Payload CMS on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Payload CMS is a TypeScript-native, code-first headless CMS and application framework built directly on Next.js. Unlike most modules in this catalogue, there is no official Payload Docker image — this module builds a real, locally-verified starter application from source (a blank create-payload-app template using the PostgreSQL adapter). This lab takes you through the full operational lifecycle of the Payload on GKE Autopilot module on Google Cloud: deploy it, access and verify it, run it day-to-day, observe it, diagnose common problems, and tear it down.

The lab focuses on operating the GKE module and the Google Cloud platform, not on Payload's own content-modeling features. For the complete list of provisioned services and every configuration input (organised by group), see the Configuration Guide — this lab deliberately does not duplicate that detail so it stays accurate over time.

Objectives

By the end of this lab you will be able to:

Deploy the module from the RAD platform and locate the resources it provisions.
Access and verify the running workload, including creating the first Payload admin account.
Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
Observe the workload with Cloud Logging and Cloud Monitoring.
Diagnose and resolve the most common deployment and runtime issues.
Tear the deployment down cleanly.

Prerequisites

Services_GCP (provides the VPC, GKE Autopilot cluster, Cloud SQL, Artifact Registry, and shared service accounts this module depends on). You do not need to deploy this yourself first — the platform automatically detects whether it already exists in the target project and provisions it before this module if not (see Task 1).
A Google Cloud project with billing enabled.
gcloud CLI authenticated: gcloud auth login and gcloud auth application-default login.
Project Owner (or equivalent) IAM on the project.
Bringing your own project? Before the first deploy into it, the deployment confirmation dialog asks you to prove you control it (Get verification code, run the commands it shows as a project Owner, then Verify) and to give the RAD deployment service account the Owner role. A project RAD creates for you needs neither.
Advanced mode for later changes. The create form asks only for the first page of inputs (and, in a project RAD creates for you, little more than the tenant name and region). Every other input in the Configuration Guide — including the scaling and version inputs in the Day-2 tasks — is changed afterwards with Update on the deployment's page after ticking Enable advanced mode, which needs a credit balance that covers the update's estimated build cost (updates never carry a module fee). On a lab environment only an administrator can use Advanced mode.
RAD platform access with permission to deploy modules into the project.

Set these shell variables once; every task below reuses them:

export PROJECT="<your-gcp-project-id>"
export REGION="us-central1"          # the region you deploy into
export NAMESPACE="<deployment-namespace>"   # reported in the deployment outputs
gcloud container clusters get-credentials <cluster-name> --region "$REGION" --project "$PROJECT"

Task 1 — Deploy the module [Automated]

In the RAD platform, open Solutions → Solution Catalog → RAD modules, then open Payload (GKE) from the Platform Modules list, choose Configuration Form under How would you like to configure this deployment? (the form opens on the Conversational Assistant if you hold purchased credits or are a partner or administrator), set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Click Deploy Module, review the estimated cost in the Deployment Confirmation dialog when it appears and click Submit (if the dialog then adds a confirmation step, such as verifying a project you bring, complete it and click Confirm), which opens the deployment status page with real-time logs.
The platform provisions the GKE workload (Deployment + Service), a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (PAYLOAD_SECRET and the database password), builds the Payload application from source via Cloud Build (there is no prebuilt image to pull), and runs two sequential jobs: db-init (creates the database role and database) followed by payload-migrate (applies the Payload schema — this needs the full application source and dependency tree, not just the trimmed runtime that serves traffic). First deploys take roughly 20–35 minutes (Cloud SQL creation and the from-source Cloud Build dominate).

When it completes, discover the resources with name-agnostic filters (so the commands keep working regardless of the deployment suffix):

kubectl get deploy,svc,jobs -n "$NAMESPACE" | grep -i payload
SERVICE=$(kubectl get svc -n "$NAMESPACE" -o name | grep -i payload | head -1 | cut -d/ -f2)
echo "Service: $SERVICE"

Task 2 — Access & verify [Manual]

Confirm the pod is healthy:

kubectl get pods -n "$NAMESPACE" | grep -i payload   # expect N/N Running, 0 restarts

Determine how the Service is exposed and reach /admin. This deployment may be running with service_type = "ClusterIP" (typically because the project's static IP quota was exhausted at deploy time) rather than the module's default LoadBalancer:

# If LoadBalancer with an external IP:
EXTERNAL_IP=$(kubectl get svc "$SERVICE" -n "$NAMESPACE" -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
curl -s -o /dev/null -w '%{http_code}\n' "http://$EXTERNAL_IP/admin"   # expect 200

# If ClusterIP (no external IP) — port-forward instead:
kubectl port-forward -n "$NAMESPACE" svc/"$SERVICE" 18080:3000
curl -s -o /dev/null -w '%{http_code}\n' "http://localhost:18080/admin"   # expect 200

Open /admin in a browser (via the external IP, or http://localhost:18080/admin if port-forwarding). Payload has no CLI command to create the first admin user non-interactively — with an empty users collection, Payload automatically shows a signup form. Fill in your email and a password and submit to create the first administrator; you are then logged into the admin dashboard. This is a required, one-time manual step — there is no pre-seeded admin credential in Secret Manager.
If the Service is ClusterIP and you want public access, flip service_type to LoadBalancer (or reserve a static IP) in the platform once IP quota is available, and re-apply.

