Langfuse Production Readiness Report
Open-source tracing, evaluation, and analytics for LLM applications. Self-hostable observability for prompts and agents.
- Category
- LLM Observability
- Language
- TypeScript
- License
- MIT (core)
- Deployment
- Docker / Kubernetes
- Datastore
- Postgres + ClickHouse
- Ingest
- SDKs + OpenTelemetry
Langfuse provides tracing, evals, prompt management, and cost analytics for LLM apps. It is the observability layer most teams add once they have AI features in production and need to debug, measure, and control spend.
Recent versions add ClickHouse for high-volume analytics alongside Postgres, plus Redis and S3-compatible storage. The self-host story is solid, but you are now operating an analytics database, so size ClickHouse and retention carefully.
Production readiness score
Best for
- Tracing prompts, chains, and agent runs
- Cost and token analytics across providers
- Prompt versioning and offline/online evals
- Teams already running an LLM product
Not ideal for
- Projects with no LLM calls to observe
- Teams unwilling to operate ClickHouse at volume
- Pure infra metrics (use Prometheus/Grafana instead)
Self-hosting is well documented, but production introduces ClickHouse and an async ingestion pipeline that need monitoring and capacity planning.
Powers analytics; size disk and memory for trace volume.
Redis-backed worker processes events; watch backlog.
S3-compatible store for large payloads and media.
Define data retention to control storage growth.
- Compute
- 2–4 vCPU / 4–8 GB for web + worker
- Database
- Managed PostgreSQL 14+
- Analytics DB
- ClickHouse
- Cache / Queue
- Redis
- Storage
- S3-compatible bucket
Security considerations
- Protect ingestion API keys; scope them per project.
- Enable SSO/OIDC for the dashboard in team settings.
- Restrict ClickHouse to private networking only.
- Apply data-retention and PII redaction policies on traces.
Backup & recovery
- Back up PostgreSQL (projects, users, prompts, configs).
- Snapshot ClickHouse using its native backup tooling.
- Back up the S3 bucket holding large event payloads.
- Confirm Postgres and ClickHouse restores align on project IDs.
Monitoring
- Alert on ingestion worker lag and Redis backlog growth.
- Track ClickHouse disk usage and query latency.
- Watch event-drop metrics to catch ingestion failures.
- Monitor dashboard p95 latency for analyst experience.
Single host + managed Postgres, modest trace volume
Dedicated ClickHouse + Redis + S3
HA, long retention, SSO, support
Reference stack
Containers + Postgres + ClickHouse + Redis + S3
- 01IngressLoad balancer + TLS
Dashboard and ingestion API
- 02AppLangfuse web
UI, API, prompt management
- 03WorkerAsync ingestion worker
Processes events into ClickHouse
- 04AnalyticsClickHouse
High-volume trace analytics
- 05DataPostgres + Redis + S3
Metadata, queue, payloads
We deploy Langfuse on infrastructure you control.
Skip the trial-and-error. Our team handles architecture, security hardening, backups, monitoring, and upgrades — then hands you a production-ready, documented deployment.
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