Kubernetes Cost Optimization: Quick Wins

Kubernetes makes it easy to ship—and easy to overspend. Most teams don’t have a single “big cost problem”; they have a handful of small leaks: over-requested resources, idle clusters, noisy observability retention, and lack of guardrails.

This guide focuses on quick wins that reduce spend without creating reliability risks.

Where spend hides

• Over-requested CPU/memory
• Idle environments and “always on” dev clusters
• Oversized node pools and poor bin packing
• Unbounded logs/metrics retention

First: measure cost the way engineers can act on

Before changing anything, make cost visible in engineering terms:

• Cost by namespace / workload / service
• Requests vs actual usage (CPU/memory)
• Cost of non-prod (often surprisingly high)
• Cost of observability (logs/metrics/traces retention and ingestion)

If you can’t answer “what changed last week that increased spend,” optimization becomes guesswork.

The fastest levers

1) Right-size requests (and be careful with limits)

The most common Kubernetes cost driver is over-requested CPU/memory, which forces larger nodes and worse bin packing.

• Lower requests based on P95/P99 usage, not peak guesses
• Be cautious with aggressive memory limits (OOMKills can create incidents)
• Start with non-critical services; expand once you see stable results

2) Turn down non-prod by schedule

Non-prod clusters and environments frequently run 24/7 “just in case.”

• Scale down dev/staging at night and weekends
• Suspend batch jobs and preview environments when unused
• Use smaller node pools for non-prod (and separate from prod)

3) Autoscale where it’s safe

Autoscaling is powerful—but it must match workload behavior.

• HPA for stateless services with stable scaling signals
• Cluster autoscaler (or equivalent) to avoid oversized pools
• Use separate pools for latency-sensitive vs batch/worker workloads

4) Improve bin packing and node pool design

Many clusters have too many “special” node pools and constraints.

• Reduce fragmentation: fewer pools, clearer intent
• Use taints/tolerations and affinity sparingly
• Ensure pod disruption budgets aren’t blocking consolidation

5) Review storage and lifecycle policies

Storage spend often grows quietly:

• Orphaned PVCs and snapshots
• High-performance storage classes used by default
• No lifecycle policy for object storage and backups

6) Put guardrails on logs/metrics retention

Unbounded retention is a slow financial incident.

• Set retention by environment (prod vs non-prod)
• Sample high-cardinality logs and traces
• Prefer actionable signals over “collect everything forever”

Reliability-safe optimization: what not to do

Cost wins shouldn’t become outage risks. Avoid:

• Dropping memory limits aggressively without testing
• Removing redundancy without understanding failure modes
• Autoscaling critical services without load testing and rollback plans
• Collapsing all workloads into one pool when isolation matters

A quick Kubernetes cost optimization checklist

• Requests are calibrated to real usage (not guesses)
• Non-prod scales down automatically
• Node pools are intentional and not overly fragmented
• Autoscaling is enabled where it makes sense
• Storage and snapshots have ownership and lifecycle rules
• Observability retention is bounded and environment-aware

When you’re unsure what the biggest drivers are

If you’re unsure where to start, a focused infrastructure audit will identify the biggest cost and risk drivers and turn them into a prioritized implementation plan.