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Databricks hits $188B as AI boom lifts private valuations
Databricks was valued at $188 billion in a new funding round reportedly worth about $3 billion, extending a rapid run fueled by enterprise AI demand.
Image: ITzine
Databricks has been valued at $188 billion in a new funding round, setting another high-water mark for private AI companies. The company did not disclose the exact size of the deal, but media reports put it at roughly $3 billion. The round is expected to close later this summer.
The rise has been unusually steep even by current AI-market standards. In February 2026, Databricks raised $5 billion at a $134 billion valuation. In September 2025, it raised $1 billion at $100 billion. And in December 2024, it secured $10 billion at $62 billion. In about a year and a half, the company’s valuation has nearly tripled.
Founded in 2013, Databricks started with a platform for storing and analyzing large enterprise datasets. Its business now extends well beyond that core: the company is selling a broader mix of data platform services, AI development tools, and infrastructure for enterprise agents.
How Databricks is pushing beyond data storage
Databricks originally focused on helping companies collect, process, and analyze data without building bulky in-house infrastructure. As the market shifted toward generative AI, the value moved from storage alone to platforms that can support model training, search across enterprise data, and the deployment of internal AI services.
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That puts Databricks in a crowded field. Snowflake is building a similar business around enterprise data and AI features. OpenAI and Anthropic sell direct access to models. Microsoft and Amazon offer an even broader stack, combining models, infrastructure, and development tools.
To avoid being seen as just a data warehouse, Databricks has been pushing its AI lineup harder. The company highlights:
- Lakebase, a database for AI agents
- Unity, a gateway
- Omnigent, a multi-agent management system
The pitch is not a single breakthrough product, but a combined stack of data, access, orchestration, and control — often the deciding factor for enterprise buyers.
Open models and lower-cost coding AI
Databricks is also emphasizing open AI models. It has specifically pointed to GLM 5.2 from China’s Z.ai as a cheaper coding alternative to closed offerings from OpenAI and Anthropic.
That argument is practical in the enterprise market, where model quality is only part of the equation and cost matters just as much. According to the company’s internal tests across 3,000 of its own developers, usage cost depends not only on the model itself, but also on the software layer that manages prompt context and task routing. In other words, the savings do not start with tokens alone, but with how the full system is designed.
Enterprise Editor
Marcus follows the money. He covers enterprise software, cloud architecture, and the tectonic shifts in Big Tech strategy. He translates dense earnings calls and complex M&A activity into actionable insights about where the industry is actually heading. If a tech giant makes a silent pivot, Marcus is usually the first to notice.
via ITzine
