Langfuse
Open-source LLM engineering platform for observability, evaluation, and prompt management.
Overview
Langfuse is an open-source LLM engineering platform designed to help developers build, debug, and improve their large language model applications. It provides a unified solution for critical aspects of LLM development, including observability, evaluation, prompt management, and metrics. The platform allows users to capture complete traces of their LLM applications and agents, enabling deep inspection of failures and the creation of robust evaluation datasets. By integrating with OpenTelemetry and supporting popular LLM/agent libraries, Langfuse offers a flexible and powerful way to understand the behavior and performance of AI systems. Its capabilities extend to managing prompts, tracking key metrics like cost and latency, and facilitating collaborative development through annotations and a public API.
Pricing
Hobby
- All platform features (with limits)
- 50k units / month included
- 30 days data access
- 2 users
- Community support via GitHub
Core
- Everything in Hobby
- 100k units / month included, additional: $8/100k units. Lower with volume
- 90 days data access
- Unlimited users
- In-app support
Pros & Cons
Pros
- Open-source nature allows for transparency and community contributions.
- Comprehensive suite of tools covering observability, evaluation, and prompt management in one platform.
- Built on OpenTelemetry, ensuring compatibility and extensibility with existing monitoring stacks.
- Supports popular LLM and agent libraries, simplifying integration into diverse projects.
- Provides detailed traces to inspect failures and build evaluation datasets.
- Offers a self-hosting option for data privacy and control.
Cons
- Requires some technical expertise for setup and integration, especially for self-hosting.
- New users might face a learning curve to fully utilize all features like advanced evaluation and prompt management.
- Performance and scalability for extremely high-volume LLM applications might require careful infrastructure planning for self-hosted instances.
- Reliance on OpenTelemetry means users need to understand its concepts for advanced customization.
Use Cases
Reviews & Ratings
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Best For
- Debugging complex LLM applications and agentic workflows.
- Evaluating the performance and quality of different LLM models and prompts.
- Managing and versioning prompts for consistent LLM behavior.
- Monitoring the cost and latency of LLM API calls.
- Building datasets for fine-tuning and improving LLM models.
- Collaborating on LLM development within engineering teams.
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