The cloud data warehouse has become the central nervous system of the modern data-driven organization. A well-architected data warehouse enables business intelligence teams to answer questions in seconds that previously took days, data scientists to train models on complete data rather than samples, and operational teams to build real-time dashboards on live business data. Snowflake and Amazon Redshift sit at the apex of this market — each being concretely the best answer in different contexts.
Architecture: The Critical Difference
Snowflake's Separated Storage and Compute
Snowflake's architecture fully decouples storage (stored in Amazon S3, Azure Blob Storage, or GCS depending on your cloud choice) from compute (virtual warehouses that query that storage on demand). This means: you pay for storage and compute completely independently, you can pause compute when not querying (stopping costs), and you can create multiple independent compute clusters reading the same data simultaneously — enabling workload isolation without data duplication. A critical data pipeline running ETL jobs won't contend with a BI dashboard running complex aggregations because they run on separate virtual warehouses.
Redshift's Cluster-Based Architecture
Amazon Redshift has historically used a cluster-based architecture where compute and storage are tightly coupled within provisioned node clusters. Redshift Serverless (launched 2022, significantly matured by 2024) now offers a more Snowflake-like on-demand model for workloads that don't require consistent performance. Amazon's AQUA (Advanced Query Accelerator) is a proprietary hardware layer that processes certain query types directly at the storage layer, delivering performance gains for specific patterns (particularly aggregations on large tables) that no competitor can match.
Query Performance: The Empirical Reality
- Concurrent workloads: Snowflake's workload isolation via separate virtual warehouses makes it significantly better at handling many concurrent users running complex queries simultaneously.
- Semi-structured data (JSON/Parquet): Snowflake's native VARIANT data type for semi-structured data makes working with unstructured data significantly easier than Redshift's JSON functions.
- Very large, well-structured tables: For predictable, high-volume query patterns on properly sorted Redshift tables, Redshift's AQUA hardware advantage delivers best-in-class performance at lower cost than equivalent Snowflake compute.
Pricing Model Deep Dive
Snowflake Pricing: Charges for storage ($23/TB/month) and compute credits (consumed when virtual warehouses are active). A single small virtual warehouse (XS size) consumes 1 credit/hour. Credit pricing ranges from $2–$4/credit. Importantly, Snowflake warehouses can be paused when inactive, stopping credit consumption entirely.
Redshift Pricing: Redshift Serverless charges per RPU-second (Redshift Processing Unit), with pricing around $0.375/RPU-hour. Reserved instances (1-year or 3-year commitment) can reduce provisioned cluster costs by 40–75% — making Redshift significantly cheaper than Snowflake for predictable, always-on analytical workloads with consistent high utilization.
Decision Framework
Choose Snowflake if: your workloads are bursty and variable, you have many concurrent users with mixed query patterns, you work heavily with semi-structured data, or your organization has a multi-cloud strategy. Choose Amazon Redshift if: your infrastructure is primarily AWS, your analytical workloads are consistent and predictable (enabling Reserved Instance savings), or you need tight AWS service integration with Security Hub, GuardDuty, and CloudTrail.