entirely by Databricks, the database incorporates features like encrypted data at rest, high availability, point-in-time recovery, and enterprise-grade compliance and security.
Market adoption and customer perspectives
During its Private Preview phase, Lakebase garnered participation from hundreds of enterprises. The potential applications of this technology span diverse sectors, from personalized product recommendations in retail to managing clinical trial workflows in healthcare.
Jelle Van Etten, Head of Global Data Platform at Heineken, remarked, “At Heineken, our goal is to become the best-connected brewer. To do that, we needed a way to unify all of our datasets to accelerate the path from data to value. Databricks has long been our foundation for analytics, creating insights such as product recommendations and supply chain enhancements. Our analytical data platform is now evolving to be an operational AI data platform and needs to deliver those insights to applications at low latency.”
Anjan Kundavaram, Chief Product Officer at Fivetran, added, “Lakebase removes the operational burden of managing transactional databases. Our customers can focus on building applications instead of worrying about provisioning, tuning, and scaling.”
David Menninger, Executive Director at ISG Software Research, noted, “Our research shows that the data and insights from analytical processes are the most critical data to enterprises’ success. In order to act on that information, they must be able to incorporate it into operational processes via their business applications. These two worlds are no longer separate. By offering a Postgres-compatible, lakehouse-integrated system designed specifically for AI-native and analytical workloads, Databricks is giving customers a unified, developer-friendly stack that reduces complexity and accelerates innovation. This combination will help enterprises maximize the value they derive across their entire data estate — from storage to AI-enabled application deployment.”
Integration and partner network
Lakebase launches with the backing of a diverse partner network, including technology vendors and system integrators such as Accenture, Deloitte, Cloudflare, Informatica, Qlik, and Redis. These partnerships aim to facilitate data integration, enhance business intelligence, and support governance as customers incorporate Lakebase into their operational frameworks.
Now available in Public Preview, Lakebase is set to receive further enhancements in the coming months, with customers able to access the preview directly through their Databricks workspace.
