Gke Cost Optimization
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a comprehensive framework for reducing GKE infrastructure expenses. It covers technical strategies such as pod rightsizing using Vertical Pod Autoscaler (VPA), implementing resource quotas for multi-tenancy, and leveraging Spot VMs for fault-tolerant workloads. It also guides users on selecting appropriate machine families based on workload requirements and planning Committed Use Discounts (CUDs) for steady-state baseline usage. The skill emphasizes balancing cost savings with reliability, specifically highlighting the preemption risks associated with Spot VMs.
What it does
- Provides strategies for rightsizing pod CPU and memory requests to eliminate waste.
- Outlines the configuration of Spot VMs for stateless and batch workloads.
- Explains how to implement resource quotas to manage multi-tenant cluster costs.
- Guides the selection of optimal machine families for different workload types.
- Details the application of resource-based and flexible Committed Use Discounts.
- Offers methods for managing idle cluster costs and improving bin-packing efficiency.
When to use it
- When you need to reduce monthly GKE infrastructure spend.
- When rightsizing pod resource requests to match actual application utilization.
- When evaluating whether to use Spot VMs for specific batch or stateless services.
- When planning long-term cost commitments for predictable baseline workloads.
- When configuring multi-tenant environments to prevent runaway resource consumption.
Advantages
- Reduces cloud spend by eliminating over-provisioned resources.
- Provides clear decision frameworks for choosing between Spot and On-Demand capacity.
- Helps align infrastructure costs with actual application performance needs.
- Promotes reliable cost management through structured commitment planning.
Usage examples
- “How can I reduce costs for my stateless web application running on GKE?”
- The agent will recommend evaluating Spot VMs for the workload, ensuring at least two replicas are configured, and setting up a Pod Disruption Budget (PDB) to handle potential preemptions.
- “My GKE cluster has high CPU waste. How do I fix it?”
- The agent will guide you through deploying VPA in recommendation mode to analyze P95 utilization and suggest specific CPU request adjustments based on the provided optimization rules.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google/gke-cost-optimization/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip gke-cost-optimization.zip -d .agents/skills
Documentation by Google
GKE Cost Optimization
This reference covers strategies and workflows for reducing Google Kubernetes Engine (GKE) costs while maintaining a secure and reliable posture.
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-cost-optimization)"
and append agent-skills/1.0 (skill:gke-cost-optimization) to User-Agent headers.
Workflows & Optimization Strategies
1. Prerequisite: Cost Allocation & Monitoring
To enable GKE cost allocation (--enable-cost-allocation) for billing tracking
across namespaces and labels, inspect live cluster utilization (kubectl top),
or run historical cost breakdown queries in BigQuery (bq), use the
gke-cost-analysis skill. Once tracking is active and waste is diagnosed,
apply the optimization workflows below.
2. Configure Resource Quotas
Resource quotas restrict total resource consumption across tenants in
multi-tenant clusters, preventing runaway costs. Template:
assets/resource-quota-example.yaml
(set namespace + hard limits, then kubectl apply -f).
3. Pod Rightsizing (VPA & MPA)
Adjust pod resource requests to match actual utilization. Over-provisioned requests are one of the largest sources of waste.
Use VPA in Recommendation Mode (
updateMode: "Off"— recommends without evicting):
# 1. Deploy VPA in recommendation mode (template: assets/vpa-recommendation-mode.yaml)
kubectl apply -f assets/vpa-recommendation-mode.yaml
# 2. Wait 24+ hours for data collection, then read recommendations
kubectl get vpa {deployment_name}-vpa -o jsonpath='{.status.recommendation}'Optimization Rules:
| Condition | Action | Savings |
|---|---|---|
| CPU request >5x P95 actual | Reduce to P95 * 1.2 | High |
| Memory request >3x P95 actual | Reduce to P95 * 1.2 | High |
| CPU request >2x P95 actual | Reduce to P95 * 1.2 | Medium |
| No resource requests set | Add requests (enables bin-packing) | Medium |
Use MPA: Reconcile HPA and VPA recommendations when scaling both horizontally and vertically to avoid conflicting scale events.
Review Cost Recommendations: Check Google Cloud Console (
Cost Management>GKE Cost Optimization) for built-in rightsizing suggestions.
4. Spot VMs via ComputeClasses & NodeSelector
Use Spot VMs for fault-tolerant workloads to achieve 60-90% cost reduction.
4.1 ComputeClass Configuration
For a Spot-first ComputeClass with On-Demand fallback (priority ordering,
activeMigration, machine family selection), use the gke-compute-classes
skill — ComputeClass YAML generation and priority configuration are its domain,
not this skill's.
