Gke cluster creation

GKE Cluster Creation

This reference guides creating GKE clusters. The golden path Autopilot configuration is the default for all new clusters.

MCP Tools: list_clusters, create_cluster, get_cluster, list_operations, get_operation

Workflow

1. Discover context: Use list_clusters to see existing clusters. Use gcloud config get-value project if project unknown.
2. Gather inputs: project_id, region, cluster_name, environment type
3. Select mode: Autopilot (default) vs Standard
4. Configure networking: auto-create subnet (default) or bring-your-own
5. Review golden path settings: present the config and confirm with user
6. Create: Use MCP create_cluster tool. Fall back to gcloud CLI only if MCP is unavailable.
7. Track: Use get_operation to monitor creation progress
8. Verify: Use get_cluster with readMask="*" to confirm golden path settings applied

Mode Selection

Criteria	Autopilot (Golden Path)	Standard
Node management	Google-managed	Self-managed
Pricing	Pay per pod resource	Pay per node (VM)
: : request : :
Node customization	Via ComputeClasses	Full control
DaemonSets	Allowed (with	Full control
: : restrictions) : :
GPU/TPU	Supported via	Supported via node pools
: : ComputeClasses : :
Best for	Most production workloads	Kernel tuning, custom OS,
: : : privileged workloads :

Rule: Default to Autopilot unless the customer has a specific requirement that Autopilot cannot satisfy.

Templates

1. Golden Path Autopilot (Production)

This is the default. All settings match ../gke-golden-path/assets/golden-path-autopilot.yaml.

Via gcloud:

gcloud container clusters create-auto <CLUSTER_NAME> \
  --region <REGION> \
  --project <PROJECT_ID> \
  --release-channel regular \
  --enable-private-nodes \
  --enable-master-authorized-networks \
  --enable-dns-access \
  --enable-secret-manager \
  --secret-manager-rotation-interval=120s \
  --scoped-rbs-bindings \
  --monitoring=SYSTEM,API_SERVER,SCHEDULER,CONTROLLER_MANAGER,STORAGE,POD,DEPLOYMENT,STATEFULSET,DAEMONSET,HPA,CADVISOR,KUBELET,DCGM \
  --quiet

Via MCP (create_cluster):

{
  "parent": "projects/<PROJECT_ID>/locations/<REGION>",
  "cluster": {
    "name": "<CLUSTER_NAME>",
    "autopilot": { "enabled": true },
    "privateClusterConfig": { "enablePrivateNodes": true },
    "masterAuthorizedNetworksConfig": {
      "privateEndpointEnforcementEnabled": true
    },
    "releaseChannel": { "channel": "REGULAR" },
    "secretManagerConfig": {
      "enabled": true,
      "rotationConfig": { "enabled": true, "rotationInterval": "120s" }
    },
    "rbacBindingConfig": {
      "enableInsecureBindingSystemAuthenticated": false,
      "enableInsecureBindingSystemUnauthenticated": false
    }
  }
}

2. Autopilot Dev/Test

Relaxes some golden path defaults for cost savings and easier access in non-production.

gcloud container clusters create-auto <CLUSTER_NAME> \
  --region <REGION> \
  --project <PROJECT_ID> \
  --release-channel rapid \
  --quiet

Warning: This does not apply golden path security hardening. Suitable for dev/test only.

3. Standard Regional (When Autopilot is Not an Option)

gcloud container clusters create <CLUSTER_NAME> \
  --region <REGION> \
  --project <PROJECT_ID> \
  --num-nodes 3 \
  --machine-type e2-standard-4 \
  --disk-type pd-balanced \
  --enable-autoscaling --min-nodes 1 --max-nodes 10 \
  --enable-shielded-nodes --enable-secure-boot \
  --workload-pool=<PROJECT_ID>.svc.id.goog \
  --enable-private-nodes \
  --enable-master-authorized-networks \
  --enable-vertical-pod-autoscaling \
  --enable-dataplane-v2 \
  --release-channel regular \
  --quiet

4. GPU/AI Workloads (Autopilot with ComputeClass)

Create a golden path Autopilot cluster, then apply a ComputeClass for GPU workloads:

# 1. Create golden path cluster (same as template 1)
gcloud container clusters create-auto <CLUSTER_NAME> \
  --region <REGION> --project <PROJECT_ID> \
  --enable-private-nodes --enable-master-authorized-networks \
  --enable-dns-access --enable-secret-manager --scoped-rbs-bindings \
  --quiet

# 2. Apply GPU ComputeClass (see gke-compute-classes.md)
kubectl apply -f gpu-compute-class.yaml

# 3. Or use GIQ for inference (see gke-inference.md)
gcloud container ai profiles manifests create \
  --model=gemma-2-9b-it --model-server=vllm --accelerator-type=nvidia-l4 --quiet > inference.yaml
kubectl apply -f inference.yaml

Instructions

• ALWAYS ask for project_id if not in context
• ALWAYS ask for region
• ALWAYS ask for a unique cluster_name
• DEFAULT to golden path Autopilot unless customer specifies otherwise
• WARN about Day-0 decisions (networking, private nodes) that are hard to change later
• WARN about cost for GPU or multi-region clusters
• When using MCP create_cluster, the cluster.name should be the short name (e.g., my-cluster), not the full resource path