Databricks is having one of those years that most enterprise software companies would quietly envy. The data and AI platform says it has reached a $5.4bn annual revenue run rate, growing 65% year over year, at a time when growth across the sector has cooled noticeably.
For a private company, that pace is rare. And it helps explain why investors have continued to pour money into Databricks, even as funding has become more selective.
The company says it has now raised more than $7bn in total capital, including recent equity funding that values the business at $134bn, alongside a large debt facility to support long-term expansion.
What’s driving the numbers isn’t just classic data analytics. Databricks says around $1.4bn of its revenue run rate now comes from AI-related products, reflecting how quickly companies are trying to turn large datasets into something usable for machine learning and generative AI. Instead of building separate stacks for data and AI, many customers are opting for platforms that combine both.
In practice, that means Databricks is leaning into tools that make complex data systems easier to use. Products like Genie, which lets people query data using plain language, and Lakebase, a new operational database designed for AI-driven applications, are meant to lower the barrier between raw data and real-world use.
While Databricks is still private, its scale increasingly puts it in the same conversation as public cloud giants.
“We’re seeing overwhelming investor interest in our next chapter as we go after two new markets,” said Ali Ghodsi, co-founder and CEO of Databricks. “With this new capital, we’ll double down on Lakebase so developers can create operational databases built for AI agents. At the same time, we’re investing in Genie to let every employee chat with their data, driving accurate and actionable insights.
Databricks’ numbers also cut through a lot of the noise around enterprise AI. While many companies are still talking about transformation, this growth suggests businesses are already spending on the unglamorous parts, cleaning data, connecting systems, and making AI usable inside real organisations.
If that trend holds, the next phase of AI won’t be defined by new models or flashy demos, but by which platforms quietly become impossible to replace. Databricks is clearly betting it can be one of them.
