gke-cost-optimization

SKILL.md

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
(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-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.