Snowflake vs Databricks

Architecture^1,17

Snowflake: Storage, compute and a cloud services layer are separate. You query with virtual warehouses that Snowflake manages, plus serverless features that Snowflake sizes for you.

Databricks: A Databricks-managed control plane and a compute plane. Classic compute runs in your own cloud account; serverless compute runs in Databricks' account in the same region as your workspace.

Storage and open table formats^8,23,25

Snowflake: Native Snowflake tables, plus Snowflake-managed Iceberg v2 and v3 tables that external engines such as Spark can read and write through Horizon Catalog's Iceberg REST API (GA).

Databricks: Unity Catalog managed tables in Delta Lake or Iceberg. Databricks supports Iceberg spec versions 1, 2 and 3, and Databricks announced managed Iceberg, foreign Iceberg and Iceberg v3 as GA in May 2026.

Compute model^1,16,17

Snowflake: Virtual warehouses you size and suspend, billed per second with a 60-second minimum each time a warehouse starts. Serverless features are resized automatically by Snowflake.

Databricks: Clusters, SQL warehouses and serverless compute, all metered in DBUs at per-second granularity.

Data engineering and pipelines^13,7,27,28

Snowflake: Dynamic tables refresh a query's results to a target lag you set. Openflow, built on Apache NiFi, is GA for ingestion and runs either inside Snowflake or, on AWS only, in your own cloud.

Databricks: Lakeflow: Lakeflow Connect for managed ingestion from SaaS apps and databases, including change data capture (CDC), Lakeflow pipelines built on Spark Declarative Pipelines in SQL or Python, and Lakeflow Jobs for orchestration.

Machine learning and AI^9,12,29

Snowflake: Cortex AI Functions such as AI_CLASSIFY, AI_EXTRACT and AI_EMBED have been GA in SQL since November 2025. Snowflake ML adds a feature store, model registry, GPU training and model serving on Snowpark Container Services.

Databricks: MLflow for experiments and the model lifecycle, Model Serving for custom models and LLMs as REST endpoints with GPU support, a feature store in Unity Catalog, Foundation Model APIs and serverless GPU compute.

Business user AI^10,11,15,33

Snowflake: Snowflake CoWork, formerly Snowflake Intelligence, answers plain-language questions over semantic views and Cortex Search and inherits Snowflake row and column policies.

Databricks: Genie One, formerly Databricks One and then Genie, gives business users chat over governed data. Chat in Genie One has been GA since 15 June 2026.

Governance and catalog^8,22

Snowflake: Horizon Catalog governs Snowflake objects and, with Apache Polaris integrated, serves Iceberg tables to external engines using your existing Snowflake users, roles and policies.

Databricks: Unity Catalog governs tables, volumes, functions, models and services in a catalog.schema.object namespace, with attribute-based policies, row and column filters and automatic lineage. An open-source version also exists.

Streaming^14,30,34,35

Snowflake: Snowpipe Streaming ingests rows continuously, with Snowflake quoting ingest-to-query latency as low as 5 seconds depending on workload.

Databricks: Spark Structured Streaming and Lakeflow pipelines. Real-time mode, a Structured Streaming trigger that has been generally available since March 2026, can reach end-to-end latency as low as five milliseconds, Databricks says. Real-time mode in Lakeflow pipelines entered Public Preview in May 2026.

Operational databases (OLTP)^{4,5,6,18,19,20,21}

Snowflake: Snowflake Postgres has been GA since 24 February 2026, on AWS and Azure only, running Postgres 16, 17 and 18 on dedicated instances.

Databricks: Lakebase is managed Postgres with autoscaling, scale to zero and branching. It is GA on AWS and Azure (Azure since 3 March 2026) and Beta on Google Cloud.

Cloud availability^3,16,17

Snowflake: AWS, Microsoft Azure and Google Cloud, plus SnowGov regions on AWS GovCloud and Azure Government.

Databricks: AWS, Microsoft Azure (as Azure Databricks, billed by Microsoft) and Google Cloud, each with its own documentation edition.

Lock-in and portability^8,24

Snowflake: Native tables are only reachable through Snowflake. Moving to Iceberg tables and Horizon's Iceberg REST endpoint lets other engines read and write the same data.

Databricks: Delta and Iceberg tables are open formats. External Iceberg clients can read and write managed Iceberg tables through Unity Catalog's Iceberg REST endpoint; Delta tables with Iceberg reads enabled are read-only to them.