Databricks vs Snowflake

Databricks

• Lakehouse architecture built on Delta Lake (ACID transactions, time travel) with native Apache Iceberg support for open table formats
• Lakeflow for unified data ingestion and pipeline orchestration across batch and streaming sources
• Serverless SQL analytics for BI and reporting on the same copy of data used for engineering and ML
• Unity Catalog for centralized governance, permissions, lineage, and auditing across data and AI assets
• Genie, a natural-language interface for asking business questions and generating insights grounded in an organization's own data
• Agent Bricks framework for building and orchestrating custom AI agents with third-party or open models
• Databricks Apps for deploying data and AI applications directly on the platform
• Lakebase, a Postgres-based operational database for combining transactional and analytical workloads

Snowflake

• Multi-cluster, cross-cloud data warehouse with storage and compute billed and scaled independently
• Snowpark for building data pipelines and applications in Python, Java, and Scala that run inside Snowflake
• Openflow for moving and integrating data from external systems into Snowflake
• Cortex AI for querying large language models and building generative AI features directly on governed data, plus vector search
• Snowflake ML for centralized model development and MLOps from a single UI
• Snowflake Notebooks and Streamlit support for building interactive data apps in Python
• Data Clean Rooms and Snowflake Marketplace for privacy-preserving data sharing and access to thousands of third-party data listings
• Horizon governance layer for built-in compliance, security, and access controls

Databricks

• Lakehouse architecture built on Delta Lake (ACID transactions, time travel) with native Apache Iceberg support for open table formats
• Lakeflow for unified data ingestion and pipeline orchestration across batch and streaming sources
• Serverless SQL analytics for BI and reporting on the same copy of data used for engineering and ML
• Unity Catalog for centralized governance, permissions, lineage, and auditing across data and AI assets
• Genie, a natural-language interface for asking business questions and generating insights grounded in an organization's own data
• Agent Bricks framework for building and orchestrating custom AI agents with third-party or open models
• Databricks Apps for deploying data and AI applications directly on the platform
• Lakebase, a Postgres-based operational database for combining transactional and analytical workloads

• Consumption-based DBU pricing makes month-to-month costs harder to predict than a flat per-seat fee
• No published self-serve price list for compute rates — teams must use the pricing calculator or talk to sales to estimate real-world cost

Snowflake

• Multi-cluster, cross-cloud data warehouse with storage and compute billed and scaled independently
• Snowpark for building data pipelines and applications in Python, Java, and Scala that run inside Snowflake
• Openflow for moving and integrating data from external systems into Snowflake
• Cortex AI for querying large language models and building generative AI features directly on governed data, plus vector search
• Snowflake ML for centralized model development and MLOps from a single UI
• Snowflake Notebooks and Streamlit support for building interactive data apps in Python
• Data Clean Rooms and Snowflake Marketplace for privacy-preserving data sharing and access to thousands of third-party data listings
• Horizon governance layer for built-in compliance, security, and access controls

• No published self-serve price list — per-credit rates depend on cloud, region, and edition and require the pricing calculator or a sales conversation
• No perpetual free tier; after the 30-day/$400 trial ends, a credit card is required to continue