Data Warehouse & Analytics Engine — Head-to-Head Comparisons
10 data warehouse & analytics engine comparisons. Each page shows side-by-side pricing, plan limits, and feature differences — verified daily against vendor pages.
← Back to Data Warehouse & Analytics Engine pricing overview
How these data warehouse & analytics engine products compare
Cloud data warehouses in 2026 operate three pricing models. 'Credit/DBU-based compute' (Snowflake $2-5/credit, Databricks $0.07-0.95/DBU): pay for compute time with tier-based rate multipliers. Storage billed separately. 'Scanned data + slot capacity' (BigQuery $6.25/TiB on-demand or $0.04-0.10/slot-hour): pay per byte scanned with optional committed capacity. Storage free up to 10 GiB/month. 'Per-hour compute + storage' (Redshift $0.543/hr provisioned or $1.50/hr serverless RPU, Microsoft Fabric $263-8,408/month capacity SKUs): traditional hourly rates with reserved discounts. Snowflake remains the category leader by mindshare and enterprise adoption. Databricks dominates ML/AI workloads via Spark + Delta Lake + Unity Catalog. BigQuery is Google Cloud's serverless warehouse — zero infrastructure management. Redshift is AWS's offering, increasingly AWS-only shops default. Microsoft Fabric (launched 2023) unifies Power BI + Synapse + Data Factory + Data Engineering on one capacity reservation model. The 2024-2026 trend: open table formats (Iceberg, Delta Lake) unlock 'unified lakehouse' architecture — Snowflake added Iceberg support, Databricks Unity Catalog spans lakehouse + warehouse, Fabric built on OneLake with open formats. Committed/reserved capacity discounts range 24-50% vs pay-as-you-go. Free tiers exist (BigQuery 1 TiB query + 10 GiB storage/month, Redshift $300/90-day) but production workloads universally require paid tier.
How to choose between data warehouse & analytics engine options
First: what's your cloud? AWS-primary: Redshift (native integration, data egress savings) or Snowflake (AWS-hosted). GCP-primary: BigQuery (serverless, native GCP integration). Azure/Microsoft: Fabric or Snowflake (Azure-hosted). Multi-cloud: Snowflake (runs across AWS/Azure/GCP with same features) or Databricks. Second: workload type? Traditional BI reporting (dashboards, periodic queries): BigQuery on-demand ($6.25/TiB scanned) or Snowflake Standard. ML/data science + engineering pipelines: Databricks (Spark + notebooks + MLflow native). Real-time analytics (<10 second queries): Snowflake with warehouses sized appropriately or BigQuery BI Engine ($0.0416/GiB-hour cache). Operational analytics (embed in product): Snowflake Enterprise with read-optimized warehouses. Third: team sophistication? SQL-only analyst teams: Snowflake (strongest SQL ergonomics), BigQuery, Redshift. Data engineering + ML teams: Databricks (notebooks, Python/Scala/SQL/R, MLflow, Delta Lake). Mixed: all five support SQL + programming APIs. Fourth: scale and volume? Small (<1 TB): BigQuery on-demand free tier covers most, Snowflake X-Small warehouses are cheapest. Medium (1-100 TB): Snowflake Standard or Databricks SQL. Large (100 TB-10 PB): Snowflake Enterprise with multi-cluster, BigQuery with capacity pricing, Databricks SQL Pro/Serverless. Massive (10+ PB): Snowflake Business Critical, Databricks Enterprise, BigQuery Enterprise Plus. Fifth: compliance? All five support SOC 2 + HIPAA via enterprise tiers. Snowflake Business Critical + Databricks Enterprise + BigQuery Enterprise Plus add Tri-Secret Secure / customer-managed keys / FedRAMP. Sixth: AI workload focus? Databricks (Mosaic AI, vector search, RAG), Snowflake Cortex (SQL functions for LLMs), BigQuery ML + Gemini integration all compete here. Seventh: storage vs compute ratio? If storage-heavy + compute-light: BigQuery's $6.25/TiB scanned can be unpredictable. If compute-heavy + storage-light: Snowflake separate compute/storage billing is favorable.
All 10 head-to-head comparisons
Building Your Business Stack?
Decision-makers comparing tools often need more than one category. Here are related comparisons: