Crawl4AI on Cloud Run — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Crawl4AI is an open-source LLM-friendly web crawler and scraper designed for AI teams building RAG pipelines, knowledge bases, and monitoring workflows. This lab takes you through the full operational lifecycle of the Crawl4AI 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 Crawl4AI 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.
• Perform day-2 operations — inspect, scale, update, and manage secrets.
• 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, 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

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Task 1 — Deploy the module [Automated]

1. In the RAD platform, open Crawl4AI (Cloud Run), 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 Gen2 service, mirrors the container image into Artifact Registry, configures VPC egress, and sets up Cloud Monitoring. Crawl4AI has no external database and no initialisation job — first deploys are faster than database-backed modules.

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="$REGION" \
     --filter="metadata.name~crawl4ai" --format="value(metadata.name)" --limit=1)
   SERVICE_URL=$(gcloud run services describe "$SERVICE" \
     --project="$PROJECT" --region="$REGION" --format="value(status.url)")
   echo "Service: $SERVICE"
   echo "URL:     $SERVICE_URL"

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Task 2 — Access & verify [Manual]

1. Confirm the service is healthy. On first request, supervisord must start Redis then Gunicorn — allow up to 60 seconds for the initial response:

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

2. Crawl4AI has no admin login and no auto-generated credentials. The service is ready when the health check above returns {"status":"ok"}. An interactive playground is available at ${SERVICE_URL}/playground in a browser — no sign-in is required by default.

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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 by changing the min/max instance inputs in the RAD platform and applying it via Update — 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).

3. Update the application version by changing the version input in the RAD UI and applying it via Update; a new image is mirrored and a new revision rolls out.

4. Manage secrets (LLM API keys and the optional JWT secret are stored in Secret Manager if supplied at deploy time):

   gcloud secrets list --project="$PROJECT" --filter="name~crawl4ai"

5. Crawl4AI is fully stateless — it has no database, no backup jobs, and no persistent storage by default. Task results live in the embedded in-container Redis and are lost on container restart. No database session is needed.

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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, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. The module also provisions an uptime check (polling /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 Crawl4AI releases.

• Revision unhealthy / service won't serve: the startup probe hits /health after a 40-second initial delay to allow supervisord to boot Redis then Gunicorn. If the revision fails to become healthy, inspect its logs for supervisord or Chromium startup errors.

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

• OOM / Chromium crashes: Chromium requires at least 4 GiB per instance. Review logs for OOM signals and increase memory_limit in the RAD platform.
• Crawls fail immediately: verify vpc_egress_setting = "ALL_TRAFFIC" — PRIVATE_RANGES_ONLY blocks all public crawl targets.
• LLM extraction returns empty results: check that any required LLM API keys were supplied via secret_environment_variables and that the secrets exist.

  gcloud secrets list --project="$PROJECT" --filter="name~crawl4ai"

• Image build / mirror failed: review Cloud Build history for the failed build's log.
• 403 / permission errors: verify the runtime service account's IAM roles.

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 — the Cloud Run service, Secret Manager secrets (if any were provisioned), and Artifact Registry images. Crawl4AI provisions no database, so there is no Cloud SQL instance to delete. Resources owned by Services_GCP (the VPC, shared registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run Gen2, mirrors image, configures VPC egress and monitoring
2 — Access & verify	Manual	Health check passes at /health; playground accessible
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision health, OOM, egress, LLM keys, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources