Langfuse
Trace, evaluate, and improve AI agents with one open platform. Use production data to optimize cost, latency, and quality.
Editor's Verdict
Key Takeaways
- LLM Observability
- Prompt Management
- Evaluation
- Metrics
In-Depth Review: What is Langfuse?
Langfuse connects tracing, monitoring, datasets, experiments, and evaluation in a continuous loop. Supports 100+ integrations, OTel native, scalable to billions of events. Used by 21 of Fortune 50, 100k+ engineers. Self-host or cloud. Start free.
Core Features
LLM Observability
Hierarchical traces capture every LLM call, tool invocation, and retrieval step. Filter by user, session, cost, latency, or custom metadata.
Prompt Management
Separate prompts from code with one-click deployments and rollbacks. Turn improving production prompts into a team sport.
Evaluation
LLM-as-a-judge, heuristic functions, or human review. Run evaluators on production data or during experiments.
Metrics
Monitor cost, latency, and quality with dashboards and automated alerts.
Playground
Test prompts on real production inputs and compare models side-by-side.
Experiments
Define test cases and run experiments, comparing results side by side.
Human Annotation
Collaborative human-in-the-loop workflows to review traces and create golden datasets.
Open Source (MIT)
All product features are MIT licensed, allowing you to inspect, fork, and modify the code.
OTel Native
Standard trace format that works with existing OpenTelemetry instrumentation.
100+ Integrations
Works with any model, any framework, and stack, including LangChain, OpenAI, Anthropic, and more.
Built for Scale
ClickHouse backend allows querying millions of traces in milliseconds, processing billions of events per month.
Async by Default
Tracing never blocks your application with background processing and automatic batching.
Self-Hosted Deployment
Deploy on your own infrastructure using Docker Compose, Kubernetes, or Terraform for full data control.
Pricing
Hobby
- 50k units/month
- 30 days data access
- 2 users
- All platform features with limits
- Community support via GitHub
Core
- 100k units/month
- 90 days data access
- Unlimited users
- In-app support
- Additional usage: $8/100k units
Pro
- 100k units/month
- 3 years data access
- Unlimited users
- Data retention management
- Unlimited annotation queues
- High rate limits
- SOC2 & ISO27001 reports
- HIPAA-ready region
- Prioritized in-app support
- Optional Teams Add-on $300/mo
Enterprise
- 100k units/month
- Everything in Pro + Teams
- Audit Logs
- SCIM API
- Custom rate limits
- Uptime SLA
- Support SLA
- Dedicated support engineer
- Optional Yearly Commitment
Pros and Cons
Pros
- Comprehensive Full-Cycle PlatformCovers tracing, prompt management, evaluations, and monitoring in one integrated platform, reducing toolchain complexity.
- Open Source (MIT)Fully open source with no vendor lock-in. Self-host for free on your own infrastructure.
- Wide Integration Ecosystem100+ integrations with major frameworks (LangChain, OpenAI, etc.) and OTel support, ensuring easy adoption.
- Production-ProvenProcesses billions of events per month with 99.9% uptime, trusted by Fortune 50 companies.
- Active Community22k+ GitHub stars, 5k+ Discord members, and frequent releases ensure continuous improvement and support.
Cons
- Limited Free TierHobby plan only includes 50k observations/month and 30 days data access, which may be insufficient for even small projects.
- Cost Escalation at ScaleAdditional usage beyond included units costs $8 per 100k observations, which can become expensive for high-volume applications.
- Advanced Features Require Higher TiersEnterprise SSO, audit logs, SCIM, and dedicated support are only available in the Pro or Enterprise plans (or as add-ons).
- Self-Hosting ComplexityWhile possible, self-hosting requires managing multiple components (Postgres, ClickHouse, Redis, S3) and may need significant infrastructure effort.
- Dependency on External LLMsSome features like LLM-as-a-judge evaluators depend on external LLM APIs, adding potential cost and latency.
Use Cases & Recommended Professions
Software Engineer→ View Toolkit
Needs Langfuse to trace and debug LLM application calls, integrate observability into existing codebases, and ensure reliable AI features.
AI/ML Engineer→ View Toolkit
Uses Langfuse for prompt management, experiment tracking, and evaluation to iteratively improve model performance and reduce hallucinations.
Data Scientist→ View Toolkit
Relies on Langfuse's metrics and dashboards to analyze cost, latency, and quality, and to build datasets for fine-tuning.
Product Manager→ View Toolkit
Leverages Langfuse's human annotation and user feedback features to monitor product quality and prioritize AI feature improvements.
DevOps Engineer→ View Toolkit
Integrates Langfuse into CI/CD pipelines using CLI and MCP, and manages self-hosted deployments for data sovereignty.
Researcher→ View Toolkit
Uses Langfuse to run controlled experiments, compare models, and gather trace data for academic studies on LLM behavior.
Frequently Asked Questions
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ℹ️ Curation Disclosure: The overview and features of Langfuse were synthesized using AI and fact-checked by our curation team to ensure accuracy.
