Databricks on Thursday announced a new round of funding that values the company 188 billion dollars. The round was led by Coatue.
Databricks did not disclose exactly how much it raised. she said the money is not in her hands yet and that the round will close later this summer. (Other outlets have since reported that the increase is approximate 3 billion dollars.) While it’s unusual for a company to announce before taking the money, one VC tells TechCrunch that the deal is firm, with so many companies wanting to do it that the company had no reason to keep its shiny new valuation a secret.
In fact, Databricks has been on a fundraising spree for a year and a half as it successfully transformed its image as an AI provider rather than just a SaaS sensation of the past. Yesterday we return to the times BC. (before ChatGPT).
Just 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 $1 billion at a $100 billion valuation. And about nine months before that, in December 2024, it raised what was a then-record $10 billion round at a $62 billion valuation.
Databricks has made so many rounds over the years that this last one has become a thing memes about running out of letters of the alphabet. “Turning on notifications for when we get Series AA,” one person posted.
But the reconstruction of his image was legal. Founded in 2013, it initially found success in the era of big data, with software that allowed businesses to store massive amounts of data in the cloud but produce rapid analytics.
Because Databricks was already in a wealth of enterprise data, Databricks was then able to respond as companies began to want AI with the same security and governance they expect from traditional business software.
The company began releasing one AI product after another, such as Lakebase, its database built for AI agents, and Unity, its AI portal, along with a “meta-dependency” called Omnigent that manages multiple agents.
Databricks also increasingly became known as one of the great examples of enterprises adopting more accessible China-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. He especially champions Z.ai’s GLM 5.2 as a coding model.
Last week, Databricks CEO Ali Ghodsi shared the results from some internal benchmarking done to manage its own AI costs for its 3,000 software engineers.
The company compared AI models to the actual tasks its developers do. Not surprisingly, in the blog post revealing the resultsDatabricks shared that “open models, specifically GLM 5.2, are now able to handle even the highest level of task difficulty” in coding and at an overall lower cost than Anthropic and OpenAI’s proprietary models.
But it surprised people to find that the choice of braid — the agent coding tool, like Codex or Claude Code, that wraps a model and manages its context and instructions — affected the cost just as much. He found the open source Pi harness to be one of the best at managing the frame surrounding each prompt, and thus one of the lowest cost options without sacrificing quality.
“The lesson here is not that a belt is always cheaper or that native belts are worse,” the post declared. “Instead, model selection is only one piece of the puzzle.”
All of this added to Databricks’ image as an AI company, even if it wasn’t founded as an AI lab. This, in turn, has given her the AI-halo to raise money and jump in its valuation. As we mentioned before, the AI phenomenon is so powerful these days that even Jersey Mike’s sandwich mentioned AI 22 times in his S-1 filings.
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