Langfuse delivers enterprise-grade LLM observability with a generous free tier and open-source self-hosting option — the best monitoring value for teams of any size.
Open-source LLM engineering platform for traces, prompts, and metrics.
An open-source dashboard that shows you exactly what your AI is doing — track costs, quality, and performance of every AI call.
Langfuse is an open-source LLM engineering platform that provides end-to-end observability, prompt management, and evaluation capabilities for AI applications. Originally launched in 2023 as a tracing tool, it has evolved into a comprehensive platform that covers the full lifecycle of LLM application development — from prompt iteration to production monitoring.
The core of Langfuse is its tracing system. Every LLM call, retrieval step, tool invocation, and custom span gets captured as a hierarchical trace. This isn't just logging — traces are structured with parent-child relationships, so you can see exactly how a complex agent workflow unfolds: which retrieval was called, what context was passed to the LLM, what the model returned, and how long each step took. The Python and JavaScript SDKs integrate with one decorator or wrapper call, and there are native integrations for LangChain, LlamaIndex, OpenAI SDK, Vercel AI SDK, and most major frameworks.
Prompt management in Langfuse is genuinely useful for teams. You version prompts in the Langfuse UI, link them to traces in production, and can A/B test prompt variants with real traffic. This creates a tight feedback loop: you see how a prompt performs in production, iterate on it in the UI, and deploy the new version without code changes.
The evaluation system supports both LLM-as-judge evaluations and human annotation workflows. You can define custom scoring functions, run them against traces automatically, and build datasets from production data for regression testing. The annotation queue feature lets you route traces to human reviewers for quality assessment.
Self-hosting is straightforward — Langfuse runs as a single Docker container with PostgreSQL and ClickHouse, or you can use their managed cloud. The self-hosted version has feature parity with cloud, which is rare and genuinely appreciated by teams with data residency requirements.
The main limitation is that Langfuse's analytics and dashboards, while improving, are less polished than commercial alternatives like Helicone or Braintrust for executive-level reporting. The UI can also feel sluggish with very large trace volumes. But for engineering teams that want an open-source, self-hostable observability platform with real prompt management and evaluation capabilities, Langfuse is the strongest option available.
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Langfuse is the leading open-source LLM observability platform, appreciated for its comprehensive tracing, prompt management, and evaluation features — all self-hostable. The community is active and development pace is fast. Users note that the self-hosted setup requires some DevOps expertise, and certain enterprise features lag behind LangSmith. The open-source model and generous cloud free tier make it an excellent starting point for any team.
Records LLM calls, retrievals, tool invocations, and custom spans as structured parent-child traces. Each trace captures inputs, outputs, latency, token counts, and costs with automatic model pricing.
Use Case:
Debugging a RAG agent that produces incorrect answers by tracing the exact retrieval results and prompt construction that led to the bad output.
Version-controlled prompt templates managed through the Langfuse UI. Prompts are linked to production traces, enabling direct comparison of how different prompt versions perform with real user queries.
Use Case:
A/B testing a new system prompt for a customer support agent by deploying two versions and comparing resolution rates in the Langfuse dashboard.
Supports custom evaluation functions, LLM-as-judge evaluators, and manual human scoring. Scores attach directly to traces and can trigger alerts or feed into dataset creation for regression testing.
Use Case:
Running automated hallucination detection on every production trace and routing low-scoring responses to a human review queue.
Create datasets from production traces or manual uploads, then run experiments comparing different model configurations, prompts, or pipeline architectures against the same test cases.
Use Case:
Building a golden dataset of 500 production queries and running regression tests whenever you update your RAG retrieval strategy.
Groups traces into user sessions and tracks per-user metrics including cost, latency, and quality scores over time. Enables analysis of user-level patterns and identification of problematic interaction sequences.
Use Case:
Identifying that a specific user segment consistently triggers longer response times due to complex multi-turn conversations.
Traces can be exported in OpenTelemetry format for integration with existing observability stacks like Grafana, Datadog, or custom dashboards, bridging LLM-specific and infrastructure monitoring.
Use Case:
Feeding Langfuse trace data into a Grafana dashboard that combines LLM latency metrics with infrastructure metrics for a unified operations view.
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View Pricing Options →Engineering teams building RAG applications who need to trace the full retrieval-to-generation pipeline and iterate on prompts without redeploying
Organizations with data residency requirements that need a fully self-hosted observability platform with no feature compromises
Teams running multi-agent systems who need hierarchical tracing to debug complex inter-agent communication and tool usage patterns
Product teams that want to combine automated LLM evaluation with human annotation workflows to maintain quality standards
Langfuse works with these platforms and services:
We believe in transparent reviews. Here's what Langfuse doesn't handle well:
They have full feature parity. The self-hosted version runs as Docker containers with PostgreSQL and ClickHouse backends. You get the same tracing, prompt management, evaluation, and dashboard features. The main difference is you handle infrastructure, updates, and scaling yourself.
Yes, but with caveats. The cloud version handles millions of traces well. Self-hosted performance depends on your ClickHouse and PostgreSQL sizing. For high-volume workloads (>100K traces/day), you'll want dedicated ClickHouse instances and may need to tune retention policies.
Both are open-source, but they emphasize different things. Langfuse focuses on the full engineering workflow (tracing + prompt management + evals), while Phoenix emphasizes ML observability with stronger drift detection and embedding visualization. Langfuse has better framework integrations; Phoenix has deeper analytical capabilities.
Yes. Langfuse supports projects with role-based access control. Team members can be assigned viewer, member, or admin roles per project. The cloud version includes SSO on higher tiers. Self-hosted RBAC works the same way but SSO requires additional configuration.
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