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AWS Compute Optimizer

AWS Compute Optimizer analyzes resource configuration and utilization metrics to recommend rightsizing and configuration changes for supported AWS resources.

Explore pricing models, common use cases, infrastructure support, and the AWS services that commonly work with AWS Compute Optimizer.

AWS Compute Optimizer pricing and cost programs

Pricing model: Analysis and recommendation usage

On-Demand
Available
Reserved Instances or reserved capacity
Not applicable
Savings Plans
Not applicable
Spot
Not applicable

Billing dimensions: Resource analysis · External metrics ingestion · Recommendation preferences

Programs and modes: Standard recommendations · Enhanced infrastructure metrics

Compute Optimizer pricing depends on enabled analysis features; it recommends discounts but is not itself covered by them.

Free Tier: Service-specific — verify current offers

Pricing reviewed 2026-07-25. Reviewed against the linked official AWS pricing page. Recheck regional rates and program terms before purchase.

Official AWS pricing

Official AWS sources reviewed 2026-07-21.

Why implement AWS Compute Optimizer?

  • Uses historical utilization and configuration data to identify over-provisioned, under-provisioned, and optimized resources.
  • Surfaces performance risk and estimated savings so teams can balance cost reduction against workload headroom.
  • Supports organization-wide analysis and recommendations for multiple compute, storage, container, and database resource types.

How to implement AWS Compute Optimizer

  1. Opt in a standalone account or AWS Organizations management account and allow the service-linked role to read supported resource data.
  2. Wait for each resource type to accumulate the required CloudWatch metrics; enable enhanced infrastructure metrics or memory collection where the extra signal is useful.
  3. Review findings, workload migration effort, performance risk, and projected savings before testing changes in a controlled rollout.

AWS Compute Optimizer best practices

  • Use a representative metrics window that includes peaks and business cycles rather than acting on a short quiet period.
  • Treat recommendations as decision support, not automatic truth; account for latency, resilience, licensing, seasonal headroom, and planned growth.
  • Export and track recommendations, apply changes gradually, and compare post-change performance and cost against the original baseline.

AWS Compute Optimizer use cases and server impact

  • EC2 and Auto Scaling rightsizing
  • EBS and database optimization
  • Fargate and Lambda resource tuning

Does not replace servers itself; it reduces waste and performance risk by recommending better sizes and configurations for the resources already in use.

Official implementation resources

Commonly paired AWS services