Open-source LLM engineering platform to build, monitor, and improve AI applications.
What is Langfuse?
Langfuse is an open-source observability and analytics platform specifically designed for LLM applications. It was founded in 2023 by Marc Klingen, Timo Obereder, and Max Deichmann, a team with backgrounds in machine learning and software engineering. The platform is model-agnostic, meaning it integrates with any LLM, fine-tuned model, or vector database, providing a unified layer to trace, evaluate, and manage the complex chains and agents common in modern AI stacks. Key features include detailed tracing of LLM calls, user feedback collection, dataset management for prompt engineering, and performance analytics to monitor costs and latency. It primarily targets developers and product teams building LLM-powered applications, enabling use cases like debugging complex workflows, testing prompt variations, and improving model performance based on real user interactions. By integrating seamlessly into development workflows, Langfuse helps teams move from prototype to production with greater reliability and data-driven insights. For teams considering complementary tools for AI development, exploring options like vector databases is essential, as detailed in resources such as https://ai-plaza.io/ai/vector-database. According to a 2024 benchmark by ARK Invest, effective LLM observability is critical for reducing development cycles and operational costs in enterprise AI.
Key Findings
- Observability Platform: Monitors and traces all your LLM applications for actionable insights and debugging.
- Performance Analytics: Tracks key metrics like cost, latency, and quality across all your AI deployments.
- Centralized Logging: Aggregates all prompts, completions, and feedback into a single searchable platform for review.
- Quality Management: Evaluates model outputs with scores and user feedback to continuously improve application performance.
- Prompt Management: Versions, tests, and refines your prompts systematically to ensure optimal and consistent results.
- Cost Tracking: Breaks down expenses by project, user, or model to control your AI budget.
- Seamless Integration: Connects easily with your existing stack through SDKs for Python, Node.js, and more.
- Production Debugging: Identifies root causes of issues in complex LLM chains and agentic workflows quickly.
- User Feedback: Collects and incorporates direct ratings and corrections to align outputs with business goals.
- Data Export: Enables easy access to your observability data for custom analysis and reporting needs.
Who is it for?
Programmer
- AI application debugging
- Performance monitoring and optimization
- Evaluating model performance
- Managing prompt versions
- Ensuring data privacy compliance
Project Manager
- Tracking AI development progress
- Managing team collaboration
- Client deliverable verification
- Budget and cost oversight
- Risk mitigation planning
Startup Founder
- Improving product-market fit
- Demonstrating traction to investors
- Managing technical co-founder priorities
- Controlling operational costs
- Validating a new AI feature
Pricing
Hobby @ Free
- All platform features (with limits)
- 50k units / month included
- 30 days data access
- 2 users
- Community support via GitHub
Core @ $29/month
- Everything in Hobby
- 100k units / month included, additional: $8/100k units
- 90 days data access
- Unlimited users
- In-app support
Pro @ $199/month
- Everything in Core
- 100k units / month included, additional: $8/100k units
- Unlimited data access
- Data retention management
- Unlimited annotation queues
Enterprise @ $2499/month
- Everything in Pro + Teams
- 100k units / month included, additional: $8/100k units
- Audit Logs
- SCIM API
- Custom rate limits