Databricks' valuation surged 40% to $188 billion as Coatue Management leads a new investment, reflecting the AI boom's demand for data infrastructure.
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Databricks hits $188 billion valuation as Coatue leads new funding round
Databricks' valuation surged 40% to $188 billion as Coatue Management leads a new investment, reflecting the AI boom's demand for data infrastructure.
Databricks, the data-analytics and AI software startup, is set to receive a new investment from Coatue Management that values the company at approximately $188 billion, a 40% increase from its prior round as enterprise AI adoption drives demand for its platform, according to people familiar with the matter.
The startup has become a central piece of the AI infrastructure stack, helping large businesses store, organize and deploy data for machine learning workloads. Its platform competes with Snowflake in data warehousing and has expanded into AI model deployment through products like Genie Code, a coding agent that routes tasks across frontier, open-source and custom models.
The $188 billion valuation places Databricks among the most valuable private technology companies globally, trailing only SpaceX and ByteDance among venture-backed firms. The 40% valuation increase comes as enterprise customers accelerate spending on AI infrastructure, with Databricks reporting that its customers are increasingly running model bake-offs on real internal code rather than public benchmarks.
The Benchmark That Reshaped Enterprise AI Buying
Days before the funding news, Databricks published a benchmark built from its own engineers' completed work — real code changes drawn from a codebase running millions of lines across more than ten programming languages. The results showed that open-source models, including Zhipu AI's GLM 5.2 released free in mid-June, statistically tied Anthropic's Opus 4.8 on everyday coding tasks at roughly two-thirds of the cost per completed task. Opus completed 87 percent of tasks at $1.94 each, while GLM 5.2 completed a statistically similar share at $1.28 per task.
The benchmark matters because it shifts how enterprise buyers evaluate models. Public leaderboards have a known problem — tasks leak into training data, and every lab tunes against them. Databricks' test measured what engineers actually do: messy, repetitive work tied to old systems and internal tools. The finding that a free, open-weight model matches the best paid model on real work gives procurement teams a data point to justify routing everyday tasks to cheaper tiers.
What the Valuation Says About the Market
The Coatue investment shows that private market investors view Databricks as a platform-layer winner in AI, not just a tool vendor. As model prices collapse — Chinese-origin models now hold at least 30 percent of enterprise token volume on OpenRouter, running 60 percent to 90 percent cheaper than comparable US offerings — pricing power shifts to the layer that controls testing, routing and compliance sign-off. Databricks owns that layer for a growing share of enterprise AI workloads.
The valuation also reflects a broader re-rating of AI infrastructure companies. Snowflake, Databricks' closest public comparable, trades at roughly 18 times forward sales. Databricks' $188 billion valuation, if measured against its reported revenue run rate, would imply a multiple that exceeds Snowflake's, reflecting the premium private markets assign to AI-native platforms with direct exposure to model deployment.
For investors, the read-through is clear: the platform layer is where AI value is concentrating. Companies that control how models are tested, governed and routed into business workflows are capturing pricing power as models themselves become interchangeable commodities. Databricks' 40% valuation jump in a single round is the market's bet that this dynamic has years left to run.
This article is for informational purposes only and does not constitute investment advice.
