Databricks Unveils Lakebase Database for AI Applications

Just weeks after announcing its acquisition of serverless Postgres startup Neon for approximately $1 billion, Databricks has wasted no time putting that technology to work. The company has launched Databricks Lakebase at their Data and AI Summit this week. The database represents what the company is calling an entirely new category of operational databases designed specifically for building intelligent applications.

It seems as though Databricks is making a play to move beyond its analytics roots and take on the big database players like Oracle, Snowflake, Amazon, and Microsoft on their own turf. Of course, Databricks insists that Lakebase isn’t just another me-too database product. They’re positioning it as something entirely new.

Addressing modern data challenges

At its core, Lakebase is a fully-managed Postgres database built specifically for data applications and AI workloads. It’s designed to address the hurdle a lot of organizations are starting to face: the need to combine operational and analytical data to build intelligent applications, whether that’s serving machine learning features and models, building standalone applications, or analyzing operational data within a lakehouse architecture.

The problem, as Databricks sees it, is that traditional databases haven’t kept pace with modern requirements. Organizations are struggling with complex provisioning, scaling challenges, and the lack of a modern developer experience when working with data. This is where the concept of “lakebases” comes in—databases designed specifically for the AI era.

The idea is to leverage Neon’s serverless Postgres technology, which essentially means developers don’t have to worry about managing servers or infrastructure. So, enterprises and developers get one platform where they can build data applications and AI agents that play nicely in the sandbox with AWS, Google Cloud, Azure, no matter their cloud setup. 

Under the hood, it runs on PostgreSQL, an open-source database that can handle just about any type of data thrown at it. 

If you’re an organization looking to build applications that actually use your data intelligently, Lakebase might just simplify that challenge considerably.

“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, the 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.”

Beyond Lakebase: A suite of new products

Lakebase was just one piece of a larger product announcement from Databricks during their summit. The company rolled out several other new offerings that highlight their push to expand across multiple areas of the data and AI landscape.

One of the releases is Lakeflow Designer, a no-code ETL tool that allows non-technical users to build production data pipelines through a visual drag-and-drop interface. It includes a natural language AI assistant, meaning users can describe what they want their data pipeline to do in plain English.

The company also introduced Databricks One, designed to give business users throughout an organization simple and secure access to the platform’s data and AI capabilities without requiring super-deep technical expertise.

Additionally, Databricks launched Agent Bricks, which automates the creation of customized AI agents for businesses. Rather than companies building these AI assistants from the ground up, the platform handles much of the complex development work.

In related news, Indicium recently unveiled AI Data Squads as a Service for streamlined Databricks migrations.