gke-scaling

name	gke-scaling
description	Configures GKE autoscaling, including HPA, VPA, and Node Auto-Provisioning (NAP). Use when configuring GKE autoscaling, setting up GKE HPA, setting up GKE VPA, or configuring GKE NAP. Don't use for configuring static cluster sizes or setting node-level machine styles directly (use gke-compute-classes instead).
version	1.0.0
author	google
license	Apache-2.0
platforms	["linux","macos","windows"]
source	https://github.com/google/skills/tree/main/skills/cloud/gke-scaling
metadata	{"category":"Containers","hermes":{"icon":"lucide:container","tags":["Google Cloud","Gke","Scaling"]}}

GKE Workload Scaling

This reference covers scaling workloads on GKE. The golden path enables VPA, OPTIMIZE_UTILIZATION autoscaling profile, and Node Auto Provisioning by default.

MCP Tools: get_k8s_resource, describe_k8s_resource, apply_k8s_manifest, patch_k8s_resource, get_cluster, update_cluster, update_node_pool

Golden Path Scaling Defaults

Setting	Golden Path Value	Notes
autoscaling.autoscalingProfile	OPTIMIZE_UTILIZATION	Aggressive scale-down for cost savings
verticalPodAutoscaling.enabled	true	VPA recommendations available
autoscaling.enableNodeAutoprovisioning	true	NAP creates node pools on demand
GPU resource limits (T4, A100)	1000000000 each	NAP can provision GPU nodes

Scaling Mechanisms

1. Manual Scaling

kubectl-only — no MCP equivalent for kubectl scale. Use kubectl directly.

kubectl scale deployment <DEPLOYMENT> --replicas=<N> -n <NAMESPACE>

2. Horizontal Pod Autoscaling (HPA)

Scales the number of pods based on metrics.

Quick setup (kubectl-only — no MCP equivalent for kubectl autoscale):

kubectl autoscale deployment <DEPLOYMENT> --cpu-percent=50 --min=1 --max=10

Manifest approach (recommended — use MCP apply_k8s_manifest):

See assets/hpa-example.yaml for a template.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: <DEPLOYMENT>-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: <DEPLOYMENT>
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

3. Vertical Pod Autoscaling (VPA)

Adjusts CPU and memory requests to match actual usage. Enabled by default on golden path.

Update modes:

• Off — recommendations only (safest, start here)
• Initial — sets resources only at pod creation
• Auto — restarts pods to apply new resource values
• InPlaceOrRecreate — updates resources without restart when possible (GKE 1.34+)

Create VPA in recommendation mode:

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: <DEPLOYMENT>-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: <DEPLOYMENT>
  updatePolicy:
    updateMode: "Off"

Read recommendations (prefer MCP describe_k8s_resource):

# MCP (preferred)
describe_k8s_resource(parent="...", resourceType="verticalpodautoscaler", name="<DEPLOYMENT>-vpa", namespace="<NAMESPACE>")

# kubectl fallback
kubectl get vpa <DEPLOYMENT>-vpa -o jsonpath='{.status.recommendation}'

See assets/vpa-example.yaml for a full template.

4. Cluster Autoscaler / Node Auto Provisioning (NAP)

On Autopilot (golden path), node scaling is fully managed. NAP automatically creates and sizes node pools based on workload demands.

For Standard clusters:

# Enable cluster autoscaler on a node pool
gcloud container clusters update <CLUSTER_NAME> --region <REGION> \
  --enable-autoscaling --node-pool <POOL_NAME> \
  --min-nodes <MIN> --max-nodes <MAX> \
  --quiet

# Enable NAP
gcloud container clusters update <CLUSTER_NAME> --region <REGION> \
  --enable-autoprovisioning \
  --min-cpu <MIN_CPU> --max-cpu <MAX_CPU> \
  --min-memory <MIN_MEM> --max-memory <MAX_MEM> \
  --quiet

Autoscaling profiles:

Profile	Behavior	Golden Path?
BALANCED	Default GKE; conservative scale-down	No
OPTIMIZE_UTILIZATION	Aggressive scale-down; lower idle	Yes
: : resources : :

Best Practices

1. Define resource requests: HPA and VPA rely on accurate requests. Always set them.
2. Avoid metric conflicts: Do not use HPA and VPA on the same metric. Typical pattern: HPA on CPU, VPA on memory.
3. Pod Disruption Budgets: Define PDBs for all production workloads to ensure availability during scaling events.
4. HPA stabilization: HPA has a default 5-minute stabilization window. Tune behavior for faster response if needed.
5. VPA "Auto" caution: Auto mode restarts pods. Ensure your app handles SIGTERM gracefully. VPA requires at least 2 replicas for evictions by default.
6. Use ComputeClasses: For workload-specific node targeting (Spot fallback, GPU, specific machine families), use ComputeClasses instead of node selectors.

Rightsizing Workflow

1. Deploy VPA in Off mode for 24+ hours
2. Read recommendations: kubectl describe vpa <NAME>
3. Compare target values against current requests
4. Apply with 20% buffer: new_request = target * 1.2
5. Use patch format to update Deployment

Condition	Recommendation	Risk
CPU request >5x P95 actual	Reduce to P95 * 1.2	Medium
Memory request >3x P95 actual	Reduce to P95 * 1.2	Medium
CPU request >2x P95 actual	Rightsizing with 20% buffer	Low
No resource limits set	Add limits to prevent noisy-neighbor	Low