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What is Langfuse?
Langfuse is an open-source platform designed to help teams build and maintain complex Language Model (LLM) applications. It offers a comprehensive suite of tools for tracing, evaluating, prompt management, and monitoring metrics, enabling efficient debugging and continuous improvement of LLM applications. The platform prioritizes enterprise security and is SOC 2 Type II and ISO 27001 certified, and GDPR compliant.
Langfuse integrates seamlessly with popular development tools and frameworks, including Python SDK, JS/TS SDK, OpenAI SDK, Langchain, and Llama-Index, among others. It offers robust features for managing the entire lifecycle of LLM application development and deployment.
Features
- LLM Tracing: Provides detailed insights into application performance and behavior.
- Prompt Management: Tools for creating, versioning, and managing prompts.
- Evaluation: Evaluate prompts and models for various cases.
- Manual Annotation: Annotate data to further improve models.
- Datasets: Organize data for efficient training and evaluation.
- Metrics: Monitor key performance indicators to track application health.
Use Cases
- Debugging LLM application performance issues.
- Improving prompt quality and effectiveness.
- Evaluating the performance of different LLM models.
- Monitoring LLM application behavior in production.
- Collecting and managing datasets for LLM training.
- Collaborating on LLM application development.
FAQs
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What is the easiest way to try Langfuse?
The easiest way to try is by signing up for the free Hobby plan, no credit card is required. -
What is an observation?
Langfuse did not specify what an observation is, please refer to the Langfuse documentation. -
Can I self-host Langfuse?
Yes, Langfuse offers a self-hosted option. -
Where is the data stored?
Data can be stored in the US or EU region. -
Do you offer discounts?
Yes, Langfuse offers discounts for early-stage startups, education/non-profits, and open-source projects.
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Langfuse Uptime Monitor
Average Uptime
100%
Average Response Time
155.79 ms
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