We've helped 11 teams pick between Redshift and Snowflake, and migrated 4 in each direction. The right answer depends on 5 specific signals — not what your AWS rep tells you.
Lakshmi Kiranmai Guduru
Founder, CARTIEAI
A note on this story: This post draws on 11 Redshift/Snowflake selection engagements (2024-2026) plus 4 production migrations (2 Redshift→Snowflake, 2 Snowflake→Redshift). Numbers are composites; patterns are real.
A VP of Data sent us a message we hear monthly:
"Our AWS rep keeps pushing Redshift. Our Snowflake rep keeps pushing… Snowflake. We're stuck. Help."
Both vendors are great. Both have niches where they're cheaper by 30-50%. The default "Snowflake is more expensive but flexible / Redshift is cheaper but rigid" framing is a decade out of date. Redshift Serverless changed the math. Snowflake Iceberg tables changed the math again. Picking on price alone in 2026 will burn you.
Here's the actual framework we use with customers.
| Signal | Lean Redshift | Lean Snowflake |
|---|---|---|
| Workload pattern | Steady, predictable, 24/7 | Spiky, ad-hoc, multi-team |
| Data sources | Mostly AWS-native (S3, RDS, DMS) | Multi-cloud or external (Azure, GCS, Salesforce) |
| Concurrency need | <50 concurrent users on a single workload | >100 concurrent users / many independent teams |
| Skill in-house | Strong AWS-native data team (DMS, Glue, Kinesis) | Strong SQL, weaker AWS DevOps |
| Lock-in tolerance | Already AWS-locked, comfortable | Want option to move clouds |
If 4 out of 5 lean one way, take that one. If 3-2, dig deeper into the next layer (cost modeling).
Forget rate cards. The real cost equation looks like this:
Total cost = compute + storage + data transfer + dev time
For each platform on a typical mid-size analytics workload (10 TB data, 30 ETL jobs/day, 50 BI users, 200 ad-hoc queries/day):
Surprise #1: Snowflake is 40-60% cheaper for this profile. Redshift's "cheap" reputation comes from the per-hour compute rate, but Managed Storage at $240/TB-month is brutal vs Snowflake's $23. For data-heavy workloads, Redshift loses badly.
Surprise #2: This flips at very high steady utilization. If you actually run 24/7 high-concurrency OLAP, Redshift Provisioned with reserved instances (3-year RIs at -50%) becomes cheaper than Snowflake credits. Threshold: ~$25K/month spend, sustained.
1. You're already deep in the AWS data toolchain. DMS for CDC, Glue for ETL, Kinesis Firehose dumping into S3 → Redshift is half a day to wire. Snowflake adds a Snowpipe layer and you're managing two tools instead of one.
2. Your queries are predictable and steady. A daily reporting workload that runs from 6am-2pm? Redshift Serverless RPUs scale linearly and predictably. Snowflake's auto-scaling is more elastic but harder to budget.
3. Tight integration with AWS-only services. Redshift Spectrum lets you query S3 directly without ingestion. Federated queries hit RDS Postgres without copying data. Lake Formation enforces row-level security across Redshift + Athena + EMR. Snowflake can't match this AWS-native depth.
4. You're worried about data egress costs. If 90% of your queries terminate in QuickSight, Tableau-on-AWS, or another AWS service: Redshift = $0 egress. Snowflake on AWS = $0.09/GB egress to non-AWS destinations.
5. Your security team requires VPC-only data planes. Redshift Serverless runs in your VPC. Snowflake runs in their account, with PrivateLink as the bridge. For some compliance regimes (FedRAMP, certain healthcare) the all-VPC model is mandatory.
1. Multi-team, multi-workload, lots of concurrency. Snowflake's multi-cluster warehouses isolate workloads beautifully. Marketing's huge query won't slow Finance. Redshift WLM can do this but requires constant tuning.
2. Schema evolution is constant.
Snowflake's VARIANT type + auto-schema-on-write makes JSON ingestion painless. Redshift requires SUPER columns or pre-flattening.
3. You need to share data externally. Snowflake Data Sharing lets you share live tables with partners — no ETL, no S3 dumps. Redshift Data Sharing exists but only across Redshift accounts, not externally.
4. Multi-cloud is in the strategy. Snowflake on AWS, GCP, and Azure. Same SQL, same UI, replication across clouds. Redshift = AWS-only, full stop.
5. Your team is SQL-strong but DevOps-weak. Snowflake "just works" out of the box. No vacuum jobs. No analyze schedules. No sortkey design. For teams without dedicated infra engineers, this is enormous.
Customer: Series C SaaS, Redshift dc2.8xl × 4 cluster, $42K/month, BI users complaining about contention.
What we found: 60% of compute went to background VACUUM and ANALYZE. WLM was set up by the previous data team and nobody understood it.
Outcome: Snowflake on a Medium warehouse, auto-suspend after 60s. $18K/month, -57%. BI latency p95 dropped from 12s → 3s.
Migration cost: ~6 weeks, 1 senior data engineer, $50K consulting. Payback in 3 months.
Customer: Profitable bootstrap, $9K/month Snowflake, but 80% of spend was 3 BI dashboards refreshing every 15 min.
What we found: Workload was 100% steady. Auto-suspend never triggered. Snowflake's elastic pricing was the wrong model for this workload.
Outcome: Redshift dc2.large × 2 with 1-year RIs. $3,200/month, -64%.
Migration cost: ~3 weeks, in-house team. No consulting needed.
Customer: Series B, escaping a 6-node Postgres data warehouse. 4 TB.
Outcome: Snowflake X-Small, ~$1,800/month. Came in under their old Postgres EC2 + EBS bill.
Customer: All analytics on Athena over S3. Costs unpredictable, big queries timing out.
Outcome: Redshift Serverless 64 RPU baseline. Stable cost ~$5K/month, 5× faster on aggregations.
Use this in 30 seconds:
CARTIE AI's Snowflake and Redshift cost analyzers connect to both warehouses, model the migration cost across 12 months, and give you a recommendation grounded in your real workload — not the rate card. Most analyses surface 20-40% savings opportunity without migrating at all (right-sizing the existing warehouse).
Even without a tool, the 5-signal framework above gives you a defensible answer to your AWS rep. Pin it.
Now go check your Managed Storage line on your Redshift bill. 🥃
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ABOUT THE AUTHOR
Founder, CARTIEAI · Building in public
I'm building CARTIE AI to fix the cloud-cost problem I saw drain millions at companies I worked for — where engineering and finance kept talking past each other. If you liked this post, here's where I share unfiltered notes on building this in public: