Databricks on Thursday announced a new funding round that values the company at $188 billion. Cotu led this round.
Databricks did not disclose the exact amount raised. The company said it doesn’t have any cash in hand yet and expects the round to close later this summer. (Other news outlets have since reported that the raise was about $3 billion.) It’s unusual for a company to announce before it receives funding, but one venture capitalist told TechCrunch that the deal was solid, with so many companies wanting to participate that there was no reason to keep its shiny new valuation a secret.
In fact, Databricks has struggled to raise funding for a year and a half as it successfully shifts its image to an AI provider rather than just the SaaS sensation of yesteryear. It was back in the BC era (before ChatGPT).
Just five months ago, in February, Databricks completed a $5 billion Series L funding at a valuation of $134 billion. Five months earlier, in September 2025, it had raised $1 billion at a valuation of $100 billion. Roughly nine months earlier, in December 2024, the company raised a then-record round of $10 billion at a valuation of $62 billion.
Databricks has raised so much money over the years that this latest funding round became the subject of a meme about running out of letters of the alphabet. “I’ll turn on alerts when Series AA comes out,” one person posted.
But the reimaging was warranted. Founded in 2013, the company initially found success in the big data era with its software that allows companies to store vast amounts of data in the cloud while performing rapid analysis.
Because Databricks already existed in a treasure trove of enterprise data, it was well-positioned to respond when enterprises started demanding AI with the same security and governance they expect from traditional enterprise software.
The company began rolling out more AI products, including Lakebase, a database built for AI agents, and a “metaharness” called Omnigent to manage multiple agents, as well as Unity, an AI gateway.
Databricks has also become known as one of the big examples of companies adopting a more affordable China-based open-weight model (where the underlying code is open for anyone to use and modify) to control costs, one of the big trends of 2026. Databricks is a particular supporter of Z.ai’s GLM 5.2 as a model for coding.
Last week, Databricks CEO Ali Ghodsi shared the results of an internal benchmark he conducted to manage AI costs for 3,000 software engineers.
The company compared its AI models based on real-life tasks performed by programmers. Unsurprisingly, in a blog post revealing the results, Databricks shared that “open models, specifically GLM 5.2, are now able to handle even the highest levels of task difficulty in coding” at lower total costs than proprietary models from Anthropic and OpenAI.
But people were surprised to find that the choice of harness (an agent-coding tool such as Codex or Claude Code, which wraps a model and manages its context and instructions) impacts cost just as much. We found the open source Harness Pi to be one of the best at managing the context surrounding each prompt, and therefore one of the lowest cost options without sacrificing quality.
“The lesson here is not that some harnesses are always cheaper or that domestic harnesses are inferior,” the post declared. “Rather, model selection is just one piece of the puzzle.”
All of this further strengthened Databricks’ image as an AI company, even though it wasn’t founded as an AI lab. This gave the company the benefit of AI to raise capital and dramatically increase its valuation. As we previously reported, the impact of AI these days is so strong that even sandwich shop Jersey Mike’s mentions AI 22 times in its S-1 documents.
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