Task 3 — Operate & keep it running (Day-2) [Manual]

Inspect the workload and its pods:

kubectl get pods,svc -n "$NAMESPACE"
kubectl describe deploy "$SERVICE" -n "$NAMESPACE"
kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=100

Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the workload spec, so scaling is a configuration change, not a manual kubectl scale (a manual edit would be reverted on the next apply).
Update the application version tag by changing the version input in the RAD platform and applying it via Update; a new Cloud Build run rebuilds the bundled Payload starter app from source and rolls out a new pod generation (there is no upstream image tag to bump — this always rebuilds).

Manage secrets and backups:

gcloud secrets list --project="$PROJECT" --filter="name~payload"
kubectl get jobs -n "$NAMESPACE"   # db-init + payload-migrate + scheduled backup jobs

Open a database session for inspection or maintenance:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
# Role and database are tenant-prefixed (e.g. payloaddemo426161cf) — not the bare app name.
DB_USER=$(gcloud sql users list --instance="$INSTANCE" --project="$PROJECT" \
  --format="value(name)" --filter="name~^payload" --limit=1)
gcloud sql connect "$INSTANCE" --user="$DB_USER" --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from the CLI or the Logs Explorer:

kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=100
gcloud logging read 'resource.type="k8s_container" AND resource.labels.namespace_name="'"$NAMESPACE"'"' \
  --project "$PROJECT" --limit 50

Monitoring — open the GKE Workloads dashboard for the deployment and review pod restarts, CPU / memory utilisation, and (for Cloud SQL) query and connection metrics. The module can provision an uptime check (when uptime_check_config.enabled = true — it defaults to false, and requires a reachable external IP); if enabled, confirm it is green under Monitoring → Uptime checks.

Task 5 — Troubleshoot & debug [Manual]

Durable techniques for the failure modes you are most likely to hit. These are platform-level diagnostics and do not change with Payload releases.

Pod unhealthy / won't become Ready: inspect the pod and its logs for startup errors. The startup probe targets /admin and allows roughly 12 minutes on first boot for the payload-migrate job to complete.
```
kubectl describe pod -n "$NAMESPACE" -l app=payload
kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=200
```
Database queries fail / "relation does not exist": the schema was never applied. Confirm payload-migrate completed successfully (it depends on db-init finishing first):
```
kubectl get jobs -n "$NAMESPACE"
kubectl logs -n "$NAMESPACE" job/payload-migrate
```
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the Cloud SQL Auth Proxy sidecar container is running in the pod (kubectl get pod <pod> -n "$NAMESPACE" -o jsonpath='{.spec.containers[*].name}').
Image build failed: review Cloud Build history for the failed build's log — remember this module always builds from source, so a broken Dockerfile or dependency change surfaces here, not as an image-pull error.
```
gcloud builds list --project="$PROJECT" --limit=10
```
No external IP / can't reach the service: confirm service_type. If it is ClusterIP (often a deliberate fallback when the project's static IP quota was exhausted), use kubectl port-forward as shown in Task 2 rather than assuming the deployment is broken.
First admin account missing / can't log in: the first admin is created manually via the /admin signup form — there is no pre-seeded credential in Secret Manager to fall back to.
403 / permission errors: verify the Workload Identity service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including why enable_gcs_storage and the Redis variables have no effect on this module).

Task 6 — Tear down [Automated]

On the Deployments page, open the deployment and click the Trash icon (Delete). Delete runs terraform destroy and is irreversible (the deployment record is retained for history). If a deployment is stuck and the RAD platform can no longer manage it (for example after manual changes that conflict with the Terraform state), use Purge instead (from the same Delete dialog) — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the deployment). This removes everything the module created — the GKE workload, Service, Cloud SQL database, Secret Manager secrets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module builds the Payload app from source via Cloud Build and provisions GKE workload, Cloud SQL (PostgreSQL 15), secrets, and runs `db-init` → `payload-migrate`
2 — Access & verify	Manual	Pod is Ready; reach `/admin` (LoadBalancer IP or `kubectl port-forward`) and create the first admin account
3 — Operate	Manual	Inspect pods, scale, update version (rebuilds from source), manage secrets/backups, DB access
4 — Observe	Manual	Query Cloud Logging; review GKE Workloads dashboard and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, migration-job, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

Need RAD to do something it does not do yet? Request it on the roadmap, or vote on what is already there.

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

Task 3 — Operate & keep it running (Day-2) [Manual]​

Task 4 — Observe: Logging & Monitoring [Manual]​

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​