Prioritized by impact

Turn automation on. The fix list clears itself.

PerfectScale ranks every workload by waste and risk, then applies the right change under the policy you set. Flip the switch to see it work, or keep a person in the loop and approve each change yourself.

Automation Interactive demo · sample dataChanges wait for someone to review and apply them. Flip the switch to see the difference.
Waste removed this month$0Turn automation on to start removing waste
Risks resolved0 of 2
Potential savings$6,150
WorkloadSignalLast 14 daysRecommended changeImpact / monthStatus
ml-feature-store
StatefulSet
 Over-provisioned
CPU4000m → 1500m
Memory8Gi → 3Gi
 −$3,940waste removed Waiting for review
orders-consumer
Deployment
 OOM risk
Memory limit1Gi → 1.5Gi
Auto-shrink paused: leak pattern
 +$41to prevent OOM kills Waiting for review
ci-runners-arm64
RunnerSet
 Over-provisioned
CPU4000m → 1800m
Memory8Gi → 5Gi
 −$2,210waste removed Waiting for review
api-gateway
Deployment
 Throttled
CPU request500m → 800m
CPU limitremoved
 +$58to stop throttling Waiting for review
Sample data for illustrationImpact shown per month

Ranked by dollars and risk

Waste and resilience issues on one list, biggest first.

The change, not a chart

Exact requests and limits to set, per container.

Visibility by owner

Cluster, node group, workload, namespace and team.

Routed to the team

Alerts go to the people who run the workload.

See what PerfectScale would fix in your clustersHow PerfectScale prioritizes fixes →