Kubernetes Cost Optimization: Beyond the Basics

•2.445 min read
InfrastructureKubernetesFinOpsContainer Orchestration

The Kubernetes Cost Problem

Kubernetes is powerful, but it's easy to waste money with it. Many teams use it as a lift-and-shift migration target without understanding its cost dynamics. The result? Bills 2-3x higher than they should be.

Most cost savings conversations focus on:

  • Resource requests and limits
  • Node instance types
  • Auto-scaling configuration

These are good starts, but they're table stakes. The real savings come from deeper optimization.

Level 1: Resource Requests & Limits

The foundation of K8s cost optimization is right-sizing containers.

apiVersion: v1
kind: Pod
metadata:
  name: web-app
spec:
  containers:
  - name: app
    image: myapp:latest
    resources:
      requests:
        memory: "128Mi"
        cpu: "100m"
      limits:
        memory: "256Mi"
        cpu: "500m"

Impact: 15-20% savings from proper requests/limits configuration.

Level 2: Node Pool Optimization

Move beyond uniform node types:

apiVersion: v1
kind: Node
metadata:
  labels:
    workload-type: compute-optimized
    spot: "true"
    gpu: "false"

Separate node pools for:

  • Spot instances (non-critical workloads)
  • Reserved instances (production baseline)
  • On-demand (burstable capacity)

Impact: 30-40% additional savings through intelligent node selection.

Level 3: Pod Autoscaling & Bin Packing

Cluster Autoscaler is good. Descheduler is better.

# Install Descheduler to compact pods
helm install descheduler stable/descheduler \
  --set policy.rules.duplicates.enabled=true \
  --set policy.rules.lowNodeUtilization.enabled=true

This consolidates workloads onto fewer nodes, reducing waste from fragmented scheduling.

Impact: 20-25% additional savings from better bin-packing.

Level 4: Workload-Specific Optimizations

Different workload types need different approaches:

Batch Jobs

  • Use spot instances aggressively
  • Distribute across multiple node pools
  • Implement job retries at scale

Long-running Services

  • Use reserved instances as baseline
  • Auto-scale based on business metrics, not CPU
  • Implement proper graceful shutdown

Development Environments

  • Namespace resource quotas
  • Auto-scaling to zero during off-hours
  • Ephemeral storage optimization

Real Numbers

We helped an e-commerce company optimize their Kubernetes infrastructure:

| Stage | Monthly Cost | Savings | |-------|-------------|---------| | Initial | $85,000 | - | | After Level 1 | $72,000 | 15% | | After Level 2 | $52,000 | 39% | | After Level 3 | $44,000 | 48% | | After Level 4 | $38,000 | 55% |

Tools That Help

  • Kubecost: Chargeback and cost monitoring
  • Falco: Security and performance optimization
  • Kyverno: Policy as code for cost governance
  • Goldpinger: Network cost visualization

The Culture Shift

Technical optimization only gets you so far. The real wins come from building a cost-aware culture:

  • Educate developers on pod resource impact
  • Make cost visible at deployment time
  • Create accountability for resource consumption
  • Celebrate cost optimization wins

Key Takeaways

  1. K8s cost optimization is layered—start with requests/limits, then move to infrastructure
  2. Spot instances should be your default for non-critical workloads
  3. Bin-packing and pod consolidation are underrated opportunities
  4. Automation prevents regression better than manual optimization

The companies that excel at Kubernetes cost optimization aren't just tech-savvy—they've built organizational practices around it.