Databricks announced a new funding round on Thursday, setting its valuation at $188 billion. The investment was led by Coatue. The company did not reveal the exact amount raised, stating that the funds have not yet been received and that the round will close later this summer. Other outlets have since reported the raise to be approximately $3 billion.

In fact, Databricks has been on a fundraising spree for the past year and a half, successfully reshaping its identity from a legacy SaaS sensation into an AI provider. The term "legacy" here refers to the era before ChatGPT. Just five months ago, in February, Databricks closed a $5 billion Series L round at a $134 billion valuation. Five months before that, in September 2025, it raised $1 billion at a $100 billion valuation. Roughly nine months before that, in December 2024, it secured a then-record-breaking $10 billion round at a $62 billion valuation. Databricks has raised so many rounds over the years that this latest one sparked memes about running out of alphabet letters. "Turning on alerts for when we get a Series AA," one person posted.

However, its image transformation has been legitimate. Founded in 2013, Databricks initially found success in the big data era by providing software that allowed enterprises to store massive amounts of data in the cloud while still delivering fast analytics. Because it already held vast troves of enterprise data, Databricks was well-positioned to respond as companies began demanding AI with the same security and governance they expected from traditional enterprise software. The company started rolling out one AI product after another, including Lakebase, its database built for AI agents, and Unity, its AI gateway, along with a "meta-harness" called Omnigent that manages multiple agents.

Databricks has also become known as a prominent example 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 major 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 internal benchmarking conducted to manage his own AI costs for his 3,000 software engineers. The company compared AI models on the actual tasks its programmers perform.

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 lower total cost than proprietary models from Anthropic and OpenAI. However, 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 the open-source harness, Pi, was one of the best at managing context surrounding each prompt, and therefore 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 contributed to Databricks' image as an AI company, even though it wasn't founded as an AI lab. This, in turn, has granted it the AI halo for raising money and boosting its valuation. As 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.