Databricks Reaches $188 Billion Valuation as AI Strategy Fuels Growth

Databricks has announced a new funding round that values the enterprise software company at $188 billion, extending one of the most remarkable valuation surges in the artificial intelligence sector. Coatue is leading the investment round, although the company said the financing has not yet formally closed and is expected to be completed later this summer.

While Databricks did not disclose the amount being raised, multiple reports have placed the funding at around $3 billion. Announcing a valuation before a deal officially closes is uncommon. Still, the company appears to have benefited from exceptionally strong investor demand, making it comfortable revealing its latest milestone ahead of receiving the capital.

The latest valuation continues an extraordinary fundraising streak that has transformed Databricks from a cloud data analytics company into one of the most highly valued enterprise AI businesses. In February, the company completed a $5 billion Series L funding round at a $134 billion valuation. That followed a $1 billion raise at a $100 billion valuation in September 2025 and a record-breaking $10 billion funding round at a $62 billion valuation in December 2024.

Founded in 2013, Databricks originally built its reputation by helping organisations store massive amounts of enterprise data in the cloud while enabling rapid analytics. As generative AI adoption accelerated, the company leveraged its existing position within enterprise data infrastructure to introduce artificial intelligence products designed to meet corporate requirements for security, governance and scalability.

Over the past two years, Databricks has steadily expanded its AI portfolio with products including Lakebase, a database designed for AI agents; Unity, its AI gateway; and Omnigent, a platform designed to coordinate multiple AI agents. These additions have strengthened its position as enterprises increasingly seek integrated AI platforms rather than standalone language models.

The company has also become a prominent supporter of open-weight AI models as businesses search for lower-cost alternatives to proprietary systems. Databricks has been particularly vocal about the capabilities of Z.ai’s GLM 5.2 model for software development, arguing that open models can deliver competitive coding performance while significantly reducing operating costs.

Chief Executive Ali Ghodsi recently shared the results of internal benchmarking carried out across coding tasks performed by the company’s approximately 3,000 software engineers. According to the findings, open models, particularly GLM 5.2, were able to handle even highly complex programming work while offering lower overall costs than proprietary models developed by companies such as OpenAI and Anthropic.

The benchmarking also suggested that selecting the right AI coding harness can influence efficiency just as much as choosing the underlying language model. Databricks found that the open-source Pi harness was among the most cost-effective options for managing context and prompts without sacrificing output quality, highlighting that enterprise AI optimisation depends on multiple layers of technology rather than model selection alone.

Databricks’ rapid evolution into a leading enterprise AI platform has significantly reshaped investor perception of the company. Once primarily recognised for big data software, it is now viewed as one of the biggest beneficiaries of the enterprise AI boom. This transformation has fuelled successive funding rounds and one of the fastest increases in valuation in the technology sector.