Infisical on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–75 minutes

Infisical is an open-source, end-to-end encrypted secrets management platform: teams and CI/CD pipelines store, inject, and rotate application secrets from a single platform. This lab takes you through the full operational lifecycle of the Infisical 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 Infisical product 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 service, and bootstrap the first admin account.
Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
Observe the service with Cloud Logging and Cloud Monitoring.
Diagnose and resolve the most common deployment and runtime issues.
Tear the deployment down cleanly.

Prerequisites

Services_GCP deployed in the target project (provides the VPC, GKE Autopilot cluster, Cloud SQL, Artifact Registry, and shared service accounts this module depends on).
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.
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

Task 1 — Deploy the module [Automated]

In the RAD platform, open Infisical (GKE), set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Review the estimated cost (if credits are enabled) and click Deploy, which opens the deployment status page with real-time logs.
The platform provisions the GKE workload (a stateless Deployment behind a LoadBalancer Service with a reserved static IP), a Cloud SQL (PostgreSQL 15) database, its Secret Manager secrets (ENCRYPTION_KEY, AUTH_SECRET, ADMIN_PASSWORD, and the database password), builds the custom container image, and runs the db-init job. First deploys take roughly 20–35 minutes (Cloud SQL creation, the custom image build, and the external IP reservation dominate).

When it completes, get cluster credentials and discover the resources:

CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"

NAMESPACE=$(kubectl get ns -o name | grep infisical | sed 's|namespace/||' | head -1)
SERVICE=$(kubectl get svc -n "$NAMESPACE" -o name | grep infisical | sed 's|service/||' | head -1)
EXTERNAL_IP=$(kubectl get svc "$SERVICE" -n "$NAMESPACE" \
  -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
echo "Namespace: $NAMESPACE"
echo "Service:   $SERVICE"
echo "External IP: $EXTERNAL_IP"

Task 2 — Access & verify [Manual]

Confirm the service is healthy and connected to its database. Infisical exposes an unauthenticated status endpoint:
```
curl -s "http://$EXTERNAL_IP/api/status"   # expect HTTP 200 with a JSON body
```
Set site_url before the admin account can be bootstrapped. Unlike the Cloud Run variant, GKE does not auto-compute a predicted URL — the admin-bootstrap job targets site_url verbatim, and if it was left empty at first deploy it defaults to an unreachable http://localhost:8080 inside the job's own pod. Now that the external IP is known, set site_url = "http://<EXTERNAL_IP>" (or your custom domain) on the deployment and apply Update. The admin-bootstrap job's pod retries automatically (up to 20 attempts, 15 seconds apart) once the target is reachable — no separate manual trigger is needed on GKE.
Retrieve the generated admin password and log in at http://$EXTERNAL_IP:
```
gcloud secrets versions access latest \
  --secret="$(gcloud secrets list --project="$PROJECT" \
    --filter="name~infisical-admin-password" --format='value(name)')" \
  --project="$PROJECT"
```
The admin email is the module's admin_email input (default admin@techequity.cloud). If the account still doesn't exist, check the job's pod logs (Task 5).

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

Inspect the workload and its pods:

kubectl get pods,svc,hpa -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 edit.
Update the application version tag by changing application_version in the RAD platform and applying it via Update; a new image builds and a new rollout begins. "latest" maps to a pinned known-good release as the Dockerfile build arg.

Manage secrets and jobs:

gcloud secrets list --project="$PROJECT" --filter="name~infisical"
kubectl get jobs -n "$NAMESPACE"   # db-init + admin-bootstrap

Open a database session for inspection or maintenance:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud sql connect "$INSTANCE" --user=infisical --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from the CLI or the Logs Explorer:

gcloud logging read 'resource.type="k8s_container" AND resource.labels.namespace_name="'"$NAMESPACE"'"' \
  --project="$PROJECT" --limit 50

Monitoring — review GKE workload metrics (CPU/memory utilisation, HPA scaling) and Cloud SQL metrics. The module can provision an uptime check via uptime_check_config; 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 Infisical releases.

Pod not Ready / CrashLoopBackOff: the startup and liveness probes target HTTP /api/status, which only returns 2xx once the database (and Redis, if enabled) connections are healthy — allow the default generous delay/threshold on first boot.
```
kubectl describe pod -n "$NAMESPACE" -l app=infisical
kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=200
```
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the db-init job completed. Confirm the Cloud SQL Auth Proxy sidecar container is running (enable_cloudsql_volume = true).
admin-bootstrap never creates an account: check the job's pod logs — the most common cause is site_url left empty or pointing at an unreachable address (see Task 2).
```
kubectl logs -n "$NAMESPACE" job/"${SERVICE}-admin-bootstrap"
```
REDIS_URL / REDIS_SENTINEL_HOSTS / REDIS_CLUSTER_HOSTS must be defined crash on boot: confirm enable_redis was forwarded correctly and, if redis_auth is set, that the Redis secret propagated.
Image build failed: review Cloud Build history for the failed build's log.
IAM / Workload Identity errors: verify the workload service account's IAM roles and Workload Identity binding.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule never to rotate ENCRYPTION_KEY after first boot, and the site_url requirement above).

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 — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the project). Delete removes everything the module created — the GKE workload and Service, the reserved static IP, Cloud SQL database, and Secret Manager secrets. 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 provisions the GKE workload, Cloud SQL (PostgreSQL 15), secrets, builds the custom image, and runs `db-init`
2 — Access & verify	Manual	Health check passes; set `site_url` to the external IP so `admin-bootstrap` can succeed, then log in with the generated admin password
3 — Operate	Manual	Inspect pods, scale, update version, manage secrets/jobs, DB access
4 — Observe	Manual	Query Cloud Logging; review GKE/Cloud SQL metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

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​