# Snowflake vs Databricks

> Choose Snowflake if most of your work is SQL analytics and you want a managed warehouse that a small team can run. Choose Databricks if you also do heavy data engineering, streaming, machine learning or agents on the same data, or want your tables in open formats in your own cloud account. Both now read and write Iceberg, so running both is realistic.

Canonical: https://www.techfabric.com/compare/snowflake-vs-databricks
Written by TechFabric, a Databricks partner. Checked against the vendors' own pages on 2026-10-10.

## Snowflake

A managed cloud data platform where you load data into Snowflake and query it with SQL on virtual warehouses billed in credits.

## Databricks

A data and AI platform built on Apache Spark, where data lives in open Delta Lake or Iceberg tables governed by Unity Catalog.

## Choose Snowflake when

- Your workload is mostly SQL reporting and dashboards, and your team is SQL analysts rather than Python or Spark engineers.
- You want one managed service with almost no infrastructure to tune, and per-second warehouse billing you can switch off when idle.
- You share data with partners or customers who are already on Snowflake.
- You mainly want AI inside SQL, such as classification, extraction and embeddings with Cortex AI Functions, without running ML infrastructure.

## Choose Databricks when

- You run large Python or Spark pipelines, streaming jobs, or model training alongside your SQL analytics and want one governance layer over all of it.
- You want tables in open Delta Lake or Iceberg formats, with classic compute running inside your own cloud account.
- You are building AI agents or ML models that need model serving, feature serving and experiment tracking next to the data.
- You need sub-second stream processing. Databricks says real-time mode in Structured Streaming, generally available since March 2026, can reach end-to-end latency as low as five milliseconds.

## Differences

| | Snowflake | Databricks |
| --- | --- | --- |
| Architecture | 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. | 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. [1, 17] |
| Storage and open table formats | 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). | 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. [8, 23, 25] |
| Compute model | 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. | Clusters, SQL warehouses and serverless compute, all metered in DBUs at per-second granularity. [1, 16, 17] |
| Data engineering and pipelines | 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. | 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. [13, 7, 27, 28] |
| Machine learning and AI | 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. | 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. [9, 12, 29] |
| Business user AI | Snowflake CoWork, formerly Snowflake Intelligence, answers plain-language questions over semantic views and Cortex Search and inherits Snowflake row and column policies. | 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. [10, 11, 15, 33] |
| Governance and catalog | Horizon Catalog governs Snowflake objects and, with Apache Polaris integrated, serves Iceberg tables to external engines using your existing Snowflake users, roles and policies. | 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. [8, 22] |
| Streaming | Snowpipe Streaming ingests rows continuously, with Snowflake quoting ingest-to-query latency as low as 5 seconds depending on workload. | 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. [14, 30, 34, 35] |
| Operational databases (OLTP) | Snowflake Postgres has been GA since 24 February 2026, on AWS and Azure only, running Postgres 16, 17 and 18 on dedicated instances. | 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. [4, 5, 6, 18, 19, 20, 21] |
| Cloud availability | AWS, Microsoft Azure and Google Cloud, plus SnowGov regions on AWS GovCloud and Azure Government. | AWS, Microsoft Azure (as Azure Databricks, billed by Microsoft) and Google Cloud, each with its own documentation edition. [3, 16, 17] |
| Lock-in and portability | 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. | 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. [8, 24] |

## Pricing

Snowflake: You pay for compute in credits, storage at a flat monthly rate per TB of compressed data, and data transfer out of a region. Warehouses bill per second with a 60-second minimum on each start, and cloud services are charged only when they exceed 10% of daily warehouse usage. Credit and storage rates depend on edition (Standard, Enterprise, Business Critical, or Virtual Private Snowflake, known as VPS) and region, and you can buy on demand or as prepaid capacity.

