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Databricks hits $188B valuation, extending its run as AI’s favorite second act
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Databricks announced a new funding round on Thursday that values the company at $188 billion, led by investment firm Coatue. The company did not disclose the exact amount raised, though reports estimate the round at roughly $3 billion, with closing expected later this summer. While unusual for a company to announce before funds are secured, a venture capitalist told TechCrunch that the deal is solid due to overwhelming investor demand, making the valuation announcement no surprise.
Founded in 2013, Databricks initially built its reputation in the big data era, offering software that enabled enterprises to store massive amounts of cloud data while delivering fast analytics. Over the past year and a half, the company has successfully rebranded itself as an artificial intelligence provider, transitioning from a traditional SaaS company to an AI powerhouse. This transformation has fueled a series of massive fundraising rounds, each significantly increasing its valuation.
The company’s fundraising trajectory has been remarkable: in December 2024, it raised $10 billion at a $62 billion valuation; in September 2025, it raised $1 billion at $100 billion; and in February 2026, it raised $5 billion at $134 billion. The frequency of these rounds has sparked memes online, with one user posting, “Turning on alerts for when we get a Series AA.” Databricks has raised so many rounds that investors joke about running out of alphabet letters for series names.
Databricks’ pivot to AI leveraged its existing enterprise data repositories, positioning it to meet growing demand for AI solutions with the security and governance expected from enterprise software. The company launched a suite of AI products, including Lakebase, a database built for AI agents; Unity, an AI gateway; and Omnigent, a “meta-harness” that manages multiple agents. These offerings have solidified its reputation as an AI-focused company.
Databricks has also become a prominent advocate for cost-effective open-weight AI models, particularly those from Chinese developers. It has championed Z.ai’s GLM 5.2 for coding tasks, reflecting a broader enterprise trend toward affordable alternatives. Last week, CEO Ali Ghodsi shared internal benchmarking results comparing AI models on his 3,000 software engineers’ actual coding tasks. The results showed that open models like GLM 5.2 could handle high-difficulty coding at lower costs than proprietary models from Anthropic and OpenAI.
Surprisingly, the benchmarking also revealed that the choice of the agentic coding tool, or “harness,” significantly impacted costs. Databricks found that the open-source harness Pi managed context efficiently, offering low costs without sacrificing quality. The company emphasized that model selection is only part of the equation, highlighting the importance of the overall toolchain.
The AI halo effect has driven Databricks’ valuation surge, mirroring a broader trend where even non-tech companies heavily emphasize AI in their offerings. As Databricks continues to expand its AI capabilities, its valuation is likely to climb further, reinforcing its position as a key player in enterprise AI. The company’s strategy of embracing open models and optimizing costs positions it well for sustained growth in the evolving AI landscape.
