Databricks has reached a valuation of $188 billion, expanding its business as the second favorite chapter in AI

Databricks on Thursday announced a new round of financing that values ​​the company at $188 billion. The tour was led by Kuato.

Databricks did not disclose exactly how much it raised; She said the money is not in her hands yet and that the tour will close later this summer. (Other outlets have since reported that the increase was approx 3 billion dollars.) Although it’s unusual for a company to go public before it gets money, one venture capitalist told TechCrunch that the deal is solid, with many wishing the company had no reason to keep its shiny new valuation secret.

In fact, Databricks has been on a fundraising tear for a year and a half as it successfully transformed its image into an AI provider and not just a SaaS sensation in the past year. Yesterday we went back to the times before BC (before ChatGPT).

Just five months ago, in February, Databricks closed a $5 billion Series L raise With a value of $134 billion. 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, she raised what amounted to… A record round of $10 billion at the time With a value of $62 billion.

Databricks has sparked many rounds over the years with this latest round being the subject of it Memes about running out of messages From the alphabet. “Turn on alerts when we get a Series AA,” one person posted.

But rebuilding her image was legitimate. Founded in 2013, it achieved initial success in the era of big data, with software that enabled organizations to store massive amounts of data in the cloud, while producing rapid analytics.

Because it already owned large amounts of enterprise data, Databricks was well-positioned to respond when companies began to want AI with the same security and governance they expected from traditional enterprise software.

The company has started rolling out one AI product after another Lakebase, its database designed for AI clientsand Unity, its AI gateway, as well as a “meta tool” called Omnigent that manages multiple clients.

Datablocks are also increasingly common became known As one of the great examples of companies adopting affordable Chinese open-weight models (models whose underlying code is published for anyone to use and modify) to control costs, One of the big trends of 2026. It’s a particular champion of Z.ai’s GLM 5.2 as a coding model.

Last week, Databricks CEO Ali Ghodsi Share the results From some of the internal benchmarks it has done to manage its AI costs for its 3,000 software engineers.

The company compared its AI models to actual tasks performed by its programmers. Not surprisingly, In the blog post revealing the resultsDatabricks shared that “open models, and GLM 5.2 in particular, are now capable of handling even the highest level of task difficulty” in programming, and at a lower overall cost than proprietary models from Anthropic and OpenAI.

But she surprised people to find that the choice of tool — a proxy encoder, such as Codex or Claude Code, that wraps around the model and manages its context and instructions — equally affected costs. I found that open source Pi harnessing is one of the best at managing the context surrounding each router, and therefore one of the less expensive options without sacrificing quality.

“The lesson here is not that one belt is always cheaper or that the original belt is worse.” Another announced. “Instead, model selection is only one piece of the puzzle.”

All of this has added to Databricks’ image as an AI company, even if it was not founded as an AI laboratory. This, in turn, gave it the aura of artificial intelligence to raise money and jump in value. As mentioned earlier, the influence of artificial intelligence is very strong these days, even Jersey Mike’s sandwich shop mentioned AI 22 times in S-1 documents.