Langfuse on Cloud Run — Lab Guide

Overview

Estimated time: 45–90 minutes

Langfuse is an open-source LLM engineering and observability platform — tracing, prompt management, evaluations, and metrics for applications built on large language models. This lab takes you through the full operational lifecycle of the Langfuse on Cloud Run module on Google Cloud: deploy it, sign up the first user, generate an API key, send a trace, 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 Langfuse 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, and complete the first-user signup.
Create an organization/project, generate an API key, and send your first trace.
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

Task 1 — Deploy the module [Automated]

In the RAD platform, open Langfuse (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.
The platform provisions the Cloud Run service, a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (NEXTAUTH_SECRET, SALT, and the database password), a Cloud Storage bucket, builds the container image (a thin wrapper on langfuse/langfuse:2), and runs a one-shot database-initialisation job that creates the role and database. Langfuse then applies its schema via prisma migrate deploy on first boot. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

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="$REGION" \
  --filter="metadata.name~langfuse" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(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]

Confirm the service is healthy and connected to its database. Langfuse exposes an unauthenticated health endpoint that returns 200 only when the server is fully initialised and PostgreSQL is reachable:
```
curl -s "$SERVICE_URL/api/public/health"   # expect an HTTP 200 with a small JSON body
```
Open $SERVICE_URL in a browser. On first visit Langfuse shows a Sign up page — there is no pre-seeded admin credential. Enter your name, email, and a password and submit; the first user to sign up becomes the instance owner. Log in.
After the owner account is created, consider disabling open sign-up by setting AUTH_DISABLE_SIGNUP = "true" in environment_variables and applying it via Update.

Task 3 — Create a project & send a trace [Manual]

In the Langfuse UI, create an Organization, then a Project inside it. Langfuse scopes traces, prompts, and API keys to a project.
Open Project → Settings → API Keys and click Create new API key. Copy the Public Key (pk-lf-...) and Secret Key (sk-lf-...) — the secret is shown only once.

Send your first trace directly to the public ingestion API with curl (Basic auth = public:secret). This is the same endpoint the Langfuse SDKs use:

PUBLIC_KEY="pk-lf-..."
SECRET_KEY="sk-lf-..."
TS=$(date -u +%Y-%m-%dT%H:%M:%SZ)

curl -s -u "$PUBLIC_KEY:$SECRET_KEY" \
  -X POST "$SERVICE_URL/api/public/ingestion" \
  -H "Content-Type: application/json" \
  -d '{
    "batch": [{
      "id": "'"$(uuidgen)"'",
      "type": "trace-create",
      "timestamp": "'"$TS"'",
      "body": { "id": "'"$(uuidgen)"'", "name": "lab-hello-trace", "input": "ping" }
    }]
  }'

A 207/200 response with a successes array confirms ingestion. Refresh Tracing in the UI — the lab-hello-trace entry should appear within a few seconds.

Task 4 — Operate & keep it running (Day-2) [Manual]

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"

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 = 1 and cpu_always_allocated = true so background processing keeps running between requests.
Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a new revision rolls out. Langfuse runs prisma migrate deploy on boot, so a version bump applies schema changes automatically — allow extra startup time on the first boot after an upgrade.

Manage secrets and backups:

gcloud secrets list --project="$PROJECT" --filter="name~langfuse"
gcloud run jobs list --project="$PROJECT" --region="$REGION"   # init + scheduled backup jobs

Open a database session for inspection or maintenance:

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

Task 5 — Observe: Logging & Monitoring [Manual]

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>".
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. If you enabled an uptime check, confirm it is green under Monitoring → Uptime checks, and review Alerting → Policies.

Task 6 — 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 Langfuse releases.

Revision unhealthy / Invalid environment variables: Langfuse's zod validation refuses to boot if NEXTAUTH_SECRET or SALT is missing. Confirm both secrets exist and are injected:
```
gcloud secrets list --project="$PROJECT" --filter="name~secret-key OR name~superuser-password"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
```
The startup probe targets /api/public/health and allows a generous window on first boot for Prisma migrations.
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the db-init job completed successfully.

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"

Migrations didn't run: Langfuse runs prisma migrate deploy on start (not in a separate job). If the schema looks empty, check the service logs for the migration output on boot.
Image build failed: review Cloud Build history for the failed build's log. Note the image is pinned to the v2 line via the LANGFUSE_VERSION build ARG — a v3 tag would break.
403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule never to rotate NEXTAUTH_SECRET or SALT after first boot).

Task 7 — 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. Delete removes everything the module created — the Cloud Run service, Cloud SQL database, Secret Manager secrets, GCS bucket, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run, Cloud SQL (PostgreSQL 15), secrets, storage bucket, and runs DB init
2 — Access & verify	Manual	Health check passes; sign up the first user (becomes owner) and log in
3 — Project & trace	Manual	Create an org/project, generate an API key, send a trace via curl
4 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups, DB access
5 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
6 — Troubleshoot	Manual	Diagnose secret/env, database, init-job, migration, build, and IAM issues
7 — Tear down	Automated	Delete (Trash) removes all module resources

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

Task 3 — Create a project & send a trace [Manual]​

Task 4 — Operate & keep it running (Day-2) [Manual]​

Task 5 — Observe: Logging & Monitoring [Manual]​

Task 6 — Troubleshoot & debug [Manual]​

Task 7 — Tear down [Automated]​

Summary​