LiteLLM on Cloud Run — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

LiteLLM is an open-source LLM proxy and AI gateway that provides a unified OpenAI-compatible API across 100+ providers. This lab takes you through the full operational lifecycle of the LiteLLM 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 LiteLLM product features such as model configuration, virtual key management, or usage dashboards. 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 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, 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 LiteLLM (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, a Cloud SQL (PostgreSQL) database with its Secret Manager secrets (including the auto-generated master key and salt key), 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):

   SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~litellm" --format="value(metadata.name)" --limit=1)
   SERVICE_URL=$(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 process is running:

   curl -s -o /dev/null -w "%{http_code}" "${SERVICE_URL}/health/liveliness"
   # expect 200

2. Confirm the service is connected to its database and Prisma migrations have completed:

   curl -s "${SERVICE_URL}/health/readiness"   # expect {"status":"healthy"} or similar

3. Retrieve the master key from Secret Manager and sign in to the Admin UI at ${SERVICE_URL}/ui:

   MASTER_KEY_SECRET=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~litellm-master-key" --format="value(name)" --limit=1)
   gcloud secrets versions access latest --secret="$MASTER_KEY_SECRET" --project="$PROJECT"

   The returned value (prefixed sk-) is the admin credential for LiteLLM. The LiteLLM product documentation covers model configuration, virtual key management, and usage dashboards.

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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 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).

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.

4. Manage secrets and backups:

   gcloud secrets list --project="$PROJECT" --filter="name~litellm"
   gcloud run jobs list --project="$PROJECT" --region="$REGION"   # db-init and any scheduled jobs

5. Open a database session for inspection or maintenance:

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

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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; 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 LiteLLM releases.

• Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved. The startup probe targets /health/readiness; a failing probe typically means the database connection or Prisma migration did not complete.

  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 enable_cloudsql_volume = true (the Auth Proxy sidecar is required; disabling it breaks the database connection).
• Initialisation job failed: list executions and read the failed one's logs:

  gcloud run jobs executions list --job="${SERVICE}-db-init" \
    --project="$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.

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, Cloud SQL database, Secret Manager secrets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, 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 Cloud Run, Cloud SQL, secrets, and runs DB init
2 — Access & verify	Manual	Liveness and readiness checks pass; retrieve master key and sign in
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, init-job, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources