Databricks has achieved a $188 billion valuation, solidifying its position as a major AI company. The company has also published research highlighting the cost savings of open-weight AI models for coding tasks.
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Databricks completed four funding rounds in 18 months, with its valuation leaping from $62 billion to $188 billion. Its success stems not only from its AI product suite but also from being an early adopter of Chinese open-source models to reduce enterprise AI costs, revealing a new competitive logic in the AI infrastructure layer.
On July 17, 2026, Databricks announced a new funding round at a $188 billion valuation, led by Coatue. The company did not disclose the exact funding amount, but other media reported it to be around $3 billion. Notably, the funds have not yet been received and are expected to close by the end of summer. A venture capitalist told TechCrunch that the deal is very solid due to the rush of many investors to participate, and the company does not need to keep it confidential.
This is Databricks' fourth funding round in 18 months: $10 billion at a $62 billion valuation in December 2024; $1 billion at a $100 billion valuation in September 2025; and $5 billion at a $134 billion valuation in February 2026. The valuation has more than tripled in just a year and a half, sparking jokes in the industry about exhausting the funding alphabet, with one netizen posting 'Series AA reminder incoming.'
Founded in 2013, Databricks initially succeeded with cloud data storage and analytics software in the big data era. As enterprise AI demand exploded, the company leveraged its existing enterprise data assets to quickly pivot to AI, launching a series of products: Lakebase (a database built for AI agents), Unity (an AI gateway), and Omnigent (a 'meta-tool' for managing multiple agents).
This transformation has made Databricks a model for the 'second act' in AI—not originating from an AI lab, yet successfully rebranding itself. TechCrunch noted that the 'AI halo' effect is so strong that even sandwich chain Jersey Mike's mentioned AI 22 times in its S-1 filing.
Databricks CEO Ali Ghodsi shared internal benchmark results last week aimed at managing AI costs for its 3,000 software engineers. The tests compared the performance of different AI models on real programming tasks. The results showed that 'open-source models, especially GLM 5.2, can now handle the highest difficulty coding tasks' with total costs lower than proprietary models from Anthropic and OpenAI. GLM 5.2 comes from Chinese AI company Z.ai and is an open-weight model.
The tests also found that the choice of agentic coding tools (e.g., Codex, Claude Code) is as important as the model itself for cost. The open-source tool Pi performed best in context management, achieving the lowest cost while maintaining quality. Ghodsi concluded in his blog post: 'The lesson is not that one tool is always cheaper, or that native tools are worse; rather, model selection is only part of the puzzle.'
This finding reinforces Databricks' image as an advocate for enterprise AI cost control and also confirms a major trend in 2026: enterprises are increasingly adopting cheaper Chinese open-weight models.
This article is primarily based on a TechCrunch report, which cited Databricks' official announcements, the CEO's blog, and comments from venture capitalists. The $3 billion funding amount is from other media reports and has not been officially confirmed by Databricks. The internal benchmark results come from Databricks' official blog, but details such as testing methodology and sample size have not been fully disclosed. Valuation data comes from company announcements, but the funds have not yet been received, introducing some uncertainty.
Databricks' valuation surge reflects the immense value of the AI infrastructure layer. Its success lies not only in product innovation but also in providing enterprises with a practical path to AI adoption by adopting Chinese open-source models and optimizing agentic tool costs. This strategy may reshape the competitive landscape of the AI industry chain.
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