Snowflake vs Redshift: Which Cloud Data Warehouse Should You Use?

The quick answer

Snowflake and Amazon Redshift are the two dominant cloud data warehouses, and the choice between them is one of the most common platform decisions data teams make. Snowflake wins on flexibility, ecosystem, and multi-cloud portability. Redshift wins on deep AWS integration and, for certain workloads, cost. For most teams not heavily invested in AWS infrastructure, Snowflake is the stronger default choice. For teams running majority-AWS stacks with large RA3-optimised workloads, Redshift is worth serious evaluation.

Architecture overview

**Snowflake**: fully separated storage and compute architecture from the ground up. Storage is on cloud-provider object storage (S3, Azure Blob, GCS) billed per TB-month. Compute is virtual warehouses — independent clusters you spin up and down. Multiple virtual warehouses can query the same data simultaneously without resource contention. You scale compute without migrating data. You can run a dozen different warehouse sizes for different workload profiles.

**Redshift**: originally a columnar MPP (massively parallel processing) database built on ParAccel. Redshift Serverless (2022) added automatic compute scaling similar to Snowflake's model. Classic Redshift provisioned clusters (RA3 node type) separate storage from compute using Redshift Managed Storage backed by S3. The RA3 architecture narrows the storage/compute separation gap between the two platforms, but Redshift's architecture remains more coupled than Snowflake's.

**Concurrency**: Snowflake's multi-cluster warehouses automatically add compute clusters when concurrent query load is high, then scale back down. Concurrency scaling in Snowflake is near-automatic. Redshift Concurrency Scaling adds temporary compute capacity for burst loads, but requires explicit configuration and has cost implications. For environments with highly variable concurrent user loads (many BI users running dashboards), Snowflake handles this more gracefully.

Performance

Performance comparisons between Snowflake and Redshift depend heavily on workload type, data volume, and configuration. General patterns:

**Complex analytical queries (large joins, window functions, aggregations over billions of rows)**: Snowflake and Redshift RA3 are roughly comparable on well-tuned workloads. Redshift can edge ahead on specific query patterns when its distribution and sort key configuration is optimised for those patterns. Snowflake's automatic clustering and search optimisation reduce the tuning effort required to achieve good performance.

**Concurrency**: Snowflake leads. Multi-cluster warehouses absorb concurrent load cleanly. Redshift requires more explicit capacity management to handle high-concurrency workloads without queue buildup.

**Data loading**: Redshift's COPY command from S3 is extremely fast for bulk loading — Redshift is native to AWS and the COPY path is heavily optimised. Snowflake's COPY command is competitive but Redshift has the edge for very high-volume S3-originated bulk loads.

**Semi-structured data**: Snowflake leads clearly. Snowflake's VARIANT type, FLATTEN function, and native JSON querying are substantially more capable than Redshift's JSON functions. For organisations with significant JSON, Parquet, or Avro data, this matters.

Cost comparison

Cost comparisons are complicated by different pricing models, workload profiles, and discount structures. General guidance:

**Snowflake**: compute billed per credit per second (no minimum), plus storage at ~$23/TB-month. Credits cost $2–3.50/credit depending on edition and cloud/region. A medium virtual warehouse (4 credits/hour) running 4 hours/day costs ~$25–50/day. Snowflake's pricing is elastic and transparent but can escalate with heavy compute use. Business Critical edition (required for HIPAA and some compliance scenarios) is more expensive.

**Redshift RA3**: RA3.xlplus node ~$1.086/node/hour on-demand; RA3.4xlarge ~$3.26/node/hour; RA3.16xlarge ~$13.04/node/hour. A 2-node RA3.xlplus cluster runs ~$1,560/month on-demand; 1-year reserved pricing reduces this by ~40%. Storage is separate at ~$0.024/GB-month for Redshift Managed Storage. For steady-state workloads with predictable utilisation, Redshift reserved pricing is often cheaper than Snowflake.

**Redshift Serverless**: billed per RPU-second (Redshift Processing Units), starting at 8 RPUs. Pricing ~$0.375/RPU-hour. For intermittent analytical workloads with unpredictable timing, Serverless can be cost-effective; for heavy continuous workloads, provisioned RA3 with reserved pricing is typically cheaper.

**Cost model fit**: Snowflake's per-second elastic model favours variable workloads. Redshift reserved instances favour steady, predictable workloads. Teams with spiky, variable compute demand often find Snowflake cheaper in practice; teams with predictable warehouse utilisation often find Redshift cheaper on paper.

Ecosystem and integrations

**dbt**: both platforms have first-class dbt support. dbt-snowflake and dbt-redshift are both mature connectors.

**Data ingestion**: Fivetran and Airbyte support both platforms equally well. Redshift has a natural advantage for teams already using native AWS services — S3, Kinesis, Glue, DMS — for ingestion, as these integrate directly without additional tooling.

**BI tools**: Tableau, Power BI, Looker, and all major BI tools support both. Snowflake's native Tableau connector and Snowpark integration are strong. Redshift's native QuickSight integration is strong if you are in the AWS BI ecosystem.

**AWS-native services**: Redshift integrates natively with Lake Formation, Glue Data Catalog, SageMaker, Kinesis, and other AWS services. If your data platform is predominantly AWS-native, Redshift's ecosystem cohesion is a material advantage. Snowflake provides connectors to these services but as a third-party integration rather than a native one.

**Snowpark**: Snowflake's framework for running Python, Java, and Scala code within Snowflake — useful for ML feature engineering, Python-based transformations, and running data science workloads without moving data out of the warehouse. Redshift has no direct equivalent.

**Multi-cloud**: Snowflake runs on AWS, Azure, and GCP and supports cross-cloud data sharing. Redshift is AWS-only. For multi-cloud or cloud-agnostic strategies, this is a significant differentiator.

Data sharing

Snowflake's Secure Data Sharing is a distinct capability — sharing live data between Snowflake accounts without copying it, across organisations and cloud regions. This enables data products, data marketplaces, and cross-organisation analytics that are difficult to replicate in Redshift. Redshift Data Sharing exists for sharing between Redshift clusters within AWS but is more limited.

When to choose Snowflake

Snowflake is the better default for: teams not locked into AWS infrastructure; environments with variable or spiky workload profiles; organisations with significant semi-structured data; multi-cloud strategies; teams that want a strong ecosystem and minimal infrastructure management; organisations that want data sharing or marketplace capabilities.

When to choose Redshift

Redshift is the better choice when: you are deeply invested in AWS services (S3, Glue, Lake Formation, Kinesis) and want native ecosystem cohesion; your workloads are steady-state and predictable, making reserved instance pricing attractive; cost is the primary constraint and Redshift reserved pricing comes out ahead for your specific workload; or you are already running Redshift and the migration cost to Snowflake outweighs the benefits.

Migration considerations

Migrating from Redshift to Snowflake (or vice versa) involves SQL dialect differences (Redshift SQL vs Snowflake SQL have meaningful divergences in window functions, semi-structured data handling, and some function syntax), schema migration, data migration, connector reconfiguration, and performance re-tuning. Budget 3–6 months for a production migration with parallel running.

For the transformation layer that runs on top of either platform, see what is dbt and dbt best practices. For the broader architecture decision, see data architecture patterns and snowflake architecture guide.

Our data architecture consulting practice evaluates and implements cloud data warehouse platforms — from initial selection through migration and optimisation. If you are choosing between Snowflake and Redshift or optimising an existing deployment, book a free 30-minute audit.

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