Databricks jumps to $188B as AI push fuels fresh round

Databricks said Thursday that a new funding round values the company at $188 billion, extending a remarkable run of back-to-back raises as it recasts itself from a big data company into an AI heavyweight. The round was led by Coatue.

The company did not say how much it is raising and noted that the money has not yet closed and is expected later this summer. Other outlets have since reported the round is roughly $3 billion. A VC told TechCrunch the deal is firm and drew enough demand that Databricks had little reason to keep the valuation quiet, even before the cash officially arrives.

The pace has been unusually fast. Five months ago, in February, Databricks closed a $5 billion Series L at a $134 billion valuation. Five months before that, in September 2025, it raised $1 billion at a $100 billion valuation. And in December 2024, it pulled in what was then a record $10 billion round at a $62 billion valuation.

Founded in 2013, Databricks built its name in the big data era, helping enterprises store vast amounts of cloud data while still running fast analytics. That legacy turned into an advantage as customers began demanding AI systems with the same security and governance standards they expect from enterprise software.

The company has since rolled out a string of AI products, including Lakebase, a database built for AI agents; Unity, its AI gateway; and Omnigent, a “meta-harness” designed to manage multiple agents.

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Databricks has also become a prominent backer of lower-cost Chinese open-weight models, one of 2026's major enterprise AI trends. It has been especially vocal about Z.ai’s GLM 5.2 for coding tasks. Last week, CEO Ali Ghodsi shared internal benchmarking based on the real work of Databricks' 3,000 software engineers. According to the company, open models, and GLM 5.2 in particular, could handle even the most difficult coding tasks at a lower total cost than proprietary models from Anthropic and OpenAI.

The tests also found that the surrounding toolchain matters as much as the model itself. Databricks said the open-source harness Pi was among the best at managing prompt context while keeping costs low without sacrificing quality.

“The lesson here isn’t that one harness is always cheaper or that native harnesses are worse. Instead, model choice is only one piece of the puzzle.”

That mix of enterprise data roots and aggressive AI positioning has helped turn Databricks into one of the clearest examples of how much the AI label is driving valuations in 2026.