Redshift offers lower costs for predictable, MPP workloads on AWS infrastructure, while Snowflake provides superior multi-cloud flexibility, ease of use, and better performance for ad-hoc queries with its unique architecture. Snowflake dominates in ease of deployment and cross-cloud capability, while Redshift wins on price for consistent, large-scale analytics.
Redshift vs Snowflake — Side-by-side comparison on A Versus B
Choose Redshift if you have predictable, massive analytics workloads on AWS and need to minimize cost per query—it excels at batch processing and can save 30-40% versus Snowflake for consistent usage. Choose Snowflake if you need multi-cloud deployment, rapid time-to-value, high concurrency, or mixed workload patterns (batch + ad-hoc); it's ideal for enterprises seeking flexibility and reduced DevOps overhead despite higher costs.
AWS-native MPP data warehouse optimized for large-scale batch analytics and predictable workloads.
Enterprise cloud data warehouse with native multi-cloud support and broad BI ecosystem integration.
Structured comparison data for Redshift vs Snowflake with 160 attributes.
Query Latency (1B row scan, 10 column aggregate)
Annual TCO (100TB storage, average usage)
Average Query Latency (1 Billion Row Scan)
Maximum Concurrent Queries (Standard Node)
Starting Monthly Cost (10GB active data)
SQL Query Performance (TPC-DS Benchmark)
Maximum Concurrent Queries Per Warehouse
Query Performance (10TB TPC-DS benchmark)
Redshift vs Snowflake — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Redshift vs Snowflake.
Which is cheaper: Redshift or Snowflake?
Redshift is 25-40% cheaper for predictable, sustained workloads. For 1PB annual throughput, expect $180K-$240K (Redshift) vs $280K-$360K (Snowflake). However, Snowflake's elastic scaling can save money on variable/bursty workloads by avoiding over-provisioning. Redshift requires upfront cluster investment; Snowflake pays as you scale.
Can I use Snowflake if I'm committed to AWS only?
Yes, Snowflake runs on AWS and provides the same functionality. However, you lose the multi-cloud arbitrage advantage. If you're AWS-locked and have predictable workloads, Redshift is typically the better financial choice due to lower costs and tighter integration (VPC, IAM, S3).
How long does it take to get each system production-ready?
Snowflake: 2-3 days (create account, load data, run queries). Redshift: 3-4 weeks (provision cluster, configure security, optimize schema, tune performance). Snowflake's self-tuning architecture eliminates manual tuning; Redshift requires data engineers to design sort keys, distribution keys, and manage compression.
Which is better for real-time dashboards and ad-hoc queries?
Snowflake wins decisively. Its separated compute/storage with auto-scaling delivers sub-5-second responses. Redshift's pre-sized clusters introduce latency (2-4 minutes typical) and require scaling before handling ad-hoc surges. Snowflake handles 500-1000 concurrent users; Redshift handles 50-100 per cluster.
What about data sharing between teams/companies?
Snowflake has native zero-copy data sharing built-in—accounts can share databases without copies. Redshift requires manual replication, snapshots, or data export. For multi-tenant or partner data ecosystems, Snowflake's sharing feature saves weeks of engineering and eliminates sync issues.