Databricks announced a $188 billion（約30兆円） valuation in a new funding round led by Coatue, with roughly $3 billion（約4800億円） raised.
The company has transformed itself from a big-data software vendor into an AI provider by building enterprise-grade tools and championing open-weight models like GLM 5.2 as a cost-effective alternative to proprietary models from OpenAI and Anthropic.
CEO Ali Ghodsi's internal benchmarking showed that open models combined with smart agentic harnesses can deliver high-quality coding assistance at lower cost than traditional proprietary solutions.
What happened
Databricks announced a new funding round valuing the company at $188 billion（約30兆円）, led by Coatue, with roughly $3 billion（約4800億円） raised (though the company says the money won't arrive until later this summer). This marks the latest in a rapid fundraising streak: the company raised $5 billion（約8000億円） at a $134 billion（約21兆円） valuation in February, $1 billion（約1600億円） at $100 billion（約16兆円） in September 2025, and $10 billion（約1.6兆円） at $62 billion（約9.9兆円） in December 2024.
Why it matters
Databricks has successfully repositioned itself from a big-data storage company into an AI provider by building products for enterprises that want AI with traditional software-grade security and governance. The company's embrace of affordable open-weight models—particularly Z.ai's GLM 5.2 for coding—resonates with enterprises seeking to control AI costs, a major trend in 2026. Internal benchmarking that CEO Ali Ghodsi shared showed open models can handle high-difficulty coding tasks at lower total cost than proprietary alternatives from Anthropic and OpenAI, positioning Databricks as a credible guide to cost-effective AI adoption.
What to watch
The round will close later this summer. Databricks has rolled out multiple AI products including Lakebase (a database for AI agents), Unity (an AI gateway), and Omnigent (a multi-agent management tool), signaling continued product expansion in the enterprise AI space.
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Databricks' $188 billion（約30兆円） valuation represents a decisive victory for the company's bet on repositioning itself in the AI era. Founded in 2013 during the big-data boom, Databricks initially succeeded by helping enterprises manage and analyze vast data stores in the cloud. That legacy gave it a natural advantage when enterprises began demanding AI: the company already sat on deep customer relationships and access to troves of enterprise data—precisely what companies need to implement AI securely and governably. The company's pivot accelerated sharply in 2025 and 2026, producing a string of AI-focused products (Lakebase, Unity, Omnigent) and positioning itself as an authority on affordable, open-weight models.
What distinguishes Databricks' fundraising momentum is its concrete answer to a real enterprise concern: cost control. CEO Ali Ghodsi's public benchmarking—testing models on actual coding tasks his engineers perform—supplied evidence that open models paired with intelligent agentic harnesses can outperform expensive proprietary alternatives on both quality and price. This is not merely marketing halo; it is a grounded recommendation backed by internal testing. As a result, Databricks has earned credibility not as an AI lab, but as a trusted guide to practical AI deployment for cost-conscious enterprises. The rapid succession of rounds (December 2024, September 2025, February 2026, and now) reflects investor appetite for companies that can translate enterprise pain points into real business value.
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