# Gke Cost Optimization > Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead). - Skill: `tuyv/gke-cost-optimization` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add tuyv/gke-cost-optimization` - Raw SKILL.md: https://api.skillmd.com/api/skills/tuyv/gke-cost-optimization/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tuyv (https://skillmd.com/u/tuyv) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tuyv/gke-cost-optimization --- # GKE Cost Optimization This reference covers strategies and workflows for reducing Google Kubernetes Engine (GKE) costs while maintaining a secure and reliable posture. ## 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](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): ```bash # 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: ```yaml nodeSelector: cloud.google.com/gke-spot: "true" ``` Full worked Deployment (replicas >= 2, `terminationGracePeriodSeconds: 25`, `preStop` hook): [assets/spot-deployment-example.yaml](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-autoscaler`** skill. - **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.