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Uptime Kuma on Cloud Run — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Uptime Kuma is a self-hosted uptime monitoring tool for websites, APIs, TCP ports, and DNS records, with status pages and 90+ notification channels. This lab takes you through the full operational lifecycle of the Uptime Kuma on Cloud Run 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 Cloud Run module and the Google Cloud platform, not on Uptime Kuma 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 the service, complete the first-run admin setup, and verify health.
  • Explain why Uptime Kuma needs always-allocated CPU and a running instance to monitor.
  • Perform day-2 operations — inspect revisions, scale, update, and check the NFS-backed state.
  • 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, Filestore/NFS networking, 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]​

  1. Click Deploy in the RAD platform top navigation, open UptimeKuma (Cloud Run) from the Platform Modules list to start configuration, 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.

  2. The platform provisions the Cloud Run service with CPU always allocated (the check scheduler runs between requests), a Filestore NFS share mounted at /app/data for the embedded SQLite database, and mirrors the official louislam/uptime-kuma image into Artifact Registry. There is no Cloud SQL instance, no application secret, and no initialization job — this is one of the fastest modules to deploy (typically 10–15 minutes; no database provisioning).

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

    SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
      --filter="metadata.name~uptimekuma" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(gcloud run services describe "$SERVICE" \
      --project="$PROJECT" --region="$REGION" --format="value(status.url)")
    echo "Service: $SERVICE"
    echo "URL:     $SERVICE_URL"

Task 2 — Access & verify [Manual]​

  1. Confirm the service is healthy. Uptime Kuma's health path is / on port 3001, which returns HTTP 200 once the Node.js server is up (first boot also creates the SQLite schema on the NFS volume — allow up to a minute on a fresh deploy):

    curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/"

  2. Open $SERVICE_URL in a browser. On first access Uptime Kuma presents its setup page — create the admin account (there are no default credentials baked into the image). Do this immediately: until the admin account exists, the setup page is publicly reachable at the run.app URL. The account is stored in SQLite on the NFS volume, so it survives restarts and revisions.

  3. Add a first monitor (e.g. an HTTPS check against a site you own) and watch it turn green. Note there is no secret to retrieve — the module creates no Secret Manager entries:

    gcloud secrets list --project="$PROJECT" --filter="name~uptimekuma"   # expect empty

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

  1. Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

    gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

  2. Scale — the setting that matters most for this app. With the default min_instance_count = 0, the service scales to zero when idle and no checks run while it is down. For genuine 24/7 monitoring, set min_instance_count = 1 and max_instance_count = 1 (SQLite is single-writer) and click Update on the deployment details page — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (a manual edit would be reverted on the next apply). Keep cpu_always_allocated = true: without allocated CPU the in-process scheduler is throttled between requests and checks stall.

  3. Update the application version by changing the version input via Update on the deployment details page; the new image tag is mirrored and a new revision rolls out. Monitors and history persist on the NFS volume.

  4. Verify the persistent state lives on NFS, not ephemeral disk:

    gcloud filestore instances list --project="$PROJECT"
    gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION" \
      --format="yaml(spec.template.spec.volumes)"

  5. No database session to open — there is no Cloud SQL instance (database_type = "NONE"); all state is the SQLite file under /app/data. Back it up by exporting from the Uptime Kuma UI (Settings → Backup) or by snapshotting the Filestore share.

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

  1. Logs — from the CLI or the Logs Explorer:

    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50

    Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".

  2. Monitoring — open the Cloud Run dashboard for the service and review request count, latency, instance count (with min = 1 it should never drop to 0 — if it does, your monitoring has gaps), and CPU / memory utilisation. Because CPU is always allocated, expect a steady low CPU baseline even with no dashboard traffic — that is the check scheduler working.

  3. Monitor the monitor. Optionally enable the module's uptime_check_config (disabled by default) so Google Cloud Monitoring probes Uptime Kuma itself — an outside-in signal that your monitoring system is up.

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 Uptime Kuma releases.

  • Revision unhealthy / service won't serve: inspect the latest revision and its logs; the startup probe allows generous time for first boot: 
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
  • Monitors show gaps / alerts arrive late: the classic Uptime Kuma-on-Cloud-Run failure. Check min_instance_count (0 means checks pause when idle) and cpu_always_allocated (must be true; request-based billing throttles the scheduler). Instance-count dropping to zero in Monitoring confirms it.
  • Data lost after a restart or update: verify enable_nfs = true, the mount path is exactly /app/data, and the execution environment is gen2 (required for Filestore mounts in Cloud Run). If the volume list in Task 3.4 is empty, Uptime Kuma has been writing to ephemeral disk.
  • SQLite lock errors / flapping under load: more than one instance is writing the database over NFS — set max_instance_count = 1.
  • Cannot reach monitored private targets: probes to RFC-1918 addresses need VPC egress; the default vpc_egress_setting = "PRIVATE_RANGES_ONLY" covers this. For probes that must egress via the VPC/NAT (stable source IP for allow-listed targets), set ALL_TRAFFIC.
  • Image pull failed: confirm enable_image_mirroring = true and check the mirrored copy in Artifact Registry (gcloud artifacts docker images list ...). There is no Cloud Build step to debug — the image is prebuilt.
  • 403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

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). This removes everything the module created — the Cloud Run service, the Filestore NFS share (including the SQLite database with all monitors and history — export a backup first if you want to keep them), and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions Cloud Run (CPU always allocated), NFS at /app/data, and mirrors the prebuilt image — no DB, secrets, or init jobs
2 — Access & verifyManualHealth check passes; admin account created on the first-run setup page
3 — OperateManualInspect revisions, set min=1/max=1 for 24/7 single-writer monitoring, update version, verify NFS state
4 — ObserveManualQuery Cloud Logging; watch instance count and CPU baseline; optionally monitor the monitor
5 — TroubleshootManualDiagnose revision, scale-to-zero gaps, NFS/data-loss, SQLite lock, egress, and IAM issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

By · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

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