LangFuse
An open-source observability and evaluation platform for LLM applications, providing tracing, prompt versioning, performance monitoring, and evaluation scoring for AI workflows in production.
What It Is
LangFuse is the logging and monitoring layer for AI applications. When an LLM-powered feature runs in production, LangFuse records what happened at every step: which prompt was sent, what the model returned, which tools were called, what data was retrieved, how long each step took, and what the total cost was. This trace is the foundation for debugging, evaluation, and optimization.
Beyond logging, LangFuse enables evaluation: you can define scoring criteria (accuracy, format compliance, policy adherence, user satisfaction) and score model outputs against those criteria at scale. This turns qualitative observations (‘the AI seems worse lately’) into quantitative measurements (‘prompt version 1.3 scores 0.82 on pricing accuracy; version 1.4 scores 0.71 on the same test set after the model update’).
LangFuse is open-source and self-hostable, which matters for B2B operators with data governance requirements. The trace data, which includes the actual queries and responses from your AI application, can be kept within your own infrastructure rather than sent to a third-party SaaS platform.
In B2B Commerce Context
When an AI-powered quote generation feature starts producing incorrect prices, the question is not “did the AI fail?” It clearly did. The question is “where in the pipeline did it fail, and why?” Without an observability tool, answering that question requires manually reconstructing the failure from application logs, which is slow and often inconclusive. With LangFuse, the full trace of that interaction is available: which prompt version ran, which pricing function was called, what it returned, and what the model did with the result.
For B2B operators, the cost monitoring capability is operationally significant. AI features that run thousands of times per day generate real API costs. LangFuse provides cost visibility per feature, per workflow, and per user segment, which is essential data for understanding the unit economics of AI features and for identifying unexpectedly expensive interactions that should be optimized or routed to a cheaper model.
The evaluation workflow in LangFuse integrates well with B2B quality requirements. You define a test set of representative queries (including known edge cases like out-of-stock substitutions, contract-specific pricing, and multi-location orders), run new prompt versions against the test set, and score the results before deploying to production. This is the minimum viable quality gate for any B2B AI feature that affects customer-facing outputs.
When You Need It
- As soon as you move an LLM feature from prototype to production. The prototype stage is where you can afford to debug interactively. Production requires observability infrastructure.
- When you need to diagnose why an AI feature is producing incorrect outputs and cannot afford to reproduce the issue manually.
- When you are managing prompt versions and need to compare the performance of different versions in production.
- When you need to demonstrate to compliance or audit stakeholders that AI outputs are monitored, logged, and reviewed.
LangFuse’s open-source version is fully functional for most enterprise deployments. The data governance advantage of self-hosting, keeping trace data within your own infrastructure, is often the decisive factor for B2B operators with sensitive commerce data.
What It Is Not
- LangFuse is not an AI model or an AI capability. It is observability infrastructure. It does not improve the quality of your AI outputs directly; it gives you the visibility to improve them yourself by identifying where and why they fail.
- It is not a replacement for guardrails. LangFuse observes and records what your AI does. Guardrails prevent your AI from doing things it should not. Both are necessary: guardrails prevent failures; LangFuse helps you diagnose failures that guardrails did not catch and improve the system so fewer failures occur.
- It is not only for large teams. The overhead of setting up LangFuse is low relative to its value. A team of two engineers building a single AI feature for an e-commerce platform benefits from LangFuse in production for the same reason any engineering team benefits from application monitoring: you cannot improve what you cannot see.
Comparison
| LangFuse capability | What it enables | B2B value |
|---|---|---|
| Tracing | Full step-by-step record of each AI interaction | Diagnose pricing errors, route failures, data gaps |
| Prompt versioning | Track and compare prompt versions by performance | Safe prompt updates with rollback capability |
| Evaluation scoring | Score outputs against defined criteria at scale | Measure accuracy before and after prompt changes |
| Cost monitoring | Token usage and cost per workflow / feature | Optimize model routing, identify expensive patterns |
| Self-hostable | Deploy within your own infrastructure | Keep sensitive query data inside your perimeter |
See also
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