Databricks Raises $4B at $134B Valuation, Overtakes Snowflake in Data Wars

Databricks Pulls Ahead in Data Platform Race

Databricks announced it is raising over $4 billion in a Series L investment, valuing the company at $134 billion, while crossing a $4.8 billion revenue run-rate during Q3 with growth exceeding 55% year over year. The round was led by Insight Partners, Fidelity Management & Research Company, and J.P. Morgan Asset Management, with participation from a syndicate that reads like a who's who of institutional investors. The funding comes as the company achieved positive free cash flow over the last 12 months, demonstrating both hypergrowth and financial discipline rarely seen at this scale. The timing marks a pivotal shift in the competitive landscape. Databricks and Snowflake have converged at approximately the same revenue scale, but their trajectories diverge sharply. While Snowflake's revenue for the twelve months ending July 31, 2025 was $4.116B, growing at 28.37% year-over-year, Databricks is now outpacing its primary competitor by nearly double in growth rate. Even more striking, Databricks' biggest competitor is Snowflake, yet Databricks commands a valuation nearly twice that of Snowflake's public market capitalization. The competitive gap widens further when examining AI capabilities. Databricks achieved over $1 billion revenue run-rate for its AI products, a milestone that underscores the company's architectural advantage in the converging data and AI market. While traditional cloud data warehouses like Snowflake, BigQuery, and Redshift are primarily focused on structured data analytics, the lakehouse architecture championed by Databricks aims to provide a single, unified platform for data engineering, SQL analytics, and advanced AI/ML. This positioning has proven prescient as enterprises increasingly demand integrated solutions rather than stitching together separate systems. The $4 billion capital injection will accelerate development across three strategic products targeting what Databricks calls "Data Intelligent Applications." Lakebase serves as the first serverless Postgres database purpose-built for the age of AI, while Databricks Apps and Agent Bricks complete the stack for building and deploying AI-native applications. In its first six months, Lakebase already has thousands of customers and is growing revenue at twice the pace of its Data Warehousing product, suggesting the company has found product-market fit with its newest offering.

Dominating the Enterprise Market

The company's market position continues to strengthen across key metrics. Databricks maintains a net retention rate exceeding 140%, indicating existing customers are dramatically expanding their usage. More than 700 customers now consume at over $1 million annual revenue run-rate, demonstrating deep enterprise penetration. Over 20,000 organizations worldwide, including over 60% of the Fortune 500, rely on Databricks to power their data and AI workloads. According to recent enterprise surveys, incumbents like Databricks and Snowflake hold 56% of the AI infrastructure market as many AI app builders continue building on the data platforms they've trusted for years. However, Databricks' AI revenue advantage appears structural rather than cyclical, with their architecture purpose-built for what enterprises need next. This architectural differentiation, rooted in Apache Spark for big data workloads, MLflow for machine learning lifecycles, and Delta Lake for unified data management, has positioned Databricks to capture the shift toward AI-first data platforms. "Enterprises are rapidly reimagining how they build intelligent applications, and the convergence of generative AI with new coding paradigms is opening the door to entirely new workloads," said Ali Ghodsi, co-founder and CEO of Databricks. The investment will also provide liquidity for employees, support potential AI acquisitions, and deepen the company's AI research capabilities, further cementing its position as the platform of choice for data-intensive AI workloads.