Langfuse for AI Engineers: LLM Observability & Evaluation
Langfuse is a Development & Misc platform that traces LLM requests and agent steps into structured, queryable data. AI engineers use Langfuse to debug multi-turn chat agents by tracing full conversation sessions before shipping fixes. Langfuse handles tracing and evaluation at scale, while teams wanting only a lightweight request logger should look elsewhere.
- United States 15.6%
- China 12.2%
- India 9.7%
- Vietnam 9.4%
- Other 53.1%
What Langfuse Does
Langfuse converts raw LLM and agent execution data into traces, scores, and datasets through SDK or OpenTelemetry instrumentation.
Langfuse connects tracing, prompt management, datasets, experiments, and evaluation in one continuous loop, so teams move from a production incident to a tested fix without switching tools. It works with any language or framework supporting OTel instrumentation and lists 100+ integrations, avoiding lock-in to a single agent framework. Because Langfuse is open source, self-hosting keeps trace data under a team’s own control instead of a vendor’s servers.
Main Features
Observability
Langfuse captures end-to-end traces of LLM applications and agents, including sessions, users, and token/cost tracking. Each request, tool call, and chain step is logged with its inputs and outputs. This gives engineers a full replay of a conversation, so a failure in turn five of a chat session can be traced back to the exact prompt or tool call that caused it.
Prompt Management
Langfuse versions and releases prompts centrally, with server- and client-side caching and composability across prompt fragments. Teams fetch prompts via SDK rather than hardcoding strings in application code. Because releases are tracked, a prompt change can be rolled back without redeploying the application that calls it.
Evaluations
Langfuse runs offline and online evaluation using LLM-as-judge scoring, human annotation queues, and custom scores. Datasets built from production traces feed experiments that compare prompt or model versions. This lets a team measure whether a change actually improved quality before it reaches every user, rather than after a rollout.
Enterprise Security & Compliance
Langfuse’s Enterprise plan supports data regions in the US, EU, Japan, or a HIPAA-ready region, plus AWS PrivateLink, organization-level RBAC, and SSO via Okta or AzureAD/EntraID. SOC 2 Type II and ISO 27001 reports are available. This matters for regulated teams that must keep trace data, which often contains customer inputs, inside a controlled jurisdiction.
Use Cases
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Debugging multi-turn chat agents
AI engineers bring stateful chat agents that fail intermittently across long sessions. Langfuse traces full conversation sessions and tracks cost per turn to isolate where quality drops. Root causes surface faster than reading raw logs turn by turn.
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Automating coding agent workflows
Engineering teams run coding agents that make many small decisions per task. Langfuse tracks each agent decision, its cost, and its outcome across the workflow. Regressions in a coding agent’s logic become visible before they reach a pull request.
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Monitoring workflow automation agents
AI product teams operate automation pipelines running unattended in production. Langfuse observes each execution step and flags reliability issues as they occur. Failures in a pipeline get caught and diagnosed without waiting for a user complaint.
Best For / Not For
Langfuse is built for teams running LLM applications and agents in production who need to see what those systems are actually doing at every step.
AI engineers debugging conversational agents, engineering teams maintaining coding agents, and AI product teams operating automation pipelines all rely on Langfuse’s tracing, evaluation, and prompt management to catch failures before users do.
Langfuse is not the right fit for a team that only wants a simple request logger without prompt management, datasets, or evaluation attached — that added depth becomes overhead they don’t need.
Pricing
Langfuse offers a free Hobby tier and paid plans starting at $29 per month for Core, scaling to $199 per month for Pro, with a custom-billed Enterprise tier above that.
| Plan | Price | Included |
|---|---|---|
| Hobby | Free | 50k units/month, 2 users, 30 days data retention |
| Core | $29 / month, billed monthly | 100k units/month, unlimited users, 90 days data retention |
| Pro | $199 / month, billed monthly | 100k units/month, unlimited users, 3 years data retention |
| Enterprise | $2499 / month, billed monthly | 100k units/month, unlimited users, 3 years data retention |
Usage beyond the included units on Core and Pro costs $8 per 100k additional units, with lower per-unit rates at higher volume tiers.
Pricing checked 2026-09-18.
Quick Comparison
Cursor is Langfuse’s main alternative for teams centering their workflow inside an AI coding editor. Cursor focuses on in-editor code generation and completion tied directly to a codebase. Langfuse instead traces and evaluates what agents do after code ships, using datasets and LLM-as-judge scoring. Choose Cursor if the priority is writing and editing code faster inside an IDE. Choose Langfuse if the priority is observing and evaluating agent behavior in production.
Verdict
Langfuse traces, evaluates, and manages prompts for LLM agents through 100+ integrations, starting free at 50k units/month and scaling to $199/month for Pro. It fits engineering and AI product teams running agents in production, not teams needing only basic request logging.
