Langfuse is an open-source LLM observability platform that traces, evaluates, and manages prompts for AI agents and applications. It captures every LLM call and retrieval step, providing visibility into cost, latency, and quality metrics in production systems.

What is the pricing model?

Langfuse offers a free tier with core tracing capabilities and a cloud platform with usage-based pricing. Specific pricing is not published on the website and requires contacting sales. Self-hosting is available for enterprise deployments.

Does Langfuse have an API?

Yes, Langfuse provides REST APIs for all functionality, Query SDK for programmatic data access, and S3 blob storage exports. It also offers MCP servers and a CLI for developers to integrate with IDEs and CI/CD pipelines.

What integrations does Langfuse support?

Langfuse supports 80+ integrations including LangChain, Vercel AI SDK, OpenAI, Anthropic, Amazon Bedrock, Google Gemini, and agent frameworks like CrewAI. It works with any language supporting OpenTelemetry instrumentation without framework lock-in.

Can Langfuse be self-hosted?

Yes, Langfuse is MIT licensed and can be self-hosted using Docker Compose, Kubernetes with Helm, or infrastructure-as-code on AWS, GCP, and Azure. The platform scales to billions of monthly events with full feature parity to cloud.

Is Langfuse suitable for recruiting teams?

Yes, recruiting teams use Langfuse to trace AI agent decisions in candidate sourcing and screening workflows, evaluate prompt quality with production data, and collaborate on continuous improvements to matching accuracy and cost efficiency.

Analytics & Observability

Langfuse

Debug AI applications and agents in minutes

What is Langfuse?

Langfuse is a globally accessible LLM observability platform with a recent Japan Cloud deployment. Used by 19 Fortune 50 companies and 100,000+ engineers worldwide, it provides enterprise-scale observability for AI applications across regions. The platform supports self-hosting on Docker, Kubernetes, AWS, GCP, and Azure, giving teams flexibility in deployment geography and data residency.

Features

What are Langfuse's key features?

Hierarchical traces capturing every LLM call, tool invocation, and retrieval step with filtering by user, session, cost, latency, and metadata

Integrated prompt management with version control, one-click deployments, rollbacks, and playground testing

Evaluation framework supporting LLM-as-a-judge, heuristic functions, and human review on production data

Experiments feature for defining test cases and comparing results side-by-side

Human-in-the-loop annotation workflows for collaborative trace review and golden dataset creation

Use Cases

How do staffing agencies use Langfuse?

Trace AI screening agent steps to debug candidate ranking.
Version-control recruiter prompts and A/B test outreach variants.
Annotate production traces and flag LLM classification errors.

Fit

Who is Langfuse best for?

Agencies or TA tech teams running multiple AI recruitment tools who need unified observability across vendor LLMs and internal agents
In-house engineering teams building custom candidate screening or matching agents who require trace-level debugging of multi-step LLM workflows
Staffing operations deploying AI copilots for recruiters where prompt performance directly impacts fill rates and client retention
Teams with data residency requirements or existing infrastructure who prefer self-hosted observability over SaaS vendor dependencies

Pros & Cons

What are Langfuse's pros and cons?

Pros

Enterprise-scale platform handles 10+ billion observations monthly with reliable ingestion and querying of verbose LLM traces that legacy observability tools cannot process
Open source with MIT license, REST APIs, S3 exports, and multiple self-hosting options prevent vendor lock-in and provide full data portability
80+ integrations including LangChain, Claude SDK, OpenAI, Anthropic, and more eliminate framework lock-in and work with any language or model provider
Active open source community with 22,000+ GitHub stars, weekly releases, and MCP/CLI support for coding agents enables rapid feature development

Cons

Self-hosting requires infrastructure expertise and operational overhead compared to managed SaaS alternatives, particularly for Kubernetes deployments
Learning curve for maximizing hierarchical tracing capabilities and setting up evaluators effectively requires dedicated engineering resources
Pricing details not transparently listed on homepage, requiring contact with sales team to understand costs at enterprise scale

Reviews

What are people saying about Langfuse?

Langfuse holds 4.8/5 across 246 Product Hunt reviews.

Source: PRODUCTHUNT · Updated May 5, 2026

FAQ

Frequently asked questions about Langfuse

Other Analytics & Observability tools

1 tool

Metabase

Metabase is an open-source business intelligence platform enabling teams to query, visualize, and explore data without coding. It offers AI-powered natural language querying and embedded analytics capabilities for both internal and customer-facing use.

Analytics & Observability

4.5 (62)

AI-firstAPIMCPGDPR

Metabase

Analytics & Observability

4.5 (62)

AI-firstAPIMCPGDPR

Ready to Scale?

Ready to Fill Your Pipeline?

Book a free 30-minute strategy call. We'll map your current stack, identify the biggest opportunity, and show you exactly how AI can accelerate your pipeline.

Book a Strategy Call

Connect on LinkedIn

30-minute call · No obligation · Custom strategy for your niche