Google Kubernetes Engine (GKE) pricing: nodes, networking, storage, and observability

GKE planning gets accurate when you treat it as a stack: compute (nodes), networking (LB/egress/inter-zone), storage, and observability. The goal is not just to estimate, but to keep the cluster right-sized as traffic and workloads change.

0) What to model (drivers you can validate)

  • Node pools: instance type, min/max nodes, and average node-hours per month.
  • Bin packing: requests/limits, kube-system overhead, and DaemonSets.
  • Networking: load balancers, internet egress, inter-zone traffic, and private connectivity.
  • Storage: PV GB-month, snapshots, and retention windows.
  • Logs/metrics: ingestion volume, retention, and scan/search behavior during incidents.

1) Node pools: instance-hours (baseline and peak)

Model each pool separately and keep baseline vs peak months distinct. Scale-out events often do not average out in practice (marketing, seasonality, incident retries).

Tool: Compute instance cost.

2) Requests/limits: the bin packing multiplier

Requests determine how many pods fit on a node. Over-requesting is one of the most common cost leaks. Validate overhead (kube-system, DaemonSets) and reserve capacity explicitly.

Tool: Requests/limits helper.

  • For CPU-bound workloads, right-sized CPU requests can reduce nodes dramatically.
  • For memory-bound workloads, use p95 memory to avoid OOM churn.

3) Networking: load balancers, egress, and inter-zone traffic

Internet-facing services often have meaningful egress. Multi-zone clusters also generate inter-zone east-west traffic that does not show up as "internet bandwidth" but still affects the bill.

Tools: Egress cost, Load balancing pricing, Inter-zone transfer.

4) Storage: PVs, snapshots, retention

Persistent volumes are a GB-month driver. Snapshots/backups and retention windows add a quiet multiplier, especially for stateful workloads.

Tool: Storage pricing (generic).

5) Observability: logs and metrics

Logging and metrics scale with pod count and verbosity. If each request emits multiple log lines, log ingestion can exceed compute. Model ingestion and retention explicitly.

Tool: Log cost tools.

Worked estimate template (copy/paste)

  • Nodes = sum(node pool avg node-hours/month) (baseline + peak)
  • Bin packing = effective pods/node based on requests/limits (headroom + overhead)
  • Egress = outbound GB/month (internet + cross-region) + inter-zone GB/month
  • Storage = PV GB-month + snapshot GB-month (retention)
  • Logs = log GB/month + retention/scan assumptions

Common pitfalls

  • Over-requesting CPU/memory so pods cannot pack (node count inflates).
  • Not splitting system overhead (DaemonSets and kube-system are real capacity).
  • Ignoring egress and load balancers (often the second-largest driver after nodes).
  • Verbose logs per request and long retention windows.
  • Underestimating inter-zone east-west traffic in multi-zone clusters.

How to validate

  • Validate node utilization vs requested resources (find over-requesting and bin packing gaps).
  • Validate outbound GB by destination and peak periods (deployments, incidents, scale-outs).
  • Validate log bytes per request and retention policies.

Related tools

Kubernetes cost Compute Egress Logs

Sources

Related guides

Kubernetes cost model beyond nodes: the checklist most teams miss
A practical Kubernetes cost model checklist: control plane, load balancers, storage, logs/metrics, and egress - plus links to calculators to estimate each part.
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Related calculators

Data Egress Cost Calculator
Estimate monthly egress spend from GB transferred and $/GB pricing.
API Response Size Transfer Calculator
Estimate monthly transfer from request volume and average response size.
VPC Data Transfer Cost Calculator
Estimate data transfer spend from GB/month and $/GB assumptions.
Cross-region Transfer Cost Calculator
Estimate monthly cross-region transfer cost from GB transferred and $/GB pricing.
Kubernetes Cost Calculator
Estimate cluster cost by sizing nodes from requests and pricing them.
Kubernetes Node Cost Calculator
Estimate cluster monthly cost from node count and per-node hourly pricing.

FAQ

What usually drives GKE cost?
Node compute dominates many clusters, but egress, load balancers, persistent disks, and logging can be large for production workloads.
How do I estimate quickly?
Estimate node-hours by pool, then add separate estimates for egress, storage GB-month, and log ingestion/retention.
What is the most common mistake?
Over-requesting CPU/memory so pods do not pack well. Poor bin packing inflates node count even when actual usage is low.
How do I validate?
Validate workload requests/limits and overhead, validate outbound GB and peak periods, and validate log bytes per request and retention.
Last updated: 2026-01-27