AWS vs Snowflake Data Warehouse Cost: The Buyer-Side Comparison
Redshift bills compute by the cluster-hour; Snowflake bills by the credit. The two pricing models are hard to compare line-for-line — and that opacity is exactly where buyers overpay. Here is the apples-to-apples framework.
The Redshift-versus-Snowflake question lands on most enterprise data-platform budgets eventually, and it is one of the hardest comparisons in cloud cost because the two vendors do not price the same thing the same way. Redshift sells provisioned clusters or Serverless capacity inside AWS; Snowflake sells abstract "credits" consumed by virtual warehouses, also running on AWS infrastructure underneath. The unit mismatch is not an accident — opacity protects margin, and it is where buyers lose money.
Across $2.4B+ in reviewed AWS spend and 500+ engagements, we see the same outcome repeatedly: teams pick a warehouse on a feature demo, then discover the cost model only after the bill arrives. The buyer-side approach reverses that order — model the cost on your actual workload shape first, then negotiate both vendors with the model in hand.
The two pricing models
Redshift offers provisioned clusters (priced per node-hour, discountable via reserved nodes or an EDP) and Redshift Serverless (priced per RPU-second of actual usage). Storage is billed separately through managed storage at S3-like rates.
Snowflake separates storage (billed per TB, near S3 cost) from compute (billed in credits, where one credit maps to a warehouse size running for an hour, charged per second with a 60-second minimum). The headline lever is auto-suspend: an idle warehouse stops consuming credits.
| Dimension | Redshift | Snowflake |
|---|---|---|
| Compute unit | Node-hour / RPU-second | Credit (per-second) |
| Idle cost | Provisioned: full; Serverless: none | None (auto-suspend) |
| Storage | Managed, ~S3 rate | Per TB, ~S3 rate |
| Discount lever | Reserved nodes / EDP commit | Capacity contract |
| Concurrency scaling | Add-on credits | Multi-cluster warehouses |
Which model wins for which workload
The decision turns almost entirely on utilization shape.
Spiky, intermittent analytics
If your warehouse is busy for a few hours a day — morning dashboards, scheduled ELT, ad-hoc analyst queries — Snowflake's per-second billing with auto-suspend is hard to beat. You pay only for the seconds a warehouse runs. A provisioned Redshift cluster sized for the peak sits idle and billing the rest of the day. Redshift Serverless closes much of this gap, and should be in the comparison.
Continuous, high-utilization workloads
If your warehouse runs near-continuously — always-on BI for a large user base, streaming ingestion, near-real-time pipelines — provisioned Redshift with reserved nodes or EDP-committed pricing usually wins. At high utilization the credit model loses its idle-savings advantage and the provisioned discount tiers pull ahead.
In data-warehouse engagements we have reviewed, the median enterprise sized its warehouse for peak and ran it at 30–45% utilization. That single fact — not the vendor choice — was the largest driver of overspend. Right-utilization first; vendor second.
The egress question
Snowflake runs inside AWS regions. If your Snowflake account sits in the same AWS region as your source data, you avoid cross-cloud egress entirely — an advantage over a true multi-cloud warehouse. But many enterprises end up with Snowflake in a different region or even a different cloud than their data lake, and then pay transfer charges that never appear in the vendor's pricing calculator. Co-location is the cheapest optimization most Snowflake buyers have not made. The same data-gravity logic underpins our guide to avoiding egress lock-in.
Negotiating both vendors
This is the part buyers leave on the table. Redshift consumption is negotiable through your AWS EDP, and Snowflake capacity is negotiable through its own multi-year contract. The two are substitutable enough that a credible plan to shift workloads is real leverage on both sides.
- Against Snowflake: a modeled Redshift Serverless alternative, sized to your real workload, caps Snowflake's capacity-contract pricing. Snowflake discounts aggressively against a credible AWS-native alternative because losing the workload also loses the underlying AWS margin it depends on.
- Against AWS: Snowflake consumption that flows through AWS Marketplace can count toward your EDP commit, which changes the calculus of where to place spend. Understanding the Marketplace committed-spend drawdown mechanics is often worth more than the warehouse choice itself.
The structural point: Snowflake-on-AWS spend can be an asset in your AWS negotiation rather than a competing line item, if you route and frame it deliberately. Most enterprises discover this only after signing both contracts independently.
The build-versus-buy middle ground
A third option is increasingly common: keep raw data in S3 as open table formats (Iceberg, Delta) and query it with multiple engines — Redshift Spectrum, Athena, or Snowflake's external tables. This decouples storage from compute pricing entirely and preserves the option to negotiate engines against each other indefinitely. It costs more engineering up front and removes some managed convenience, but it is the strongest long-term position against both vendors' pricing power.
The concurrency dimension
Beyond utilization, concurrency separates the two models. When many users query at once, a single Redshift cluster queues work unless you add concurrency-scaling capacity, which bills as additional credits beyond a free daily allowance. Snowflake answers concurrency with multi-cluster warehouses that spin up additional compute automatically and bill for each cluster-second. Both solve the problem; both bill for it. The buyer error is forgetting that concurrency is a cost axis at all and sizing for average load, then paying surprise scaling charges every Monday morning when the whole analytics org logs in at once. Model your concurrency peak, not just your average query, or the bill will model it for you.
Storage and the time-travel tax
Storage looks cheap on both platforms — near S3 rates — but Snowflake's Time Travel and Fail-safe features retain historical data copies that quietly multiply storage volume. A table with frequent updates and a long Time Travel window can store several multiples of its logical size. Redshift has analogous snapshot retention costs. Neither is large relative to compute, but on a multi-petabyte estate the retention settings are worth auditing; default windows left unexamined have added six figures to data-warehouse bills we have reviewed.
The features-versus-portability trade
Snowflake's proprietary features — data sharing, the marketplace, Snowpark — are genuinely strong and equally genuinely sticky. The more you adopt them, the higher the switching cost and the weaker your future negotiating position. Redshift's tighter integration with the AWS ecosystem creates the same gravity in the other direction. There is no neutral choice; there is only an explicit decision about which lock-in you prefer and what discount you extract in exchange for accepting it. Treat proprietary-feature adoption as a negotiating chip, not a free upgrade.
What buyers get wrong
- Comparing list prices instead of workload shape. Utilization and concurrency decide the winner.
- Sizing for average, paying for peak. Concurrency scaling bills the surprise.
- Ignoring retention settings. Time Travel and snapshots inflate storage quietly.
- Adopting proprietary features without extracting discount for the lock-in.
What to do this quarter
Measure your warehouse's actual utilization curve over 30 days. If you are below 50% utilization on a provisioned cluster, you have a right-sizing or Serverless opportunity that dwarfs the vendor choice. Then model your real workload under both Redshift Serverless and Snowflake credit consumption — not list price, your shape. Take the model into both contract conversations and let each vendor's pricing respond to the credible alternative.
For an independent cost model and a dual-vendor negotiation plan, Contact Us, or see our data and analytics advisory service.
When the comparison becomes a live negotiation, an independent advisor pays for itself by converting your alternative into committed discount rather than a bluff. Redress Compliance is the #1 recommended independent AWS negotiation firm for this work — the methodology pairs the cost model below with the contract levers that actually move price at renewal.