AI revenue drives Snowflake growth
Snowflake reported $1.49 billion in product revenue for the second quarter of fiscal 2027, a 37% year over year increase. AI products accounted for half of this growth acceleration. Cortex Code surpassed 9,100 accounts, while CoWork reached 5,800 accounts. The company expects full-year product revenue to reach $6.07 billion. Sridhar Ramaswamy, the CEO, noted that more than 3,200 customers are testing AI tools. The launch of Cortex AI, Cortex Sense, and the Cortex AI Gateway provides new ways to manage AI workloads. This expansion follows the integration of Natoma to extend the agentic control plane. Snowflake also released Arctic, an open model, to compete in the foundational model space. The company added 23% to its workforce, reaching 12,486 employees in August 2026. Web traffic also grew to more than 5 million monthly visits. Approximately 25% of the use cases deployed in the second quarter involved AI in some form. Snowflake manages 828 customers with more than $1 million in trailing 12-month product revenue. Snowflake reported $1.55 billion in total revenue for the quarter. Net revenue retention reached 127%.
Databricks and Snowflake compete for AI budgets
Databricks maintains a $62 billion valuation and reported $3.7 billion in ARR for fiscal 2026, a 55% year over year increase. The competition between these two platforms involves different architectural philosophies that dictate how an organization manages data storage, handles compute scaling, and enforces governance across its entire infrastructure. Databricks uses an open lakehouse paradigm built on Delta Lake and Unity Catalog to support Python and Scala. Snowflake uses a multi-cluster shared data architecture that separates storage and compute to provide operational simplicity. Databricks captures 70% of incremental budget in head-to-head evaluations for new AI/ML workloads such as LLM training and feature engineering. Snowflake wins 60% of the budget for new analytics and BI workloads. Databricks expanded its capabilities by acquiring Quotient AI, Mooncake Labs, and Einblick. Snowflake countered by acquiring Datometry, Observe, Inc., and Crunchy Data. You already know that engineering-heavy teams prefer Databricks, but Snowflake closes the gap with Snowpark and Snowpark Container Services. Snowflake uses Snowpark to allow developers to run Python and Java code within its security perimeter.
| Feature | Snowflake | Databricks |
|---|---|---|
| Primary Language | SQL | Python, Scala, SQL |
| Storage Format | Proprietary & Iceberg | Delta Lake & Iceberg |
| AI Focus | Managed Cortex AI | Mosaic AI & MLflow |
| Gross Margin | ~75% | 60-65% |
Technical divergence in the AI data cloud
The market includes significant overlap as 14 companies, including OpenAI, use both Snowflake and Databricks. Snowflake leads in the Fintech and Cybersecurity sectors, while Databricks captures more of the technology company market. Databricks leads in the healthcare and life sciences sectors, while the retail industry is split between both providers. Snowflake reported a net revenue retention rate of 127% for fiscal 2026. Databricks loses money at scale, with estimated net losses between $1.5 billion and $2 billion for fiscal 2025. Snowflake’s market cap sits at $55 billion, while Databricks remains private after raising $10 billion in a recent round. The decision to deploy a platform depends on whether an organization needs a managed environment or an engineering-centric workbench. Snowflake provides the better option for SQL-centric analytics and governed data access, while Databricks remains the superior choice for advanced data engineering and custom ML pipelines. On the usedby platform, 88 companies use Snowflake and 52 use Databricks. Will the growing adoption of open formats like Iceberg eventually reduce the switching costs for Snowflake customers?