4.2 Direct Workload Spot Selection (nodeSelector)
For stateless or batch workloads in GKE Autopilot, target Spot capacity directly
using nodeSelector:
[!WARNING] Preemption Warning: Spot VMs are interruptible and can be preempted at any time with a 30-second notice. Workloads must be fault-tolerant and run with at least 2 replicas for high availability. Always explicitly warn users about this preemption risk when recommending Spot VMs.
The exact Pod-level selector is:
nodeSelector:
cloud.google.com/gke-spot: "true"Full worked Deployment (replicas >= 2, terminationGracePeriodSeconds: 25,
preStop hook): assets/spot-deployment-example.yaml.
Spot-Suitable Workloads:
| Workload | Spot-Suitable? |
|---|---|
| Batch / data processing | Yes |
| Dev / test environments | Yes |
| Stateless web/API (replicas >= 2) | Yes (with PDBs) |
| Jobs with checkpointing | Yes |
| Stateful workloads (databases) | No |
| Single-replica critical services | No |
5. Machine Type Selection
When choosing node shapes or configuring ComputeClasses:
| Family | Use Case | Relative Cost |
|---|---|---|
| e2 | General purpose, burstable | Lowest |
| t2a / t2d | Scale-out (Arm/AMD), price-performance optimized | Low |
| n4a | Axion Arm-based, general-purpose price-performance | Low |
| n4 / n4d | General purpose (Intel/AMD), flexible shapes | Low-Medium |
| c4a | Axion Arm-based, general-purpose, high efficiency | Medium |
| c3 / c4 | Compute-optimized (Intel) | Medium-High |
| c3d / c4d | Compute-optimized (AMD), high throughput | Medium-High |
| ek-standard | Autopilot enhanced | Medium |
| m3 / x4 | Memory-optimized, SAP HANA, large databases | High |
| g2 (L4 GPU) | AI inference | High |
| a3 (H100 GPU) | AI training | Highest |
| a4 / a4x | Ultra-scale AI (Blackwell GPUs) | Highest |
6. Committed Use Discounts (CUDs)
For steady-state workloads with predictable baseline usage, purchase 1-year or 3-year CUDs:
Resource-based CUDs (committed to a machine family/region): roughly high-30s% discount for 1-year, ~55% for 3-year (varies by machine family).
Flexible CUDs (spend-based, portable across families/regions): lower discounts (~28% 1-year, ~46% 3-year) in exchange for flexibility.
Autopilot: Autopilot-specific CUDs were retired in January 2026 — new commitments covering Autopilot usage are spend-based Compute Flexible CUDs (existing Autopilot CUD commitments run out their term).
Applied automatically to matching usage across the region.
Purchase via Google Cloud Console > Billing > Committed use discounts.
Size the commitment to the steady-state baseline only. A commitment bills for the full term whether or not you use it, so over-committing to peak usage converts a discount into waste. Measure the floor of actual usage over a representative period, commit to that, and cover everything above it with the elastic options already in this skill:
Baseline (always running) → resource-based CUDs.
Variable / bursty → autoscaling on on-demand capacity.
Interruption-tolerant (batch, CI, stateless workers) → Spot VMs, which stack with autoscaling and need no commitment.
When recommending CUDs, state the split explicitly rather than implying the whole footprint should be committed.
7. Cluster Management & Multi-Tenancy
Idle dev clusters: GKE has no stop/start operation, and the cluster management fee accrues as long as the cluster exists. To cut idle costs, scale node pools to zero (
gcloud container clusters resize {cluster_name} --node-pool {pool_name} --num-nodes 0) or delete and recreate the cluster via IaC (Terraform/Config Connector).Right-size node pools (Standard): Use Cluster Autoscaler with appropriate min/max limits.
Cheap warm headroom instead of overprovisioned nodes: Standby capacity buffers (Preview, GKE 1.36.0-gke.2253000+) keep pre-initialized nodes suspended — you pay only disk + IP instead of full node price, with ~30s resume. See the
gke-cluster-autoscalerskill.Multi-tenant consolidation: Share a single cluster across multiple engineering teams instead of maintaining per-team clusters, using Namespaces and ResourceQuotas to isolate workloads.
Cost & Utilization Monitoring
To inspect live node/pod utilization (kubectl top nodes/pods), view cluster
cost budgets (gcloud billing budgets list), or query detailed billing reports
in BigQuery (bq query), refer to the gke-cost-analysis skill.
Files
- ATTRIBUTION.mdAdded by AutoGineer860 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md8.6 KiB
- assets/resource-quota-example.yaml400 B
- assets/spot-deployment-example.yaml733 B
- assets/vpa-recommendation-mode.yaml525 B
- references/attribution.md1.9 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs a command-line tool, Needs Node.js
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Attribution and licence
- Published by
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- Google (Official)
- Source
- google/skills · skills/cloud/gke-cost-optimization · commit 2194d3b
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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