Databricks holds a $134 billion valuation following its December 2025 Series L funding round. This capital injection from investors including Insight Partners and Fidelity follows a $100 billion Series K round in August 2025. At the beginning of 2025, the company valuation sat at $62 billion. This jump places Databricks as the fourth most valuable private company in the world. The company reached $3.7 billion in annualized revenue by July 2025. This revenue is a 50% year-over-year increase. Databricks has raised more than $18 billion in total funding through debt and equity.
The company faces competition from Snowflake, which trades at a $66.5 billion market cap. While Databricks is a private entity, its valuation math suggests a significant premium over its closest public market competitor. Databricks also maintains a revenue growth rate of 50% while Snowflake grows at 26%. This difference in speed creates a wide gap in how investors value the two organizations. Databricks has more than 15,000 enterprise customers, and 50 of these customers spend over $10 million annually.
Snowflake AI revenue reaches 100 million
Snowflake Cortex AI drives a $100 million AI revenue run rate for the company. The company reports that 9,100 customer accounts use Snowflake AI capabilities. Snowflake Intelligence scaled to 2,500 accounts within three months of its release. Sridhar Ramaswamy says this tool provides data to every business user. You likely know that both companies dominate the data space, yet their financial trajectories diverge sharply.
The platform enables users to interact with data beyond static dashboards. Customers use the tool to create specialized data agents for various functions. For example, the USA Bobsled team and Fanatics use these capabilities to manage their data. These customers use the data to answer questions that cut across any dimension. This functionality helps employees at all levels of a company interact with complex data.
Growth rates diverge between the giants
Databricks grows at 50% year-over-year. This is nearly double the 26% growth rate of Snowflake. While Snowflake manages a trailing twelve-month revenue of $3.84 billion, Databricks maintains a much faster expansion pace that challenges the existing market leaders. Databricks reached $3.7 billion in annualized revenue by July 2025. Snowflake reported $1.3 billion in Q4 FY 2026 revenue.
The growth of Databricks is tied to its position in the AI infrastructure market. The company builds the tools that power AI transformation across enterprises. Snowflake also captures AI growth, but its growth is tied to the consumption of its AI Data Cloud. Snowflake reported product revenue of $1.2 billion for Q4 FY 2026. This revenue was a 30% increase compared to the previous year.
Customer spending and retention metrics
Databricks maintains a net dollar retention rate above 140%. This level of retention indicates that customers expand their usage of the platform over time. Databricks also has 700 customers that spend more than $1 million annually. Snowflake maintains a net revenue retention rate of 125%. Snowflake also has 733 customers that spend more than $1 million annually.
The concentration of high-spending customers is a factor for both companies. Snowflake has 56 customers that spend more than $10 million annually. Databricks has 50 customers that spend more than $10 million annually. Both companies see significant revenue from these large enterprise accounts. This concentration shows that both platforms are vital for large organizations.
Architecture and operational costs
Databricks uses a lakehouse architecture with Delta Lake, Spark, and Unity Catalog. This setup uses open Parquet files in cloud object storage. Databricks users can read these tables with other engines like Trino or Dremio. The architecture is open, which reduces concerns about vendor lock-in.
Snowflake uses three layers: storage, compute, and cloud services. The storage layer keeps data in a proprietary, columnar format. The compute layer is made of virtual warehouses that scale independently. The cloud services layer manages metadata, security, and query optimization. This design allows Snowflake to handle concurrency by spinning up additional clusters.
| Pricing Element | Snowflake | Databricks |
|---|---|---|
| Compute Currency | Credits | DBUs |
| AI Feature Pricing | AI Credits | DBU-based |
| Storage Rate | ~$23 per TB/month | Cloud provider rates |
| Growth Rate | 26% | 50% |
Pricing models and hidden expenses
Snowflake charges for compute using credits and for storage per terabyte. A single-node XS warehouse uses 1 credit per hour. Storage costs about $23 per terabyte per month for on-demand users. Time Travel features can multiply storage needs by 3 to 5 times the raw data volume. This amplification can increase costs for users who keep long retention periods.
Databricks uses Databricks Units (DBUs) for compute. All-purpose compute costs 2.5 to 3 times more per DBU than jobs compute. Many teams use all-purpose clusters for development but fail to migrate to jobs compute for production. This behavior leads to higher costs. A team with 20 engineers using all-purpose clusters can spend $15,000 to $25,000 per month on idle compute alone. Databricks users must use auto-termination to control these expenses.
AI agent capabilities and workloads
Databricks acquired Neon for $1 billion to build Lakebase, a serverless Postgres database. Lakebase revenue grew at twice the pace of the data warehousing business in its first six months. Databricks also provides Agent Bricks for building and scaling AI agents.
Snowflake provides Cortex AI modules like Cortex Search and Cortex Analyst. Bayer uses Snowflake Cortex AI to enable natural language chat for enterprise analytics. They use Cortex Analyst to query datasets and Document AI to extract contract terms. Alberta Health Services uses Cortex AI to automate clinical documentation in emergency departments. This pilot increases the number of patients physicians see per hour by 10 to 15 percent. Coda uses Snowflake Cortex AI to power its Coda Brain product. Compare Club uses it to analyze customer call transcripts and extract behavioral signals. Markaaz uses Cortex Search to index and vectorize records.
The market verdict on valuation
Databricks is a better value because of its 50% growth and AI positioning. While Snowflake captures AI revenue through its Cortex AI suite, Databricks targets the underlying infrastructure with Lakebase and Agent Bricks. Databricks also has higher net dollar retention at 140% compared to Snowflake’s 125%.
Palantir provides a comparison for these high valuations. Palantir has a $420 billion market cap and 48% growth. Databricks has a much lower revenue scale, but its growth is comparable to Palantir. Will Databricks maintain this growth as it nears an IPO? Databricks continues to expand its reach through Lakebase and Agent Bricks.
