Databricks Soars to $188 Billion Valuation as AI Momentum Builds

Key points

• Databricks announced a valuation of $188 billion in a new funding round led by Coatue as of 2026.
• The company has raised $5 billion this year alone, growing from a $134 billion valuation.
• Databricks is known for using more affordable Chinese-based open-weight AI models like Z.ai's GLM 5.2.

Databricks, a company that once thrived in the big data era, is riding the AI wave to new heights. It has announced a new funding round, led by Coatue, that values it at an impressive $188 billion. While the exact amount of funds raised remains undisclosed, other reports suggest it’s around $3 billion.

Founded in 2013, Databricks initially helped businesses store and analyze large amounts of data in the cloud. However, in recent years, it has rebranded itself as a major player in artificial intelligence (AI). This transformation has been so successful that it managed to secure $5 billion in funding this year alone, climbing from a $134 billion valuation just five months ago.

The company's strategic pivot comes as businesses demand AI solutions that offer the same security and governance as traditional software. Databricks has delivered by releasing several AI tools, including Lakebase and Unity, designed to make AI integration smoother for enterprises.

One of Databricks' notable strategies is its embrace of more affordable open-weight models, which are AI models whose underlying code is available for anyone to use and modify. Specifically, it champions Z.ai's GLM 5.2 model for coding tasks, emphasizing cost efficiency without compromising performance. This approach has resonated with many businesses looking to manage AI expenses effectively.

Last week, CEO Ali Ghodsi shared insights from internal tests to optimize AI costs for Databricks' own software engineers. The results showed that not only do open models like GLM 5.2 handle complex coding tasks well, but the choice of harness, a tool that manages an AI model's context and instructions, also impacts costs significantly. Databricks found the open-source harness, Pi, to be particularly effective.

This strategic use of AI models and tools has helped solidify Databricks' reputation as a significant AI company, which in turn boosts its appeal to investors.

What happens next?

For Databricks, the path forward involves continuing to innovate in AI and refining its products to meet evolving enterprise needs. For businesses and developers, this means access to efficient, cost-effective AI tools that can be seamlessly integrated into their existing systems. If you're managing AI in your organization, consider exploring open-weight models and harnesses like those Databricks champions, they could offer both performance and savings.