Redshift and Snowflake are the two most commonly evaluated cloud data warehouses for mid-market enterprise analytics. Both are mature, capable platforms — but they make different architectural trade-offs that matter depending on your workload, team, and cloud strategy. This guide gives you the honest comparison.
Redshift and Snowflake are both mature, capable cloud data warehouses that can handle the analytical workloads of most mid-market enterprises. The choice between them is not a question of which is better in the abstract — it is a question of which is better for your specific workload, cloud strategy, and team. The right answer depends on factors that are largely organisation-specific.
The Core Architectural Difference
Redshift is a managed distributed database where you provision a cluster of nodes and pay for that cluster continuously. The cluster has a defined node type (dc2.large, ra3.xlplus, etc.) and a defined number of nodes. You control scaling by adding or removing nodes.
Snowflake separates storage and compute entirely. Storage is charged by the byte stored on S3 (Snowflake's internal storage layer). Compute is charged by the second while a virtual warehouse (Snowflake's compute cluster) is running. Virtual warehouses can be started, stopped, paused, and resized dynamically. You can have multiple virtual warehouses of different sizes running simultaneously against the same data — separating compute for different workloads without data duplication.
This architectural difference drives most of the practical trade-offs.
Concurrency and Workload Isolation
**Snowflake** handles concurrency through virtual warehouse isolation. Give the data engineering team their own virtual warehouse. Give the business users another. Give the executive dashboards a third. Each workload gets dedicated compute; none competes with the others. Scaling one workload means resizing or adding that team's virtual warehouse — it does not affect anyone else.
This is Snowflake's strongest architectural advantage for mixed-workload environments. An ETL job that runs an expensive transformation does not slow down a dashboard load. A burst of business user queries at 9 AM does not cause the night extract to fail.
**Redshift** in its standard deployment shares compute across all workloads. Workload Management (WLM) provides queue-based priority management, but workloads still share the same physical nodes. Ra3 instances with Concurrency Scaling can provision temporary additional capacity for burst query demand, but this is a reactive mechanism rather than proactive workload isolation.
For organisations with clear workload separation requirements — executive dashboards that must be fast during business hours regardless of what the data team is doing — Snowflake's architecture is a meaningful advantage.
Cost Model Comparison
**Redshift** on ra3 nodes charges per node-hour plus separate S3 charges for storage (since ra3 decouples storage from compute). You pay for the cluster continuously, whether it is running queries or idle. For predictable, continuous workloads with high utilisation, this produces a lower per-query cost than Snowflake. Reserved instances (1-year or 3-year) reduce costs significantly for steady-state clusters.
**Snowflake** charges per second of virtual warehouse runtime plus storage. Virtual warehouses that are suspended consume no compute credits. For workloads that are bursty or irregular — heavy weekday usage with minimal weekend activity — Snowflake's consumption model can produce significantly lower total costs than a continuously-running Redshift cluster. For steady-state 24/7 workloads with high utilisation, Snowflake can be more expensive.
The cost comparison requires modelling your actual workload pattern. Organisations that run analytical jobs continuously with high cluster utilisation tend to find Redshift more cost-competitive. Organisations with bursty, variable workloads or intermittent usage patterns (multiple teams with different schedules) tend to find Snowflake more cost-competitive.
AWS Integration
Redshift is an AWS service with native integration advantages that Snowflake can partially but not fully replicate:
**Redshift Spectrum**: Query data directly in S3 without loading it into Redshift, using the same SQL interface. Native S3 integration with no data movement cost for Spectrum queries.
**AWS Glue integration**: Redshift connects natively with the AWS Glue Data Catalog for schema management across services. Data pipelines from Lambda, Kinesis, or DMS land directly in Redshift.
**Security and IAM**: Redshift uses AWS IAM roles natively for authentication and access management. If your organisation already has mature IAM governance, Redshift integrates into existing controls without a separate access management layer.
**VPC-native deployment**: Redshift runs inside your VPC — not as a SaaS service hitting your VPC. Network access controls apply directly to the cluster.
Snowflake runs as a SaaS service with AWS as the underlying cloud provider. It supports VPC peering and AWS PrivateLink for private connectivity, but the service itself is Snowflake-managed, not AWS-managed. If your organisation has a strong AWS-native preference — all infrastructure managed through AWS accounts and IAM, all compliance posture based on AWS certifications — Redshift is the more natural fit.
SQL Dialect and Ecosystem Compatibility
Redshift's SQL dialect is based on PostgreSQL 8.0.2 with significant modifications. It looks like PostgreSQL but has important differences: not all PostgreSQL extensions are available, some functions behave differently, and some PostgreSQL queries require modification to run on Redshift. Teams migrating from PostgreSQL will find Redshift familiar but will encounter edge cases.
Snowflake supports both ANSI SQL and a Snowflake-specific dialect. It has strong compatibility with Oracle, SQL Server, and Redshift SQL through conversion tooling. Snowflake's STRING_AGG, PIVOT, UNPIVOT, and array handling are generally considered cleaner than Redshift equivalents.
For dbt, both warehouses are first-class supported adapters. dbt Core works well with both; the model of writing SQL models and materialising them as tables or views translates identically to either platform.
When Redshift Is the Better Choice
- Deep AWS integration is a priority (all infrastructure is AWS-native, IAM governance is mature)
- Workload is predictable and continuous — clusters run at high utilisation with steady throughput
- Budget model favours reserved capacity commitments over consumption billing
- Team has PostgreSQL SQL background and values dialect familiarity
- Data volumes are large and already reside in S3, with Spectrum query patterns
When Snowflake Is the Better Choice
- Multiple teams or workloads with isolation requirements (finance, data engineering, business users each need dedicated compute)
- Bursty or irregular workload patterns — intermittent usage, scheduled batch with idle periods
- Multi-cloud strategy or preference not to be exclusively AWS-native
- Time Travel and zero-copy cloning features would be used actively (development/test environment management, point-in-time recovery)
- Data sharing with external parties is required (Snowflake's data sharing model is significantly stronger)
The Microsoft Factor
If your organisation is Azure-native rather than AWS-native, this comparison mostly does not apply — Microsoft Fabric or Azure Synapse is the likely choice, not Redshift or Snowflake. Both Snowflake and Redshift have Azure deployment options, but neither integrates as naturally into an Azure-native stack as Microsoft's own platforms.
Our cloud engineering and data architecture practice guides organisations through warehouse selection with a workload-specific evaluation — contact us to discuss which platform is the right fit for your environment.
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