How to Reduce Kubernetes Costs by 40-60%: Enterprise Cost Optimisation Guide [2026]

Deploy Cost Visibility

You can't optimise what you can't see. Deploy cost monitoring first:

• Kubecost — Open-source Kubernetes cost monitoring. Shows cost per namespace, deployment, pod, and label.
• OpenCost — CNCF project, vendor-neutral cost monitoring. Simpler than Kubecost but covers the basics.
• Cloud provider tools — AWS Cost Explorer with EKS tags, Azure Cost Management, GCP Billing reports
• CAST AI / ScaleOps — Automated cost optimisation with AI-driven right-sizing (we partner with both)

First measurement: Calculate your cluster utilisation rate. CPU requested vs CPU used, memory requested vs memory used. Most enterprises see 15-30% actual utilisation.

The #1 Cost Reduction Lever

Most pods request far more resources than they use because developers set requests once and never revisit them:

• Analyse actual usage — Look at P95 CPU and memory usage over 7-14 days
• Set requests to P95 actual usage — This is what the pod actually needs
• Set limits to 2-3x requests — Allow burst capacity without unlimited resource consumption
• Use Vertical Pod Autoscaler (VPA) in recommendation mode first — it suggests right-sized requests based on actual usage

Typical savings: 30-50% reduction in requested resources → proportional cost reduction

Three Levels of Autoscaling

• Horizontal Pod Autoscaler (HPA) — Scale pods based on CPU, memory, or custom metrics. Essential for variable workloads. Use KEDA for event-driven scaling (queue depth, API request rate).
• Vertical Pod Autoscaler (VPA) — Automatically adjust resource requests based on actual usage. Run in recommendation mode first, then enable auto-update for non-critical workloads.
• Cluster Autoscaler / Karpenter — Scale nodes based on pending pod scheduling. Karpenter (AWS) is faster and more efficient than the standard cluster autoscaler.

Typical savings: 20-40% from autoscaling alone (no more paying for peak capacity 24/7)

• AWS Spot Instances: 60-90% discount. Use for stateless workloads, batch processing, CI/CD runners, and dev/staging environments.
• GCP Preemptible/Spot VMs: 60-91% discount. Similar use cases.
• Azure Spot VMs: Up to 90% discount.
• Best practice: Mix on-demand (for stateful/critical workloads) with spot (for everything else). Use pod disruption budgets to handle spot interruptions gracefully.
• For AI workloads: GPU spot instances save significantly on training jobs. Not recommended for production inference serving.

Typical savings: 50-70% on workloads that can tolerate interruption

FinOps Practices for Kubernetes

• Resource quotas per namespace — Prevent any single team from consuming unlimited cluster resources
• Cost allocation by label — Label every workload with team, project, and environment. This enables showback/chargeback.
• Idle resource detection — Automated alerts for namespaces with < 5% utilisation over 7 days
• Environment lifecycle — Auto-shutdown dev/staging environments outside business hours (saves ~65% on non-prod)
• Reserved instances/savings plans — For base capacity that runs 24/7, commit to 1-year reservations for 30-40% savings

1. Deploy Kubecost or OpenCost (day 1)
2. Right-size the top 10 most expensive deployments (week 1, saves 20-30%)
3. Enable cluster autoscaler / Karpenter (week 1-2)
4. Move non-critical workloads to spot instances (week 2-3)
5. Implement HPA on variable workloads (week 3-4)
6. Auto-shutdown non-prod environments at night/weekends (week 2)
7. Set namespace resource quotas (week 4)
8. Establish monthly cost review cadence (ongoing)

Expected total savings: 40-60% reduction in monthly Kubernetes spend within 30 days.