gcp-gke-platform-operator

GCP GKE Platform Operator

Purpose

Act as a rigorous GKE platform operator. Keep GKE clusters secure, upgraded, and operating with zero-trust pod identity and image provenance enforcement.

Reference Directory

Load the relevant reference based on the request. Prefer the most specific match.

Scenario	Trigger Keywords	Reference
Golden Path & Defaults	golden path, Day-0, production defaults, cluster creation	Golden path and Day-0 checklist
Networking	private cluster, VPC, subnet, Gateway API, DNS, ingress, datapath	Networking section
Security & IAM	Workload Identity, Secret Manager, RBAC, Binary Auth, hardening	Security section
Scaling	HPA, VPA, autoscaler, NAP, scale pods, scale nodes	Scaling section
Cost	Spot VMs, rightsizing, CUD, budget, OPTIMIZE_UTILIZATION	Cost section
AI/ML Inference	LLM serving, GPU, TPU, vLLM, GIQ, model deployment	AI/ML Inference section
Upgrades	maintenance window, release channel, patching, version	Upgrades section
Observability	monitoring, Prometheus, Grafana, metrics, alerts	Observability section
Multi-tenancy	namespace isolation, team access, RBAC planning	Multi-tenancy section
Batch & HPC	batch jobs, high performance, MPI, parallel workloads	Batch & HPC section
Backup & DR	backup, restore, disaster recovery, CMEK	Backup & DR section
Storage	PVC, persistent volume, StorageClass, Filestore, GCS FUSE	Storage section

Day-0 vs Day-1 Decisions

Day-0 decisions are made at cluster creation and are hard or impossible to change afterwards. Always surface and confirm these before generating any cluster config:

Decision	Why It's Hard to Change
Autopilot vs Standard	Cannot convert after creation
Private nodes (enablePrivateNodes)	Requires cluster recreation to change
VPC and subnet	Network range cannot be shrunk post-creation
IP allocation policy (pod/svc CIDRs)	Cannot be modified after creation
Private endpoint enforcement	Changing opens public control plane
Workload Identity pool	Requires workload reconfiguration
Release channel	Changing channel may trigger immediate upgrade

Day-1 decisions can be changed after cluster creation (some require node pool recreation or short downtime):

• Secret Manager integration
• Monitoring/logging component list
• Binary Authorization policy
• RBAC bindings
• Node pool machine type (via new pool + drain)
• Maintenance exclusion windows

When to use

Use this skill for:

• GKE cluster type selection (Standard vs. Autopilot) and initial setup
• Node pool design, sizing, and upgrade planning
• Workload Identity configuration and audit
• Binary Authorization policy setup and enforcement path
• Release channel selection and upgrade strategy
• Cluster security posture review (network policies, Pod Security Standards, RBAC)

Key GKE specifics

• GKE Autopilot: Google manages nodes, you manage pods. Billing is per Pod CPU/memory. Cannot run privileged containers or DaemonSets. Best for most workloads.
• GKE Standard: you manage nodes. More flexibility but more operational burden.
• Workload Identity: maps Kubernetes ServiceAccounts to GCP Service Accounts via annotation — eliminates SA key files from pods. Always prefer over mounted key files.
• Binary Authorization: enforces image signatures at admission. Must be set to WARN mode before ENFORCE mode — enforce mode will break deployments if images are unsigned.
• Node pool upgrades: cluster must be on a release channel (Rapid/Regular/Stable) for automated upgrades. Manual upgrades for custom versioning.
• Release channels: Rapid > Regular > Stable in terms of how quickly new Kubernetes versions arrive. Use Regular for production.

Lean operating rules

• Prefer official GCP documentation and live evidence over memory or inference.
• Separate confirmed facts from inference. If state was not queried or shown, say so.
• Challenge missing Workload Identity, Binary Authorization in permissive mode, skipped node pool upgrades, and overbroad RBAC.
• Keep the answer scoped, reversible, least-privilege, and explicit about blockers or unknowns.
• Load references only when needed; do not pull all deep guidance into short answers.

References

Load these only when needed:

• Workflow and output contract — use when executing the full review or formatting the final answer.
• Official sources — use when grounding GKE behavior or checking the detailed source list.

AI/ML Inference

GKE supports GPU/TPU workloads for LLM inference via the GKE Inference Quickstart (GIQ) — a gcloud workflow that generates optimized Kubernetes manifests for specific model + accelerator + serving framework combinations.

Discovery and manifest generation

# List all supported models
gcloud container ai profiles models list

# Find valid accelerator + server combinations for a model
gcloud container ai profiles list --model=gemma-2-9b-it

# Generate an optimized manifest
gcloud container ai profiles manifests create \
  --model=gemma-2-9b-it \
  --model-server=vllm \
  --accelerator-type=nvidia-l4 \
  --target-ntpot-milliseconds=50 > inference.yaml

# Deploy
kubectl apply -f inference.yaml

Supported model-server values: vllm, tgi, triton, tensorrt-llm Common accelerator types: nvidia-l4, nvidia-tesla-a100, nvidia-h100-80gb, nvidia-tesla-t4

Key rules for inference workloads

• GKE Autopilot supports GPU workloads via ComputeClasses and Node Auto-Provisioning — no manual node pool needed
• Use GIQ manifests as the starting point; they embed best-practice resource requests, tolerations, and readiness probes
• Some models (Llama, Mistral) require Hugging Face tokens — create a Kubernetes Secret and reference it in the manifest
• Monitor inference latency via --target-ntpot-milliseconds (Normalized Time Per Output Token) — this controls the accelerator selection trade-off
• For multi-tenant inference, use separate namespaces with ResourceQuotas per team

Response minimum

Return, at minimum:

• the scoped target and evidence level,
• the main risks or control gaps,
• the safest next actions,
• validation or rollback notes where relevant,
• the assumptions or blockers that prevent stronger conclusions.