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Cost & Cloud Economicsservice

AWS Compute Optimizer & Rightsizing

Metric-driven recommendations for reducing idle or oversized resources while preserving workload performance and capacity requirements.

CLF-C02SAA-C03SOA-C03SAP-C02

Visual overview

CLOUD ECONOMICS LOOPEstimate, measure, attribute, optimize, and control cost continuously
01EstimateArchitecture before launch
→
02MeasureActual cost + usage
→
03OptimizeRightsize · schedule · commit
→
04ControlBudgets · alerts · ownership
Cloud cost management is a feedback loop. Pricing choices become effective only when architecture, utilization, ownership, and business value are measured together.
AWS SERVICE MAPMeasure before resizing

Operational metrics support rightsizing analysis, and financial analysis validates the resulting savings.

CloudWatchSupply utilization evidence
Compute OptimizerGenerate rightsizing recommendations
Cost ExplorerValidate cost impact
EXAM-RELEVANT MECHANICS

Technical reference

Recommendation quality depends on observation history, available metrics, and the headroom preferences used to model risk.

InputResource configuration + utilization metrics

Compute Optimizer uses CloudWatch and supported additional metrics to assess current resource behavior.

Default lookbackRecent utilization window

AWS currently documents a 14-day default analysis history for core recommendation behavior; verify current resource-specific requirements.

Enhanced metricsLonger paid lookback for supported resources

AWS currently documents up to 93 days for enhanced infrastructure metrics on supported resource types.

HeadroomCPU/memory utilization preference

More headroom reduces the risk of saturating a recommended size but can also reduce estimated savings.

Decision orderRightsize → establish baseline → commit

Do not use a discounted rate to justify unused capacity that should first be removed.

Service limits and capabilities can change. Values shown here reflect the current AWS documentation; use the linked official sources below as the source of truth.

Rightsizing connects utilization evidence to a smaller resource footprint

AWS Compute Optimizer analyzes resource configuration and utilization metrics to identify idle resources and generate rightsizing recommendations. It uses Amazon CloudWatch metrics and resource specifications to model whether a different configuration can reduce cost while maintaining required performance. AWS currently supports multiple resource types including EC2 instances and Auto Scaling groups, EBS volumes, Lambda functions, ECS services on Fargate, databases, NAT Gateway, DynamoDB, and additional managed resources.

A recommendation is evidence, not an automatic proof that downsizing is safe. Business cycles, month-end processing, seasonal peaks, future growth, and metrics not visible to the service can justify additional headroom. Evaluate projected utilization and operational requirements before applying a recommendation, then observe the workload after the change.

Lookback period and headroom affect the recommendation

Compute Optimizer must be opted in and requires sufficient metric history for supported resources. AWS documentation currently describes a default analysis window based on recent CloudWatch metrics and an enhanced infrastructure metrics option that extends the lookback for selected resource types. Recommendation preferences can alter CPU and memory headroom or utilization thresholds, which changes how aggressively potential downsizing is evaluated.

Memory deserves special attention for EC2 because standard EC2 service metrics do not inherently provide guest-OS memory utilization in the same way they provide CPU and network telemetry. Compute Optimizer supports selected external memory-metric ingestion and other mechanisms documented for supported cases. A recommendation built without the metric that constrains the workload should be treated cautiously.

Rightsizing and purchase optimization solve different problems

Rightsizing changes the amount or shape of infrastructure consumed. Savings Plans and reservations primarily change the effective rate for eligible stable usage. Those levers can compound, but they should usually be evaluated in that order: first establish the resource footprint the workload needs, then decide how much of its stable baseline is safe to commit.

Cost Optimization Hub can aggregate and deduplicate recommendations from supported AWS optimization sources and incorporate customer-specific pricing and discounts into savings estimates. Compute Optimizer remains the deeper service for utilization-driven resource recommendations, while Cost Explorer provides the historical cost and commitment perspective used to validate financial impact.

Key takeaways

  1. 01

    Compute Optimizer uses configuration and utilization data to identify idle resources and rightsizing opportunities.

  2. 02

    The service must have sufficient metrics, and recommendation preferences can change the headroom used in analysis.

  3. 03

    Recommendations should be validated against workload peaks and business requirements before implementation.

  4. 04

    Rightsizing reduces the resource footprint; commitments reduce eligible rates—these are different optimization levers.

  5. 05

    Rightsize before committing when possible so long-term discounts are based on a realistic baseline.

Official AWS sources

Use these primary AWS resources for the source material behind this article and for deeper reference.