Dify on Cloud Run — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Dify is an open-source LLM application development platform for building production-grade AI applications with a visual workflow builder, RAG pipeline, agent framework, and multi-model management. This lab takes you through the full operational lifecycle of the Dify 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 Dify 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, including the web frontend.
• 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, Cloud SQL, Artifact Registry, Filestore NFS, 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

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Task 1 — Deploy the module [Automated]

1. Click Deploy in the RAD platform top navigation, open Dify (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 two Cloud Run services (the API+Celery service and the Next.js web frontend), a Cloud SQL (PostgreSQL 15 with pgvector) database with its Secret Manager secrets, a dedicated GCS storage bucket, Redis via the NFS server, builds the container image, and runs a one-shot database-initialisation job. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

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

   # API service
   SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~dify" \
     --format="value(metadata.name)" --limit=1)
   API_URL=$(gcloud run services describe "$SERVICE" \
     --project="$PROJECT" --region="$REGION" --format="value(status.url)")
   
   # Web frontend service (the URL to open in a browser)
   WEB_SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~dify-web" --format="value(metadata.name)" --limit=1)
   WEB_URL=$(gcloud run services describe "$WEB_SERVICE" \
     --project="$PROJECT" --region="$REGION" --format="value(status.url)")
   
   echo "API service: $SERVICE   ($API_URL)"
   echo "Web service: $WEB_SERVICE   ($WEB_URL)"

---

Task 2 — Access & verify [Manual]

1. Confirm the API service is healthy and connected to its database:

   curl -s "$API_URL/health"   # expect {"status":"ok"}

2. Dify does not store a pre-generated admin password in Secret Manager. On the first visit the application displays a setup wizard where you create the admin account. Open the web frontend URL in a browser and complete the setup:

   echo "$WEB_URL"

   Enter your admin email and a password when prompted. After completing setup, the Dify console opens. Dify's own product documentation covers the workflow builder, RAG pipeline, and LLM provider configuration.

3. Confirm both services are listed and the SECRET_KEY (session signing key) is present in Secret Manager:

   gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~dify"
   
   gcloud secrets list --project="$PROJECT" --filter="name~dify AND name~secret-key"

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

1. Inspect the services and their 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 by changing the min/max instance inputs and clicking 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 min_instance_count at 1 or higher so the embedded Celery worker maintains its Redis broker connection.

3. Update the application version by changing the version input via Update on the deployment details page; a new image builds and a new revision rolls out to both services.

4. Manage secrets, storage, and jobs:

   gcloud secrets list --project="$PROJECT" --filter="name~dify"
   gcloud storage buckets list --project="$PROJECT" --filter="name~dify"
   gcloud run jobs list --project="$PROJECT" --region="$REGION"   # db-init and backup jobs

5. Open a database session for inspection or maintenance:

   INSTANCE=$(gcloud sql instances list --project="$PROJECT" \
     --filter="name~dify" --format="value(name)" --limit=1)
   gcloud sql connect "$INSTANCE" --user=dify_user --project="$PROJECT"

---

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 each service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. The module also provisions an uptime check targeting /health; confirm it is green under Monitoring → Uptime checks, and review Alerting → Policies.

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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 Dify releases.

• Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors. The API server and database migrations run on every start, so allow the configured startup probe delay before expecting traffic.

  gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
  gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100

• Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists and is injected, and the db-init job completed. Without the Cloud SQL Auth Proxy sidecar (enable_cloudsql_volume = true) the container cannot reach the database at all.
• Celery tasks not running / async failures: Redis is required for all background processing. Confirm NFS is enabled (required when no external Redis host is set) and that the NFS_SERVER_IP placeholder resolved correctly in the service environment.
• Initialisation job failed: list executions and read the failed one's logs:

  JOB=$(gcloud run jobs list --project="$PROJECT" --region="$REGION" \
    --filter="metadata.name~dify AND metadata.name~db-init" \
    --format="value(metadata.name)" --limit=1)
  gcloud run jobs executions list --job="$JOB" --project="$PROJECT" --region="$REGION"

• Image build failed: review Cloud Build history for the failed build's log.
• 403 / permission errors: verify the runtime service account's IAM roles and that the GCS storage bucket and Secret Manager secrets are accessible.

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

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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 — both Cloud Run services, the Cloud SQL database, the GCS storage bucket, Secret Manager secrets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, Filestore NFS, shared Cloud SQL, registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions two Cloud Run services, Cloud SQL (PostgreSQL + pgvector), secrets, GCS bucket, and runs DB init
2 — Access & verify	Manual	Health check passes; complete the admin setup wizard via the web frontend
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, Celery/Redis, init-job, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources