AWS Redshift costs $15K–$120K+ annually and is slow compared to modern alternatives. Compare 6 cheaper data warehouses with 2-10x better performance.
AWS Redshift was the dominant cloud data warehouse 10 years ago. But today, it's expensive, slow, and harder to use than competitors.
Why companies switch away:
100-person data team: $15K–$50K/year per data warehouse
1,000-person enterprise: $80K–$120K+/year
• Data startup: Redshift $45K → BigQuery $12K/year (-73%)
• Enterprise SaaS: Redshift $78K → Snowflake $25K + negotiation (-68%)
• Fintech: Redshift $95K → Databricks $18K + savings (-81%)
| Data Warehouse | Query Speed | Annual Cost (100-person team) | Setup Complexity | Best For |
|---|---|---|---|---|
| Redshift | Slow (baseline) | $15K–$50K | Very high | Legacy analytics |
| BigQuery BEST PRICE | 10x faster | $8K–$18K | Low | Cost-conscious teams |
| Snowflake BEST UX | 8x faster | $18K–$35K | Low | Multi-cloud data teams |
| Databricks BEST FOR ML | 10x faster | $22K–$45K | Medium | ML/AI teams |
| DuckDB CHEAPEST | 10x faster | $0–$2K | Low | On-premise analytics |
| Postgres + TimescaleDB | 5x faster | $5K–$12K | Medium | Time-series analytics |
| Presto (Open-source) | 6x faster | $3K–$8K | High | Multi-source queries |
When to use: You want the cheapest option with the fastest queries. Best for cost-conscious data teams that are all-in on Google Cloud.
Cost model: Pay-per-TB scanned (~$7/TB) — incentivizes efficient queries. No storage costs until you exceed 1GB free tier.
Savings vs. Redshift: $12K–$25K/year (60–75% less)
Use code samples:
SELECT COUNT(*) FROM `project.dataset.table` LIMIT 1 (no clustering cost, instant results)
When to use: You need on-demand scaling, separate compute + storage pricing, and flexibility to run on AWS/Azure/GCP.
Cost model: $2–4/credit (compute hour). 1 TB table scan = ~8 credits = $16–32. Storage ($23/TB) separate.
Savings vs. Redshift: $10K–$18K/year (35–60% less)
Negotiation tactic: Annual commitment = 15–25% discount. Snowflake offers per-org pricing.
When to use: Your team uses Spark/ML pipelines. Native Delta Lake format is cheaper than converting Redshift data.
Cost model: $0.30–0.80/DBU (compute unit) + $0.02–0.04/GB storage. Job clusters cost 80% less than all-purpose.
Savings vs. Redshift: $15K–$35K/year (30–70% less)
Quick optimization: Use job clusters for scheduled workloads (80% cheaper than interactive clusters)
When to use: Analytics runs on-premise or in your app. DuckDB runs in-process (0 DevOps). Free and open-source.
Cost model: $0 (self-hosted). Only pay for cloud storage if using S3/GCS for data files.
Savings vs. Redshift: $15K–$50K/year (100% for many use cases)
Limitation: Single-machine concurrency (not for 100+ simultaneous users). Better for internal dashboards.
When to use: You're storing metrics, time-series data, or financial transactions. TimescaleDB runs on Postgres.
Cost model: $0 open-source (self-hosted) or $500–2K/month managed (Timescale Cloud).
Savings vs. Redshift: $10K–$35K/year self-hosted, $5K–$15K/year managed
Real use case: Financial services replaced Redshift + InfluxDB with TimescaleDB, saved $28K/year
When to use: You query data across Postgres, Hive, Elasticsearch, JDBC sources simultaneously. Redshift cannot do this natively.
Cost model: Open-source (free) + hosting/support costs ($3K–8K/year managed).
Savings vs. Redshift: $8K–$20K/year
Limitation: Higher administration overhead. Requires data engineering team support.
Situation: 15-person data team using Redshift for customer analytics. Queries were slow (5–30 minute wait times). Team spent 30% of time on Redshift administration.
Problem: Redshift costs $45K/year. Query latency hurting product speed.
Solution: Migrated to BigQuery over 4 weeks. Kept same schemas (Redshift and BigQuery SQL are similar). Set up 1 person on BigQuery governance.
Results:
Situation: 30-person data team split across analytics + ML pipelines. Using Redshift for analytics, Spark for ML (dual infrastructure).
Problem: Redshift $78K/year + Spark cluster management = $120K/year total. Team wanted to consolidate.
Solution: Evaluated Snowflake vs. Databricks. Chose Databricks for native ML support. Consolidated both workloads (analytics queries + ML feature engineering).
Results:
Situation: 20-person analytics team using Redshift for transaction analytics. Needed HIPAA + SOC2 compliance.
Problem: Redshift didn't have audit logging. Had to build custom compliance layer ($30K setup). Redshift costs $95K/year + $30K compliance = $125K total.
Solution: Migrated to Snowflake (native HIPAA + SOC2). Snowflake handles audit logs out-of-box (saved $30K compliance costs).
Results:
SELECT COUNT(*), SUM(bytes) FROM pg_tables WHERE schema_name != 'pg_' to understand data volume, table count, and query frequencyPricePulse tracks pricing for 90+ SaaS tools including data warehouses. See your team's annual spend, compare alternatives, and find negotiation opportunities.
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