Crawl4AI on GKE Autopilot — 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 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 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.
• Connect to the GKE cluster and access the running workload.
• Perform day-2 operations — inspect, scale, update, and manage secrets.
• 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 deployed in the target project (provides the VPC, GKE Autopilot cluster, Artifact Registry, and shared service accounts this module depends on).
• A Google Cloud project with billing enabled.
• gcloud CLI and kubectl installed; gcloud auth login and gcloud auth application-default login completed.
• 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 Crawl4AI (GKE) 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 deploys the workload into the GKE Autopilot cluster, mirrors the container image into Artifact Registry, provisions a Horizontal Pod Autoscaler, and exposes the service via a LoadBalancer. Crawl4AI has no external database and no initialisation job — first deploys complete faster than database-backed modules.

3. Connect to the cluster and discover the namespace with name-agnostic filters:

   CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
   gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"
   
   NS=$(kubectl get ns -o name | grep crawl4ai | head -1 | cut -d/ -f2)
   echo "Cluster: $CLUSTER   Namespace: $NS"
   kubectl get all -n "$NS"

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Task 2 — Access & verify [Manual]

1. Confirm the workload is running and find its external address. On first pod start, supervisord must boot Redis then Gunicorn — allow up to 60 seconds before the health check responds:

   kubectl get pods,svc -n "$NS"
   EXTERNAL_IP=$(kubectl get svc -n "$NS" \
     -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
   echo "External IP: $EXTERNAL_IP"
   curl -s "http://${EXTERNAL_IP}:11235/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 http://${EXTERNAL_IP}:11235/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 workload — deployment, pods, HPA, and (if enabled) persistent volumes:

   kubectl get deploy,pods,hpa,pvc -n "$NS"
   kubectl describe deploy -n "$NS"

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

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 rolling update replaces the pods.

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

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$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-pod Redis and are lost on pod restart. No database session is needed.

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Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from kubectl or the Logs Explorer:

   kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" --tail=50

   Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and HPA scaling events. The module can also provision an uptime check (polling /health); when enabled, review Monitoring → Uptime checks and 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.

• Pod not Ready / CrashLoopBackOff: the startup probe hits /health after a 40-second initial delay to allow supervisord to boot Redis then Gunicorn. Inspect events and logs for startup errors:

  kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
  kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container

• OOM / Chromium crashes: Chromium requires at least 4 GiB per pod. Review logs for OOMKilled events and increase container_resources.memory_limit in the RAD platform.
• Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP (kubectl get svc -n "$NS").
• LLM extraction returns empty results: check that any required LLM API keys were supplied via secret_environment_variables and that the secrets have materialised into the namespace.

  gcloud secrets list --project="$PROJECT" --filter="name~crawl4ai"
  kubectl get secrets -n "$NS"

• Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it.

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 Kubernetes workload and namespace, 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, GKE cluster, shared registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, mirrors image, provisions HPA and LoadBalancer
2 — Access & verify	Manual	Connect to the cluster; health check passes at /health; playground accessible
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod health, OOM, scheduling, LLM keys, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources