The $1 Billion database bet: What Databricks’ Neon acquisition means for your AI strategy

The importance of databases to modern enterprise AI operations cannot be overstated.

Data helps to train and ground AI, and multiple research reports show that without proper data, AI efforts tend to fail. With trends like vibe coding and agentic AI, it’s also increasingly important to have database technology that can be spun up as needed in a serverless approach to modern development efforts.

In that environment, it should come as no surprise that databases are a particularly valuable commodity. 

This week, that fact was on display with Databricks‘ acquisition of privately held serverless PostgreSQL startup Neon, which was founded in 2022. The deal is reportedly valued at a staggering $1 billion, which is shocking given that barely two years ago, the company raised $46 million in a series B round of funding.

What is also particularly interesting is that Databricks itself is a data vendor, with its data lakehouse platform. At various points in the company’s history, it has positioned itself as an alternative to traditional databases, providing a data lake structure where users can make queries. So what was missing, and why did Databricks need to spend a billion dollars? What does it mean and say about what enterprise AI really needs?

Functionally, it’s all about meeting developers’ needs to build agentic AI applications. According to Neon, over 80% of the databases created on its platform were created by AI agents.

What is serverless PostgreSQL and why does it matter?

While Neon is a startup, the core database technology that it’s based on is not new.

PostgreSQL is one of the oldest and most established open-source database platforms, dating back to 1996. It’s a relational database technology, meaning it has tables and rows alongside extremely stable features that organizations have trusted for decades. The core open-source PostgreSQL database is now updated in a yearly release cadence. The most recent stable update was PostgreSQL 17, which debuted in Sept. 2024.

As an open-source technology, PostgreSQL has enjoyed broad adoption and contributions. At one point, it was often compared to other proprietary relational database options, including Oracle as an alternative option. In 2025, though, PostgreSQL stands on its own.

DB-Engines currently ranks PostgreSQL as the fourth most popular database in use today, behind Microsoft SQL Server, MySQL and Oracle. The state of PostgreSQL 2024 report from Timescale identifies the open-source database’s rising prominence as the database of choice for AI applications. The database’s well-established and understood nature and broad availability are among the numerous factors that make it attractive.

PostgreSQL on its own is just the database, though. Running it as a serverless offering is an operational and deployment activity. The promise of any serverless database is ease of operations. Rather than requiring a dedicated database deployment that continually runs with dedicated resources, serverless is spun up on demand as needed. It’s a deployment option that is particularly attractive to developers as a way to build applications quickly. AI-based development is even more appealing as databases can be built and deployed programmatically.

The serverless PostgreSQL landscape has a lot of vendors

Every cloud hyperscaler has some form of PostgreSQL service and has for years. 

Google has multiple offerings, including AlloyDB, Microsoft has Azure Database for PostgreSQL, while AWS has Amazon RDS for PostgreSQL and Amazon Aurora. Each of them also has some flavor of serverless offering, that is, a database available on demand.

Numerous smaller vendors exist, including Timescale, EDB and NetApp Instaclustr. In fact, nearly two years ago, Databricks acquired serverless PostgreSQL vendor bit.io, which was also an early rival of Neon.

As it turns out, the goals and capabilities of bit.io are quite different from Neon.

“Together with the Neon team, we look forward to building the most developer and AI-agent-friendly database platform,” Phil Shin, senior director of corporate development and ventures at Databricks, told VentureBeat. “In contrast, the bit.io acquisition was not actually about Postgres but targeting developer experiences, especially in the trials and self-service process.”

Shin added that the bit.io acquisition had a big impact on Databricks’ seamless signup experience. 

How serverless PostgreSQL fits into the enterprise database landscape

While Neon has only been around for a few years with its serverless PostgreSQL implementation, commercial vendor EDB has been in business since 2004. EDB has a series of its own commercially supported PostgreSQL offerings.

Matt Yonkovit, VP of Product for EDB, told VentureBeat that the acquisition of Neon is a strong vote of confidence in Postgres as a foundational technology for AI and analytics. 

“It reinforces what we’ve long believed: Postgres is increasingly central to modern data stacks,”  Yonkovit said. “Serverless is a great fit for dev/test environments and for quickly jumpstarting AI projects—but as those use cases scale, enterprises need the performance, compliance, and control of a sovereign platform.”

Yonkovit noted that serverless is well-suited for short bursts and smaller workloads. It can scale up and down quickly or shut off entirely when idle, which significantly reduces costs associated with compute, power and storage. However, in his view, there are tradeoffs.

“A significant challenge with serverless is that sovereign data management can become messy because you can’t control where the data is processed unless you have a well-restricted pool of resources,” Yonkovit said.

The power of serverless PostgreSQL for agentic AI

Neon’s serverless PostgreSQL approach separates storage and compute, making it developer-friendly and AI-native. It also enables automated scaling as well as branching in an approach that is similar to how the Git version control system works for code.

Amalgam Insights CEO and Chief Analyst Hyoun Park noted that Databricks has been a pioneer in deploying and scaling AI projects. 

“One of the big challenges in AI is managing the storage and compute associated with the data,” Park told VentureBeat. “Each of these needs will be increasingly hard to predict in an agentic world where end-user prompts and requests may quickly lead to unexpected demands in storage or compute to solve the problem.

Park explained that Neon’s serverless autoscaling approach to PostgreSQL is important for AI because it allows agents and AI projects to grow as needed without artificially coupling storage and compute needs together. He added that for Databricks, this is useful both for agentic use cases and for supporting the custom models they have built over the last couple of years after its Mosaic AI acquisition. 

What it means for enterprise AI leaders

For enterprises looking to lead the way in AI, this acquisition signals a shift in infrastructure requirements for successful AI implementation.

Data is critical for AI; that’s not a surprise. What is particularly insightful, though, is that the ability to rapidly spin up databases is essential for agentic AI success. The deal validates that even advanced data companies need specialized serverless database capabilities to support AI agents that create and manage databases programmatically. 

Organizations should recognize that traditional database approaches may limit their AI initiatives, while flexible, instantly scalable serverless solutions enable the dynamic resource allocation that modern AI applications demand. 

For companies still planning their AI roadmap, this acquisition signals that database infrastructure decisions should prioritize serverless capabilities that can adapt quickly to unpredictable AI workloads. This would transform database strategy from a technical consideration to a competitive advantage in delivering responsive, efficient AI solutions.