How do I reduce Redshift compute costs?
On Redshift the highest-return lever is pausing clusters you do not need and choosing RA3 sizing deliberately. After that, stop full-refreshing: switch heavy assets to a staging table plus MERGE so each run moves only what changed, and apply sort keys and distribution keys chosen for your actual join patterns. Bruin helps on the second and third of those, because incremental strategy is a property of the asset definition rather than something you hand-write per table. The tool licence is rarely the biggest line on the bill.
Command
bruin runDefined in
SQL + YAML
Works with
Redshift + Bruin CLI
What you get
How to do it
- 1
Measure first: find the Redshift jobs that dominate spend before changing anything.
- 2
Apply pausing clusters you do not need and choosing RA3 sizing deliberately.
- 3
Convert the largest full-refresh assets to a staging table plus MERGE.
- 4
Apply sort keys and distribution keys chosen for your actual join patterns to the tables that dominate scan volume.
- 5
Re-measure and confirm the change actually moved the bill.
How it works in code
/* @bruin
name: mart.orders
materialization:
type: table
strategy: merge
incremental_key: updated_at
@bruin */Run bruin run and Bruin moves only changed rows on Redshift instead of rebuilding the table.
Worth knowing
On Redshift, a bad distribution key forces data across nodes on every join and no amount of extra compute fixes it Measure before and after: cost work done on intuition usually optimises the wrong job.
Other ways to do this
Bruin is not always the right answer. Here is where the alternatives are stronger.
| Option | When it is the better choice |
|---|---|
| Bruin | Practical levers for cutting compute spend on Redshift. |
| Native Redshift cost tooling | Use it. Redshift's own usage reporting is the right place to find out where the money actually goes before changing any tool. |
| dbt incremental models | The same incremental savings if dbt is already your transformation layer on Redshift. No reason to migrate for this alone. |
| A cost-observability vendor | Worth it once spend is large enough that attribution across teams is the hard part rather than the optimisation itself. |
Common questions
How do I reduce Redshift compute costs?
Start with pausing clusters you do not need and choosing RA3 sizing deliberately, then convert full refreshes to a staging table plus MERGE, then apply sort keys and distribution keys chosen for your actual join patterns.
Is a cheaper tool the way to cut Redshift costs?
Usually not. Warehouse compute is normally the largest line and the most reducible. Licence savings matter, but far less than how often you rebuild tables and how much data each query reads.
What is the cheapest stack around Redshift?
One with no per-seat and no per-row licence in it: open-source ingestion, open-source transformation, and your CI runner as the scheduler. That leaves warehouse compute as the only real bill.
Fewer tools, a smaller bill
Open source. No per-seat and no per-row fee, so the bill is warehouse compute.