Databricks hits $188B valuation, extending its run as AI's favorite second act

Databricks on Thursday announced a new funding round that values the company at $188 billion, led by Coatue. The company did not disclose the exact amount raised, noting the money is not yet in hand and the round will close later this summer. Other outlets have since reported the raise is roughly $3 billion. A VC told TechCrunch the deal is solid, with so many firms wanting in that the company had no reason to keep its shiny new valuation a secret.

This is Databricks' fourth major raise in roughly 18 months. In February 2026, it closed a $5 billion Series L at a $134 billion valuation. In September 2025, it raised $1 billion at a $100 billion valuation. And in December 2024, it raised a record-breaking $10 billion at a $62 billion valuation. The pace has been so relentless that the alphabet-based series naming convention became the subject of memes about running out of letters.

Founded in 2013, Databricks initially grew to success in the big-data era with software enabling enterprises to store enormous amounts of data in the cloud while producing speedy analytics. Because it already sat on troves of enterprise data, the company was well-positioned as organizations began demanding AI with the same security and governance they expect from traditional enterprise software.

The company rolled out one AI product after another, including Lakebase, its database built for AI agents, Unity, its AI gateway, and Omnigent, a meta-harness that manages multiple agents. Databricks has also become one of the big examples of enterprises adopting more affordable Chinese-based open-weight models for cost control, a major AI trend of 2026. It is a particular champion of Z.ai's GLM 5.2 as a model for coding.

Last week Databricks CEO Ali Ghodsi shared results of internal benchmarking of its 3,000 software engineers. The company's blog post revealed that open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty in coding, at a total lower cost than proprietary models from Anthropic and OpenAI. The testing also found that the choice of agentic coding harness — the tool wrapping around a model — equally impacted costs, with open-source harness Pi performing among the best.

“The lesson here isn't that one harness is always cheaper or that native harnesses are worse,” the post declared. “Instead, model choice is only one piece of the puzzle.” The finding has practical implications for how enterprises evaluate AI coding tools beyond just model selection.

Databricks' AI transformation has granted it what observers call the AI halo for fundraising, propelling its valuation from $62 billion to $188 billion in just over a year and a half. Whether the company will eventually pursue an IPO, and whether its open-weight model strategy continues to gain traction against proprietary alternatives, are the next questions for the market to watch.