Databricks: You pay for DBUs, a normalized unit of processing, billed per second with no upfront cost on pay as you go; committed use contracts bring discounts. On classic compute, storage and networking are billed separately by your cloud provider. On Azure, Microsoft sets and bills Azure Databricks pricing. [1, 2, 16]

## Using both

Snowflake can write Iceberg tables that Databricks then reads through Unity Catalog's Snowflake catalog federation. That feature reads Snowflake-managed Iceberg tables straight from cloud storage on Databricks compute. In the other direction, Snowflake can attach Unity Catalog's Iceberg REST endpoint through a catalog integration. Non-Iceberg Snowflake tables can still be queried from Databricks through query federation, which runs the query in Snowflake. [26, 24, 8]

## Moving from Snowflake to Databricks

1. **Size the estate.** Inventory and size the estate first. Databricks' Lakebridge toolkit, provided by Databricks Labs, covers assessment, SQL transpilation and reconciliation, and lists Snowflake among its source platforms.
2. **Map the catalog and policies.** Map Snowflake databases and schemas to Unity Catalog catalogs and schemas, and Snowflake roles and masking or row access policies to Unity Catalog grants, row filters, column masks or attribute-based policies.
3. **Move or federate the data.** Move data either by converting tables to Snowflake-managed Iceberg and reading them in place through catalog federation, or by copying them into Delta or Iceberg managed tables. Lakebridge converts code, it does not copy data.
4. **Convert the SQL and pipelines.** Convert SQL, stored procedures and tasks with Lakebridge's transpiler, then rebuild pipelines as Lakeflow pipelines and jobs; dynamic tables usually map to materialized views or streaming tables.
5. **Run both and reconcile.** Run both platforms side by side and use Lakebridge reconciliation to compare row counts and values before you repoint dashboards and switch off Snowflake warehouses.
6. **Keep federation to the cutover.** Plan for overlap costs. Non-Iceberg Snowflake tables reached from Databricks go through query federation, which runs on Snowflake, so keep federated reads to the cutover window.

## Questions

### Is Databricks cheaper than Snowflake?

Neither vendor publishes a like-for-like comparison, so price a representative month of your own queries and pipelines on both. On Snowflake, model the warehouse minimum carefully. Every time a warehouse starts it bills at least 60 seconds, so short queries that keep waking a suspended warehouse cost more than their run time suggests, and cloud services become billable once they pass 10% of daily warehouse usage. Databricks bills DBUs per second plus, on classic compute, your cloud provider's charges.

### Can Databricks read Snowflake data without copying it?

Yes, for Iceberg tables. Unity Catalog's Snowflake catalog federation reads Snowflake-managed Iceberg tables directly from cloud storage using Databricks compute. Ordinary Snowflake tables can be queried through query federation, which pushes the work to a Snowflake warehouse.

### Does Snowflake support open table formats now?

For Iceberg, yes. Snowflake-managed Iceberg v2 and v3 tables can be read and written by external engines such as Spark through Horizon Catalog's Iceberg REST API, which Snowflake lists as generally available. Snowflake's native tables are still only reachable through Snowflake.

### Which is better for AI and machine learning?

Databricks has the deeper toolset for training and serving your own models and agents, with MLflow, Model Serving with GPUs and serverless GPU compute. Snowflake is strong if you mainly want AI functions inside SQL, such as classification and extraction with Cortex AI Functions, without running ML infrastructure.

### Do both have a Postgres database for applications?

Both do. Snowflake Postgres has been GA since February 2026 on AWS and Azure. Databricks Lakebase is GA on AWS and Azure and in Beta on Google Cloud, with branching and scale to zero.

