Rates last reviewed: July 2026.

Snowflake vs Redshift Pricing

Comparing Snowflake and Redshift is harder than it looks. Snowflake uses credits consumed by virtual warehouses. Redshift has two completely different pricing models — provisioned clusters (pay by the node per hour) and Serverless (pay by RPU-hour). The math is not apples-to-apples, and that is the point: the right choice depends on your workload shape, not just the list price.

Pricing model breakdown

Before you compare numbers, you have to understand the billing unit each platform uses. They do not map neatly onto each other.

Snowflake pricing: credits, warehouses, and storage

Snowflake charges you for compute in credits. You pick a warehouse size (XS, S, M, L, XL…), and that warehouse burns a fixed number of credits per hour while it runs. You pay for storage separately at about $23/TB-month for compressed data. When the warehouse is suspended, you pay zero compute. That per-second billing after the first minute means a query that takes 3 seconds costs you 3 seconds of compute.

On top of warehouse compute, Snowflake adds a cloud services layer — roughly 10% of your warehouse credit usage — for metadata, query planning, and caching. It shows up on your bill whether you watch it or not.

Redshift pricing: two models, one service

Redshift is really two products with the same name. The pricing is completely different depending on which you choose.

Provisioned clusters

This is the original Redshift. You pick a node type (dc2.large at $0.25/hr, ra3.xlplus at $0.83/hr, etc.) and a node count. You pay for every hour the cluster is running, even if nobody is querying it. There is no auto-suspend. A dc2.large cluster with 2 nodes costs $0.50/hr whether you run one query or a thousand. Storage is local SSD on dc2 nodes or managed S3-backed storage on RA3 nodes at $0.024/GB-month.

Provisioned clusters work well when you need predictable performance and know your peak hours. They punish you for idle time.

Redshift Serverless

Redshift Serverless is the answer to "why would I pay for idle nodes?" You pay per RPU-hour (Redshift Processing Unit) — base rate of ~$0.25/RPU-hour in us-east-1 — and compute suspends after a configurable idle period, same as Snowflake. The base compute capacity is 8 RPUs, and it auto-scales up to handle concurrency.

Serverless makes Redshift competitive on bursty workloads in a way provisioned clusters never were. But the RPU pricing is slightly higher per unit than a well-utilized provisioned node. You pay for the flexibility.

Cost comparison: real workload sizes

Here is what typical bills look like across three workload sizes. These are estimates using standard rates — your actual bill depends on region, compression ratios, concurrency, and a dozen other variables. Use the calculator on this site to get a number for your specific workload.

The provisioned cluster wins on pure cost at steady state. The Serverless model sits between Snowflake and provisioned — cheaper than Snowflake at medium-to-large scale, but not by as much as the provisioned gap would suggest. At small scale, all three are close enough that pricing should not be your deciding factor.

Where Snowflake wins

• Bursty and unpredictable workloads. If your team queries heavily some days and not at all others, Snowflake's per-second billing on auto-suspending warehouses means you pay nothing when nobody is using it. Provisioned Redshift charges you for every hour the cluster is alive. Serverless Redshift closes this gap, but Snowflake still has slightly more mature auto-scaling and multi-cluster warehouse support.
• Multi-cloud. Snowflake runs on AWS, Azure, and GCP. Redshift is AWS-only. If your company wants cloud-agnostic architecture, or if you are already on Azure or GCP, Snowflake is the obvious choice.
• Data sharing. Snowflake's data sharing between accounts is seamless and does not require copying data. Redshift has data sharing too (since RA3), but it is less mature and still AWS-region-bound in most cases.

Where Redshift wins

• Steady, predictable workloads. If you run the same number of queries every day on a fixed schedule, provisioned Redshift is almost always cheaper. You are paying for capacity, not consumption, and the per-node pricing is aggressive — especially on reserved instances (1-year or 3-year commitments can cut the bill by 30-50%).
• AWS ecosystem integration. If the rest of your stack is on AWS — S3, Glue, Athena, QuickSight, Kinesis — Redshift integrates natively. Spectrum queries against S3 without moving data. Zero-ETL integrations with Aurora, RDS, and DynamoDB mean data lands in Redshift without pipelines. Snowflake can sit on AWS too, but the integration is not as tight.
• Redshift Serverless is genuinely competitive now. Early Redshift Serverless was priced high enough that you had to squint to see the value. The current pricing (around $0.25/RPU-hour) and improved auto-scaling make it a real alternative to Snowflake for teams that want the AWS integration without the node-management headache.

Hidden costs people miss

The list price is not the full story. Here is what adds up on both sides:

Redshift

• Data transfer out of AWS. If your BI tools or users are outside AWS, egress charges apply. Snowflake is also subject to cloud egress, but if you are already on AWS, Redshift-to-AWS services is free.
• Redshift Spectrum. Querying data in S3 via Spectrum costs $5/TB scanned on top of your cluster or Serverless compute. These costs add up fast if you use S3 as a data lake layer.
• Concurrency scaling. Provisioned clusters pay extra for concurrency scaling. It kicks in automatically when your workload exceeds the base cluster capacity, and the additional compute is billed per-hour at the same node rate.
• RA3 managed storage. $0.024/GB-month sounds cheap, but a 100 TB data warehouse adds $2,400/month just for storage compute (the "managed" part of managed storage).

Snowflake

• Cloud services layer. If your cloud services compute exceeds 10% of your warehouse compute, Snowflake bills the excess. On complex workloads with heavy metadata operations, this can add 5-15% to your bill.
• Auto-scaling warehouse costs. Multi-cluster warehouses automatically spin up additional warehouses to handle concurrency. The credits burn faster than you expect.
• Snowpipe. Continuous ingestion via Snowpipe is billed per row, and high-volume ingestion adds to your compute costs outside the main warehouse.

When provisioned Redshift makes sense

You should consider provisioned Redshift if you have a steady 8-12 hour daily workload, the underlying AWS reservation model fits your budget cycle, and your team can tolerate the node management overhead (resizing, vacuuming, distribution keys). The cost advantage at medium-to-large scale is real.

When Serverless tips the scale

If you are comparing Snowflake to Redshift, you are most likely comparing Snowflake to Redshift Serverless — because the provisioned model is a fundamentally different tradeoff (pay-for-capacity vs pay-for-consumption). Serverless makes sense when you want AWS-native integration but do not want to babysit nodes. The RPU pricing is close enough to Snowflake credits that the decision should be based on your cloud ecosystem and feature needs, not a 5% price difference.

Use the calculator to compare your workload

The numbers in this comparison are directional. Your actual bill depends on your exact workload: query complexity, concurrency, compression, data freshness, region, and negotiated discounts. The calculator on this site lets you model your workload across all four platforms (including Redshift Serverless) with adjustable rate assumptions.

Compare Snowflake vs Redshift with your actual workload