Databricks hits $188 billion valuation in new funding round

Databricks announced a new round of funding on Thursday that values the company at $188 billion, led by Coatue. Databricks didn’t disclose exactly how much it raised, saying the money isn’t in its hands yet and that the round will close later this summer — though other outlets have since reported the raise is roughly $3 billion. While it’s unusual for a company to announce before the money lands, a VC told TechCrunch the deal is solid, with so many firms wanting in that Databricks had no reason to keep the valuation secret.

The round extends a year-and-a-half fundraising run in which Databricks has recast itself as an AI provider rather than a legacy SaaS company from the pre-ChatGPT era. Only five months earlier, in February, Databricks closed a $5 billion Series L raise at a $134 billion valuation. Five months before that, in September 2025, it raised $1 billion at a $100 billion valuation. And roughly nine months before that, in December 2024, it raised what was, at the time, a record-breaking round of $10 billion at a $62 billion valuation.

Founded in 2013, Databricks initially grew on software that let enterprises store enormous amounts of data in the cloud while still producing fast analytics. Because it already held troves of enterprise data, the company was well-positioned as businesses began wanting AI with the same security and governance they expect from traditional enterprise software. It has since rolled out AI products including Lakebase, a database built for AI agents; Unity, an AI gateway; and Omnigent, a “meta-harness” that manages multiple agents.

Databricks has also become a prominent example of an enterprise adopting cheaper Chinese-based open-weight models for cost control, championing Z.ai’s GLM 5.2 for coding in particular. Last week, CEO Ali Ghodsi shared results of internal benchmarking Databricks ran to manage AI costs across its 3,000 software engineers, finding that open models — GLM 5.2 especially — can now handle even the highest level of task difficulty at a lower total cost than proprietary models from Anthropic and OpenAI. The benchmarking also found that the choice of harness, the agentic coding tool that wraps around a model, mattered as much as the model itself, with the open-source harness Pi among the cheapest without sacrificing quality: “The lesson here isn’t that one harness is always cheaper or that native harnesses are worse,” the blog post declared. “Instead, model choice is only one piece of the puzzle.”

Why it matters

Databricks’s products and its enterprise-AI credibility are helping it capture a growing share of enterprise AI spending, even as its own benchmarking suggests that cheaper open-weight models and smarter tooling — not just frontier proprietary models — are increasingly winning on cost and capability inside large engineering organizations.