The $1 Billion database wager: What Databricks Neon acquisition means for AI strategy

The value of databases in modern enterprise AI operations can’t be overstated.

Data is essential to the training and grounding of AIand research shows that AI efforts are likely to fail without it. It’s becoming increasingly important to have database technologies that can be spun-up as needed to support modern development efforts, especially with trends like agentic AI and vibe coding.

It should not be surprising that databases are highly valuable in this environment. This weekthis fact was displayed with Databricksacquires privately held PostgreSQL startup Neonwas founded in 2022 . The deal, according to reports, is valued at $1 billion. This is shocking, considering that the company only raised $46 million two years ago in a Series B round of financing.

It is also interesting to note the fact that Databricks is a data vendor with its own data lakehouse platform. In the past, Databricks has been positioned as an alternative to traditional database systems, offering a data lake structure that allows users to make queries. What was missing and why did Databricksneed to spend a billion dollar? What does this mean and what does it say about enterprise AI’s real needs?

In terms of functionality, it’s about meeting the needs of developers to build agentic applications. According to Neon’s statistics, over 80% the databases created by its platform were created using AI agents.

What is serverless PostgreSQL, and why is it important?

Although Neon is a new startup, the database technology on which it is based is not.

PostgreSQL, one of the oldest open-source databases, dates back to 1996. It’s a relational technology, which means it has tables and columns alongside features that are extremely stable. Organizations have trusted this technology for decades. The core open source PostgreSQL is now updated on a yearly basis. The most recent stable version was PostgreSQL 17 which debuted in September 2024.

PostgreSQL, as an open-source software, has been widely adopted and contributed to. It was once compared with other proprietary relational databases, including Oracle, as an alternative. PostgreSQL will be the only relational database in 2025.

DB Engines currently PostgreSQL is ranked as the fourth most popular database today, behind Microsoft SQL Server MySQL and Oracle. Timescale’s state of PostgreSQL report 2024 highlights the open-source database as the database of preference for AI applications. The well-established nature of the database and its wide availability are just two of many factors that make it appealing. PostgreSQL is just a database. It is a deployment and operational activity to run it as a serverless service. Any serverless database promises ease of operation. Serverless databases are spun up as needed, rather than requiring a database deployment that is constantly running with dedicated resources. It’s an option that developers find particularly attractive as it allows them to build applications quickly. AI-based development has even more appeal, as databases can be created and deployed programmatically.

There are many vendors in the serverless PostgreSQL market

All cloud hyperscalers offer some form of PostgreSQL services and have done so for years.

Google offers multiple services, including AlloyDB. Microsoft has Azure Database PostgreSQL. AWS offers Amazon RDS PostgreSQL. Each of them has a serverless offering as well, which is a database that can be accessed on demand.

There are many smaller vendors, such as Timescale, EDB, and NetApp Instaclustr. Databricks purchased a serverless PostgreSQL vendor nearly two years ago. Bit.io was also a rival of Neon in the early days. As it turns out the goals and capabilities are quite different between Neon and bit.io.

Phil Shin, senior vice president of corporate development and ventures for Databricks told VentureBeat that “together with the Neon Team, we look forward building the most developer- and AI-agent friendly database platform.” “In contrast, bit.io’s acquisition was not about Postgres, but focused on developer experiences, particularly in the trials and the self-service process.”

How serverless PostgreSQL fits in the enterprise database landscape

Neon’s serverless PostgreSQL has only been available for a few short years, but commercial vendor EDB is in business since 2004. EDB offers a number of commercially supported PostgreSQL products.

Matt Yonkovit is the VP of Product at EDB,said to VentureBeat that Postgres is a foundational technology in AI and analytics.

Yonkovit stated that the acquisition of Neon “confirms what we have long believed: Postgres has become a central part of modern data stacks.” “Serverless works well for dev/test environments, and for launching AI projects quickly. But as these use cases scale, enterprises will need the performance, compliance and control that a sovereign platform can provide. It can be scaled up or down quickly, or shut off completely when idle. This reduces the costs associated with computing, power, and storage. In his opinion, there are tradeoffs.

Yonkovit stated that a significant challenge of serverless data management is that it can be messy, as you cannot control where the data will be processed without a well-restricted resource pool.

The power of serverless PostgreSQL in agentic AI

Neon’s serverless PostgreSQL separates storage from compute, making it developer friendly and AI-native. It also allows for automated scaling and branching, similar to the way Git version control works. Amalgam Insights’ CEO and Chief analyst Hyoun Park stated that Databricks was a pioneer when it came to deploying and scaling AI.

Park told VentureBeat that managing the storage and computing associated with data is one of the biggest challenges in AI. “Each of these requirements will be harder to predict in a world where end-user requests and prompts can quickly lead to unexpected storage or compute demands to solve the issue.

Park explained Neon’s serverless approach to PostgreSQL was important for AI, because it allows AI projects and agents to grow as required without artificially coupling compute and storage needs together. He said that Databricks can use this for both agentic use cases as well as custom models built in the last two years following its Mosaic AI purchase.

What it means to enterprise AI leaders

This acquisition signals a change in infrastructure requirements for a successful AI implementation for enterprises looking to be at the forefront of AI.

Data are critical for AI. That’s no surprise. It is a revelation, however, that the ability of agentic AI to quickly spin up databases is crucial. The deal confirms that even advanced data companies require specialized serverless databases to support AI agents who create and manage database programmatically.

Organizations need to recognize that traditional database solutions may limit their AI initiatives. Serverless solutions that are instantly scalable and flexible allow for the dynamic resource allocation required by modern AI applications.

This acquisition is a signal to companies that are still planning their AI roadmap that they should prioritize serverless capabilities which can adapt quickly and easily to unpredictable AI workloads. This would transform database strategies from a technical consideration into a competitive edge in delivering efficient, responsive AI solutions.