Databricks values at $188 billion
Databricks announced on Thursday a funding round valuing the firm at $188 billion. Coatue led the investment.
The company did not state the exact sum raised. It noted the capital has not yet arrived and the round will conclude later this summer. Other media reports suggest the raise is approximately $3 billion. Announcing a deal before funds land is rare. A venture capitalist told TechCrunch the agreement is firm. So many firms want in, Databricks has no reason to hide its new valuation.
Databricks has enjoyed a year-and-a-half fundraising streak. It successfully rebranded as an AI provider rather than just a legacy SaaS firm. Legacy is now a term for the period before ChatGPT.
Only five months ago, in February, Databricks closed a $5 billion Series L raise at a $134 billion valuation. Five months prior, in September 2025, it raised $1 billion at a $100 billion valuation. Roughly nine months before that, in December 2024, it secured a record-breaking $10 billion round at a $62 billion valuation.
Databricks has raised so many rounds that jokes emerged about running out of alphabet letters. One person posted about setting alerts for a Series AA round.
The rebranding is genuine. Founded in 2013, the company grew during the big data era. Its software allowed enterprises to store massive datasets in the cloud while generating fast analytics.
Because it already held large volumes of enterprise data, Databricks was well positioned when companies demanded AI with the same security and governance expected from traditional enterprise software.
The company began releasing AI products one after another. These include Lakebase, a database built for AI agents, and Unity, an AI gateway. It also launched Omnigent, a tool designed to manage multiple agents.
Databricks also became a key example of enterprises adopting affordable Chinese-based open-weight models. This approach publishes underlying code for anyone to use or modify. It is one of the major trends of 2026. The firm is a particular champion of Z.ai’s GLM 5.2 for coding tasks.
Last week Databricks CEO Ali Ghodsi shared results from internal benchmarking. He used the data to manage AI costs for his 3,000 software engineers.
The company compared AI models on the actual tasks its programmers perform. In the blog post revealing the results, Databricks stated that “open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty” in coding. They also cost less than proprietary models from Anthropic and OpenAI.
It did surprise people to find that the choice of harness impacted costs equally. A harness is an agentic coding tool, like Codex or Claude Code, that wraps around a model to manage context and instructions. The firm found the open-source harness Pi to be one of the best at managing context around each prompt. This makes it one of the lowest cost choices without sacrificing quality.
“The lesson here isn’t that one harness is always cheaper or that native harnesses are worse. Instead, model choice is only one piece of the puzzle,” the post declared.
All of this has added to Databricks image as an AI company, even though it was not founded as an AI lab. This has granted it an AI halo for raising money and increasing its valuation. As previously reported, the AI effect is so strong that sandwich shop Jersey Mike’s mentioned AI 22 times in its S-1 documents.
What it means
For developers and business leaders, the shift means cost savings are no longer solely about switching to open-source models. The tool wrapping around the model matters just as much. Choosing the right harness can lower expenses while maintaining performance.
