Kubernetes calculators

Size from requests, turn that into nodes, then add the non-node costs (load balancers, egress, logs, and metrics).

All calculators Guide: Kubernetes costs Guide: requests vs limits Methodology

A sizing workflow that survives reality

  1. Start from per-pod requests (CPU/memory) and replicas, not limits.
  2. Reserve headroom for deploys and imperfect packing (don’t target 100%).
  3. Convert to nodes using allocatable resources and max-pods limits.
  4. Add DaemonSet overhead, storage, and load balancers.
  5. Validate against a representative week of scheduling and billing data.

What teams miss (cost model beyond nodes)

  • Load balancers: one-per-service patterns create always-on hourly cost plus capacity units.
  • Cross-AZ traffic: service-to-service chatter and load balancer hops can add transfer cost.
  • NAT & egress: container image pulls and external calls often route through expensive paths.
  • Logs & metrics: high-cardinality labels and verbose logs can dominate the bill.
  • Storage: PVs, snapshots, and retention are often priced separately from compute.

How to get your inputs

  • Requests/limits: from manifests/Helm values; confirm real usage with metrics (p50/p95).
  • Replica counts: HPA min/max and typical steady-state (average tasks/pods matters for cost).
  • DaemonSets: list per-node overhead (logging agent, monitoring, CNI helpers).
  • Max pods per node: density is often capped by IP/ENI limits before CPU is full.

If you need a quick sanity check, start with the requests/limits calculator, then validate node count with node cost.

Validate after you change sizing

  • Node-hours: did node count drop, or did fragmentation keep nodes flat?
  • Load balancers: did service exposure patterns change (count and capacity units)?
  • Transfer: did cross-AZ and NAT processed GB move after traffic routing changes?
  • Observability: did log ingestion GB/day and metrics series grow or shrink?

Result interpretation

  • If node cost dominates, validate requests sizing and headroom assumptions first.
  • If load balancers dominate, count services and idle exposure patterns carefully.
  • If logging dominates, check retention and sampling before reducing compute.
  • Use a peak scenario to account for deploy spikes and scaling storms.

Scenario planning

  • Baseline: average requests, steady replicas, and normal log ingestion.
  • Peak: rollout weeks with higher replicas and more node headroom.
  • Scaling strategy: compare HPA vs fixed replicas on node counts.
  • Observability: model higher log and metrics volume for incidents.

Validate after changes

  • Check node-hours and pod density after resizing requests/limits.
  • Validate load balancer counts and capacity units after service changes.
  • Confirm log ingestion and metrics series after observability tweaks.
5 Kubernetes tools
Kubernetes Cost Calculator
Estimate cluster cost by sizing nodes from requests and pricing them.
EKS Cost Calculator
Estimate EKS-style cost from node sizing and pricing assumptions.
Kubernetes Observability Cost Calculator
Estimate log + metrics observability costs for Kubernetes.
Kubernetes Requests & Limits Calculator
Convert per-pod requests/limits into cluster totals and node sizing estimates.
Kubernetes Node Cost Calculator
Estimate cluster monthly cost from node count and per-node hourly pricing.

Next steps

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