Cloud data warehouses · Snowflake Inc.

Snowflake

Cloud-native data warehouse that separates storage and compute and bills usage in per-second credits.

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Snowflake is a fully managed, cloud-native data warehouse available on AWS, Azure, and Google Cloud. It separates storage from compute, so independent virtual warehouses can query the same shared data without contending for resources, and warehouses can be resized, suspended, or run concurrently to isolate workloads. Snowpark extends the platform to Python, Java, and Scala for data engineering and ML pipelines, and native data sharing lets customers exchange live data across accounts without copying it. Snowflake runs as a hosted service only; there is no self-hosted option. It competes most directly with BigQuery, Redshift, and Databricks SQL as a general-purpose cloud warehouse for BI, ELT, and increasingly AI/ML workloads.

At a glance

Vendor Snowflake Inc.
Pricing model Usage-based
Free tier No
Deployment Cloud
Open source No
Best for Teams wanting a fully managed, multi-cloud SQL warehouse with strong concurrency isolation and native data sharing.

Pricing

Billed in per-second Snowflake credits whose price depends on edition (Standard, Enterprise, Business Critical, VPS), plus separate storage charges; a free trial is offered but there is no ongoing free tier.

Plan Price Notes
Standard $2.00 per credit AWS, US East (Northern Virginia); entry-level edition with core platform functionality
Enterprise $3.00 per credit AWS, US East (Northern Virginia); adds multi-cluster compute, extended Time Travel, governance controls
Business Critical $4.00 per credit AWS, US East (Northern Virginia); adds Tri-Secret Secure, private connectivity, failover/failback

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Storage-compute separation with independently scalable virtual warehouses
  • Multi-cluster warehouses for concurrency scaling
  • Snowpark for Python, Java, and Scala pipelines
  • Native cross-account and cross-cloud data sharing
  • Time Travel and Fail-safe for data recovery
  • Automatic query optimization and result caching
  • Semi-structured data support (JSON, Avro, Parquet) via VARIANT
  • Support for external tables over cloud object storage

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Google BigQuery Serverless cloud data warehouse billed by data scanned or by slot capacity, with no infrastructure to provision. Amazon Redshift AWS's managed cloud data warehouse, available as provisioned clusters or an auto-scaling serverless option. Databricks Managed lakehouse platform, built by Apache Spark's creators, combining Spark-based compute with governance and ML tooling.

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Terms to know

Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Query optimization The process, largely automated by the database, of choosing the fastest execution plan for a given query.

Related guides

How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a cloud data warehouse Start from your cloud, your pricing tolerance and your workload shape — general BI, high-concurrency apps, or enterprise mixed workloads — not the feature list. How to reduce data warehouse costs Where usage-based warehouse spend actually goes, and the levers — query, storage, scheduling — that reliably bring it down.