Data warehouse choice in 2026 hinges on three questions:where does your data come from?(cloud determines stack),what's the workload?(analytics vs ML vs streaming),who runs it?(managed vs self-hosted).
Quick Picks
- AWS-native→RedshiftorSnowflake
- Google Cloud-native→BigQuery
- Multi-cloud / vendor-neutral→Snowflake
- ML / AI-heavy workloads→Databricks
- Small data / fast SQL→ClickHouseor MotherDuck
Pricing (May 2026, 1TB scanned/mo)
| Warehouse | Storage | Compute (1TB scan) |
|---|---|---|
| Snowflake | $23/TB/mo | $2-4 per credit |
| BigQuery | $20/TB/mo (active) | $5-7.50 per TB scanned |
| Databricks | S3 backing (~$23) | DBU-based ($0.40-1.00/DBU) |
| Redshift | $24/TB/mo | $0.25/RPU/hour (Serverless) |
| ClickHouse Cloud | $0.0235/GB | From $0.43/hour |
What Each Wins At
Snowflake — The Multi-Cloud Default
Snowflake runs on AWS, Azure, and GCP — choose your cloud. Separation of compute and storage is mature. Cortex (AI) integrates LLMs directly in SQL. Best for: orgs wanting cloud independence; complex SQL workloads.
BigQuery — Google Cloud Native
BigQuery is serverless from day one — no clusters to manage. Pay-per-query model. Native Gemini integration for natural language queries. Best for: GCP-native orgs, ad-hoc analytics, ad-tech.
Databricks — Lakehouse + ML
Databricks is the lakehouse pioneer. Strong fit for ML and streaming workloads. Mosaic AI integrates training pipelines with the warehouse. Best for: ML teams, data science, complex transformations.
Redshift — AWS Default
Redshift is AWS-native — deep integration with S3, IAM, Glue. Serverless 2.0 (2026) finally simplifies management. Best for: AWS-heavy orgs, mature analytics teams.
ClickHouse / MotherDuck — Small Data Champions
For sub-TB workloads, ClickHouse and MotherDuck (DuckDB cloud) outperform on cost and speed. Best for: startups, sub-100GB analytics, embedded analytics.