Databricks Soars to $188B, Solidifying Its Role as AI's Top Second Act

Databricks revealed on Thursday a fresh funding round, elevating the company's valuation to an impressive $188 billion. This latest investment initiative was spearheaded by Coatue.

While Databricks did not specify the exact amount secured, it noted that the funds are yet to be disbursed and the round is anticipated to conclude later this summer. (Reports from other sources suggest the raise is approximately $3 billion.) Despite the unconventional timing of announcing a funding round prior to its official closure, a venture capitalist informed TechCrunch that the deal is robust, with significant interest from numerous firms, negating any need for Databricks to withhold its new, substantial valuation.

This funding spree marks a consistent trend for Databricks over the past eighteen months, during which it has successfully redefined its public perception from a legacy SaaS provider to a leading AI innovator. The "yesteryear" context refers to the era "Before ChatGPT" (BC).

This follows a rapid succession of significant capital injections: just five months prior, in February, Databricks finalized a $5 billion Series L round, valuing it at $134 billion. Five months before that, in September 2025, it secured $1 billion at a $100 billion valuation. Approximately nine months earlier, in December 2024, the company raised a then-record-breaking $10 billion, achieving a $62 billion valuation.

The frequency of Databricks' fundraising efforts over the years has become a notable point, even inspiring humorous memes about exhausting the alphabet for series designations. As one individual quipped, "Turning on alerts for when we get a Series AA."

Nevertheless, this transformation of its corporate identity is well-founded. Established in 2013, Databricks initially achieved prominence during the big data era, offering software that empowered enterprises to efficiently store vast quantities of data in the cloud while simultaneously generating rapid analytics.

Leveraging its existing foundation of extensive enterprise data, Databricks was uniquely positioned to address the burgeoning demand from companies for AI solutions that uphold the same stringent security and governance standards expected of conventional enterprise software.

The company subsequently launched a series of AI-centric products, including Lakebase, its specialized database designed for AI agents, and Unity, its AI gateway. It also introduced Omnigent, a "meta-harness" engineered to manage multiple AI agents.

Databricks has also gained recognition as a prominent example of enterprises embracing more cost-effective, Chinese-based open-weight models — AI models with publicly available and modifiable underlying code — as a key strategy for cost control, aligning with a major trend observed in 2026. The company specifically advocates for Z.ai’s GLM 5.2 as a preferred model for coding tasks.

Last week, Databricks CEO Ali Ghodsi released the findings from internal benchmarking conducted to optimize AI-related expenditures for his team of 3,000 software engineers.

The company assessed various AI models against the practical tasks performed by its programmers. As detailed in the blog post unveiling these results, Databricks confirmed that “open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty” in coding, achieving this at a significantly lower overall cost compared to proprietary alternatives from Anthropic and OpenAI.

Intriguingly, the benchmarking also revealed that the selection of the "harness" — the agentic coding tool, such as Codex or Claude Code, that encapsulates a model and manages its context and instructions — had an equally significant impact on costs. Specifically, the open-source harness, Pi, was identified as exceptionally proficient in managing the contextual information for each prompt, thereby offering one of the most cost-effective solutions without compromising quality.

The post concluded, stating, “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.”

Collectively, these developments have solidified Databricks' reputation as a formidable AI company, despite its origins not being that of an AI research lab. This "AI-halo" has, in turn, facilitated its impressive fundraising success and substantial valuation growth. As previously reported, the pervasive influence of AI is currently so potent that even a sandwich chain like Jersey Mike’s referenced AI 22 times in its S-1 documents.