Databricks on Thursday announced a new round of funding that values the company at $188 billion. The round was led by Coatue.
Databricks didn’t disclose exactly how much it raised; it said the money isn’t in its hands yet and that the round will close later in this summer. (Other outlets have since reported the raise is roughly $3 billion.) While it’s unusual for a company to announce before it gets the money, a VC tells TechCrunch that the deal is solid, with so many firms wanting in that the company had no reason to keep its shiny new valuation a secret.
In fact, Databricks has been on a year-and-a-half fundraising tear as it successfully transitioned its image into an AI provider and not just a yesteryear SaaS sensation. Yesteryear being back in the BC times (Before ChatGPT).
Only five months ago, in February, Databricks closed a $5 billion Series L raise at a $134 billion valuation. Five months before that, in September 2025, it raised $1B at $100 billion valuation. And roughly nine months before that, in December 2024, it raised what was a record-breaking round at the time of $10 billion at a $62 billion valuation.
Databricks has raised so many rounds over the years that this latest one became the subject of memes about running out of letters of the alphabet. “Turning on alerts for when we get a Series AA,” one person posted.
But its image reconstruction has been legit. Founded in 2013, it initially grew to success back in the big data era, with software that enabled enterprises to store enormous amounts of data in the cloud, yet produce speedy analytics.
Because it already sat on troves of enterprise data, Databricks was then well-positioned to respond as companies started wanting AI with the same security and governance they expect from traditional enterprise software.
The company began rolling out one AI product after another, like Lakebase, its database built for AI agents, and Unity, its AI gateway, along with a “meta-harness” called Omnigent that manages multiple agents.
Databricks also increasingly became known as one of the big examples of enterprises adopting more affordable Chinese-based open-weight models (models whose underlying code is published for anyone to use and modify) for cost control, one of the big trends of 2026. It is a particular champion of Z.ai’s GLM 5.2 as a model for coding.
Last week Databricks CEO Ali Ghodsi shared the results of some internal benchmarking done to manage his own AI costs for his 3,000 software engineers.
The company compared AI models on the actual tasks its programmers do. Not surprisingly, in the blog post revealing the results, Databricks shared that “open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty” in coding, and at a total lower cost than proprietary models from Anthropic and OpenAI.
But it did surprise people by finding that the choice of harness — the agentic coding tool, like Codex or Claude Code, that wraps around a model and manages its context and instructions — equally impacted costs. It found that open-source harness, Pi, to be one of the best at managing context surrounding each prompt, and therefore one of the lowest costs 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 if it wasn’t founded as an AI lab. This, in turn, has granted it the AI-halo for raising money and leaping its valuation. As we previously reported, the AI effect is so strong these days, that even sandwich shop Jersey Mike’s mentioned AI 22 times in its S-1 documents.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.