Shopwave chooses Autopilot regional GKE for the default path

The production cluster runs in europe-west1 with three application namespaces. Checkout and catalog are stateless; payments uses regional block storage and a stricter disruption budget.

Decision	Choice	Reason
Mode and location	Autopilot, europe-west1	Regional control plane and managed nodes reduce toil
Scale envelope	Peak 1,200 Pods; address plan sized beyond current demand	Avoid IP-led scale failure
Ingress	Global external managed Gateway with Cloud Armor	One secure entry with direct Pod backends
Identity	One Kubernetes ServiceAccount principal per service	No service-account keys
Data	Regional PD for payments; object data through Cloud Storage APIs	Match semantics and recovery objectives

The migration removes node toil, but increases the importance of precise requests, IAM and provider-edge testing.

Shopwave migration is easiest to understand by separating the Kubernetes contract from the Google Cloud implementation. The scenario deliberately chooses Autopilot because the three services fit its managed constraints. Standard remains available for a future workload that proves it needs node control. The Kubernetes objects stay familiar, but GKE supplies controllers, infrastructure and safe defaults around them. This is why a team can move from an on-premises cluster without rewriting every workload, while still needing to redesign networking, identity and operational ownership for the cloud environment.

A useful inspection step is `gcloud container clusters create-auto shopwave-prod --region europe-west1`. Read the output as evidence, not as a ritual: first confirm the desired object exists, then look at status conditions, events and the Google Cloud resource it represents. In production, capture the expected result in a runbook or automated check so an operator can distinguish slow reconciliation from a configuration error.

Production gotcha: Numbers in a worked example are planning inputs, not universal defaults; recalculate Pod density, traffic and storage from measurements. The safe habit is to verify quotas, regional availability and feature support against current Google Cloud documentation before rollout. Fleet and AI extensions should follow real requirements.