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Cost Optimization

Stop overpaying for K8s resources you aren't using.

65% of containers use less than half their requested CPU and memory. Wave finds the waste — and eliminates it.

The Problem

Kubernetes clusters accumulate waste from three sources: over-provisioned containers that developers sized by gut feel, idle nodes that linger after deployments change, and orphaned Persistent Volumes that no one cleaned up. CNCF and Flexera data consistently show 65%+ of containers running below 50% utilization — yet teams are afraid to right-size because they don't trust the numbers.

The Wave Solution

  • 1Smart Sizing analyzes real CPU and memory consumption and recommends right-sized requests and limits — backed by percentile data, not guesswork.
  • 2Idle Node Detection surfaces nodes running below threshold utilization across the whole cluster, so you can consolidate or drain them.
  • 3Unused PV Detection and PV Cleanup identify orphaned volumes and automate safe removal — no manual audit required.
  • 4Karpenter Spot Workload Placement automates threshold-based Spot/On-Demand splitting — stable baseline on On-Demand, burst overflow on Spot.

Expected Outcomes

  • ✓20–40% compute cost reduction from right-sizing alone
  • ✓Up to 70% node cost reduction through safe Spot adoption
  • ✓Automatic cleanup of 100% of orphaned storage volumes

"Our containers use less than half of what they request"

Developers over-request CPU and memory to stay safe, and nothing tells them what the workload actually needs. Result: 65%+ of containers run under 50% utilization, and you pay for capacity that never moves.

20–40% compute cost reduction
Smart Sizing

"We have nodes running with no workloads on them"

Nodes accumulate from old deployments, misaligned autoscalers, or Karpenter configurations that don't consolidate well. You don't know which nodes are idle until someone audits manually.

Automatic idle node discovery, cluster-wide inventory
Idle Node DetectionSpot Workload Placement

"Our bill is full of PVs nobody owns anymore"

Persistent Volumes outlive the workloads that created them. No one tracks orphans, no one cleans up, and the storage line on your invoice keeps growing.

Automatic unused PV detection and cleanup
Unused PV DetectionPV Cleanup

Frequently Asked Questions

How does Wave reduce Kubernetes cloud costs?▼
Wave analyzes cluster-wide resource utilization and identifies three key waste sources: idle nodes running at low utilization, orphaned persistent volumes no one uses, and over-provisioned container resources. It provides actionable recommendations for consolidation, cleanup, and right-sizing — typically achieving 20–40% cost reduction without impacting performance.
Can Wave optimize Spot instance usage?▼
Yes. Wave automates threshold-based separation between On-Demand and Spot instances. Stable baseline workloads stay on On-Demand for reliability, while overflow and fault-tolerant workloads are placed on Spot — reducing node costs by up to 70%.
How quickly can I see cost savings after deploying Wave?▼
Wave begins analyzing your cluster immediately after deployment. Initial recommendations appear within hours based on current utilization data. Most teams see measurable savings within the first week as they apply right-sizing and idle node recommendations.

See how much you could save

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Resource OptimizationK8s DiagnosisTraffic ProtectionGPU Visibility & VirtualizationK8s AI AgentAI SRE Agent · 2026 Q3
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