## Sources

1. [Understanding overall cost](https://docs.snowflake.com/en/user-guide/cost-understanding-overall), Snowflake, read 2026-10-10
2. [Snowflake pricing options](https://www.snowflake.com/en/pricing-options/), Snowflake, read 2026-10-10
3. [Supported cloud regions](https://docs.snowflake.com/en/user-guide/intro-regions), Snowflake, read 2026-10-10
4. [Snowflake Postgres](https://docs.snowflake.com/en/user-guide/snowflake-postgres/about), Snowflake, read 2026-10-10
5. [Feb 24, 2026: Snowflake Postgres (General availability)](https://docs.snowflake.com/en/release-notes/2026/other/2026-02-24-snowflake-postgres-ga), Snowflake, read 2026-10-10
6. [Snowflake Postgres: Unify Postgres and Analytics on One Platform](https://www.snowflake.com/en/blog/streamline-data-movement-snowflake-postgres/), Snowflake, read 2026-10-10
7. [About Openflow](https://docs.snowflake.com/en/user-guide/data-integration/openflow/about), Snowflake, read 2026-10-10
8. [Access Apache Iceberg tables with an external engine through Snowflake Horizon Catalog](https://docs.snowflake.com/en/user-guide/tables-iceberg-query-using-external-query-engine-snowflake-horizon), Snowflake, read 2026-10-10
9. [Nov 04, 2025: Cortex AI Functions (General availability)](https://docs.snowflake.com/release-notes/2025/other/2025-11-04-cortex-aisql-operators-ga), Snowflake, read 2026-10-10
10. [Overview of Snowflake CoWork](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-cowork), Snowflake, read 2026-10-10
11. [Nov 04, 2025: Snowflake Intelligence (General availability)](https://docs.snowflake.com/release-notes/2025/other/2025-11-04-snowflake-intelligence), Snowflake, read 2026-10-10
12. [Snowflake ML overview](https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview), Snowflake, read 2026-10-10
13. [Dynamic tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-about), Snowflake, read 2026-10-10
14. [Snowpipe Streaming overview](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview), Snowflake, read 2026-10-10
15. [Snowflake CoWork](https://www.snowflake.com/en/product/snowflake-cowork/), Snowflake, read 2026-10-10
16. [Databricks pricing](https://www.databricks.com/product/pricing), Databricks, read 2026-10-10
17. [Databricks components overview (AWS)](https://docs.databricks.com/aws/en/getting-started/overview), Databricks, read 2026-10-10
18. [Databricks Lakebase is generally available](https://www.databricks.com/blog/databricks-lakebase-generally-available), Databricks, read 2026-10-10
19. [Azure Databricks Lakebase is generally available](https://www.databricks.com/blog/azure-databricks-lakebase-generally-available), Databricks, read 2026-10-10
20. [Lakebase release notes (AWS)](https://docs.databricks.com/aws/en/release-notes/lakebase/), Databricks, read 2026-10-10
21. [Lakebase release notes (GCP)](https://docs.databricks.com/gcp/en/release-notes/lakebase/), Databricks, read 2026-10-10
22. [What is Unity Catalog?](https://docs.databricks.com/aws/en/data-governance/unity-catalog/), Databricks, read 2026-10-10
23. [What is Apache Iceberg in Databricks?](https://docs.databricks.com/aws/en/iceberg), Databricks, read 2026-10-10
24. [Access Databricks tables from Apache Iceberg clients](https://docs.databricks.com/aws/en/external-access/iceberg), Databricks, read 2026-10-10
25. [Unity Catalog and the next era of Apache Iceberg](https://www.databricks.com/blog/unity-catalog-and-next-era-apache-icebergtm), Databricks, read 2026-10-10
26. [Enable Snowflake catalog federation](https://docs.databricks.com/aws/en/query-federation/snowflake-catalog-federation), Databricks, read 2026-10-10
27. [Lakeflow Connect](https://docs.databricks.com/aws/en/ingestion/lakeflow-connect/), Databricks, read 2026-10-10
28. [Spark Declarative Pipelines](https://docs.databricks.com/aws/en/ldp/), Databricks, read 2026-10-10
29. [AI and machine learning on Databricks](https://docs.databricks.com/aws/en/machine-learning/), Databricks, read 2026-10-10
30. [Real-time mode in Structured Streaming](https://docs.databricks.com/aws/en/structured-streaming/real-time), Databricks, read 2026-10-10
31. [Migrate to Databricks](https://www.databricks.com/migration), Databricks, read 2026-10-10
32. [Lakebridge documentation](https://databrickslabs.github.io/lakebridge/), Databricks, read 2026-10-10
33. [AI/BI and Genie One release notes 2026](https://docs.databricks.com/aws/en/ai-bi/release-notes/2026), Databricks, read 2026-10-10
34. [Announcing General Availability of Real-Time Mode for Apache Spark Structured Streaming on Databricks](https://www.databricks.com/blog/announcing-general-availability-real-time-mode-apache-spark-structured-streaming-databricks), Databricks, read 2026-10-10
35. [Databricks platform release notes, May 2026](https://docs.databricks.com/aws/en/release-notes/product/2026/may), Databricks, read 2026-10-10