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Evaluate every AI-generated AWS optimization recommendation as a hypothesis, not a guarantee. Check whether its data represents the workload, recalculate savings using your account’s pricing and commitments, assess performance and migration risks with the application owner, then roll out the change in a controlled way and measure the result.

What an AWS recommendation can—and cannot—tell you

AWS Compute Optimizer analyzes resource configuration and utilization to produce rightsizing and idle-resource recommendations. It provides utilization history and projected utilization to help compare options. AWS describes the graphs this way: “Compute Optimizer also provides graphs showing recent utilization metric history data, as well as projected utilization for recommendations, which you can use to evaluate which recommendation provides the best price-performance trade-off.” AWS Compute Optimizer User Guide.

That information can support a decision, but it does not establish that a change will preserve every application’s service-level objectives (SLOs) or deliver the displayed savings under every billing arrangement. AWS documentation does not publish a general accuracy rate or an independently validated success rate for these recommendations. Apply the same evidence checks to third-party AI advisors; AWS service documentation describes AWS tools, not the accuracy of every external system.

Evaluate a recommendation before changing anything

  1. Record the recommendation and its evidence

    Capture the affected resource, current and proposed configuration, recommendation source and model or service version if shown, timestamp, account and Region, rationale, estimated savings, and performance-risk indicator. In Compute Optimizer, inspect the utilization graphs and projected utilization associated with the options rather than relying only on the ranking.

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  2. Check whether the data represents the workload

    Compute Optimizer starts with CloudWatch utilization metrics and, by default, analyzes 14 days of history after opt-in. Its rightsizing preferences offer 14-, 32-, or 93-day lookbacks; the 93-day option requires paid enhanced infrastructure metrics. See Compute Optimizer metrics and data and rightsizing preferences.

    Choose a window that includes relevant monthly or seasonal patterns, traffic peaks, batch jobs, and failover periods. Confirm that the inputs include the resource dimensions that matter to your workload. For EC2, memory is not collected by default in CloudWatch; Compute Optimizer can ingest external EC2 memory metrics. If memory pressure could constrain the application, a recommendation based only on CPU may miss an important risk. AWS explains the default collection limitation in its EC2 CloudWatch monitoring guidance.

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  3. Inspect risk settings and constraints

    For supported resources, review recommendation preferences such as CPU thresholds and CPU and memory headroom. AWS documents a default P99.5 CPU threshold and 20% CPU and memory headroom for EC2 rightsizing preferences. These are Compute Optimizer settings, not universal engineering targets. Lower thresholds can disregard more peaks; lower headroom can increase potential savings while increasing the chance that capacity will be insufficient. Confirm that allowed instance families and processor architectures fit the organization’s technical and operational constraints.

  4. Recalculate savings for your account

    Where appropriate, use Cost Optimization Hub to view AWS recommendations with account-specific discounts incorporated into savings estimates and to group or prioritize related opportunities. Compare the estimate against actual billing data and applicable Savings Plans and Reserved Instances. Avoid adding estimates for overlapping changes as though each opportunity were independent.

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    Check which AWS tool produced the estimate. Cost Explorer rightsizing uses the preceding 14 days, and its recommendations are a subset of Compute Optimizer results. AWS notes that Cost Explorer can omit second-order effects such as Reserved Instance hour reallocation. Compute Optimizer can also surface performance-oriented recommendations that increase cost. Read the relevant documentation for Cost Optimization Hub and Cost Explorer rightsizing recommendations.

  5. Compare options beyond the savings figure

    Compute Optimizer can present up to three EC2 options per finding, ranked using estimated savings, performance risk, and migration effort. Its EC2 details let reviewers compare CPU, memory, network, and disk metrics with recommendation capacity. Consider those dimensions alongside architecture compatibility, licensing, dependencies, migration work, downtime, and operating constraints. If an option moves an x86 workload to Graviton/ARM64, verify application and dependency compatibility instead of treating the suggested price-performance ratio as a guaranteed outcome. AWS describes the recommendation presentation in its Compute Optimizer enhancements article.

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  6. Ask the workload owner what metrics cannot show

    Confirm SLOs, latency sensitivity, traffic patterns, planned growth, recovery requirements, scheduled work, and operational constraints with the application team. Metrics may not reveal the business context behind seasonal traffic or a scheduled batch workload. A technically plausible target can still be unsuitable if it conflicts with an upcoming launch, recovery design, or team operating model.

  7. Roll out the change and measure it

    Agree on an owner, baseline, staged change plan, relevant service-level and resource metrics, and rollback path. After implementation, compare performance with the pre-change baseline and the service’s own objectives. Check actual costs in Cost Explorer rather than treating the recommendation’s estimate as realized savings. AWS recommends regular review, workload-owner validation, and tracking savings after changes in its Compute Optimizer guidance.

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Use the same review rubric for every candidate

Review area Questions to answer
Input coverage Which metrics, time window, Regions, accounts, and resources informed the result? Are memory, network, disk, and peak periods represented where relevant?
Savings realism Is the estimate before or after discounts? Does it reflect current Savings Plans, Reserved Instances, actual usage, and interactions with related recommendations?
Performance risk Which observed peaks and headroom remain? Which SLOs could be affected, and how will they be monitored?
Compatibility and effort Does the target family or architecture support the workload, dependencies, licensing, and operating model? What migration work or downtime is involved?
Explainability Can reviewers trace the suggestion to observed inputs and understand its assumptions, caveats, and model or service version?
Validation Is there an owner, staged implementation, rollback plan, baseline, and agreed measure for realized savings and performance?

How to use AWS tools without conflating their estimates

Cost Optimization Hub aggregates AWS cost-optimization recommendations, supports filtering, grouping, prioritization, benchmarks, and progress tracking, and lists opportunities including rightsizing, idle resources, Savings Plans, and Reserved Instances. It can improve portfolio prioritization, but reviewers still need to check account settings and avoid double-counting related opportunities.

Cost Explorer and Compute Optimizer do not present identical recommendation sets or necessarily the same cost assumptions. Verify the source and estimate type before comparing dollar amounts, and treat each result as evidence to investigate rather than a promise of savings.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.