Langfuse
Trace and score LLM application runs so teams can debug agent behavior and track cost per user or session.
The verdict on Langfuse: Engineering teams who need production visibility into LLM application behavior that standard monitoring tools miss Langfuse gives engineering teams the production visibility into LLM application behavior that standard application monitoring tools weren't designed to provide: tracing individual LLM calls, scoring outputs, tracking cost per user or session. Pricing: Free (50K units) / $29/mo Core / $199/mo Pro.
Best For
Engineering teams who need production visibility into LLM application behavior that standard monitoring tools miss
Standout Feature
One of the strongest open-source LLM observability platforms, working with any provider rather than locking you in
TL;DR
A genuinely capable eval and monitoring layer, setup requires real SDK integration into your codebase.
Alternatives
Fit Score: use-case reference
How this is scoredLangfuse scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
An engineering team running an LLM application in production that needs to trace, debug, and track cost over time
4.2/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 4/5
- Ease
- 3/5
- Compatibility
- 5/5
Langfuse gives production visibility that standard application monitoring tools weren't designed to provide, is open source, and works with any LLM provider rather than locking teams into one, per the tool's own take.
A team doing initial prototyping rather than debugging a production LLM application
2.4/5- Capability
- 2/5
- Reliability
- 3/5
- Value
- 2/5
- Ease
- 2/5
- Compatibility
- 3/5
Cons note setup requires real SDK integration work and the dashboard can feel complex for simple use cases, and the tool's own take says this is the right tool for production debugging, not initial prototyping.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Langfuse is an open-source LLM observability and evaluation platform that provides production monitoring, debugging, and quality evaluation for AI applications, giving engineering teams the visibility into LLM application behavior that traditional application monitoring tools don't cover. Every LLM call, retrieval operation, and tool use in your application creates a trace in Langfuse: inputs, outputs, model parameters, latency, token usage, and cost, all organized into hierarchical session views that show how a user's request flowed through your system. This trace data transforms debugging from 'reproduce the problem manually' into 'filter traces by the condition that produced the issue and inspect exactly what happened.' Prompt management allows storing, versioning, and deploying prompts from the Langfuse interface rather than hardcoding them, enabling non-engineers to iterate on prompts without code deployments.
Evaluation runs compare model outputs against quality criteria, either LLM-judged or human-reviewed, across datasets, enabling systematic measurement of whether prompt or model changes improve or degrade quality. The analytics dashboard aggregates trace data into trend views of quality scores, latency distributions, cost per feature, and user session patterns. SDK integrations cover Python, TypeScript, LangChain, LlamaIndex, and direct API clients.
Self-hosted under an MIT license or available as a cloud service with a free tier.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • One of the best open-source options in LLM observability
- • Works with any LLM provider
- • Eval framework helps catch quality regressions early
- • Setup requires SDK integration in your codebase
- • Dashboard can feel complex for simple use cases
Key Features
- Full LLM call tracing
- Prompt version management
- User session tracking
- Cost and latency analytics
- Evaluation datasets
- Self-hostable
Trust & Data
Verified 2026-09
- Training on your data
- NoLangfuse's Security FAQ and Privacy FAQ explicitly state it does not train or fine-tune ML or LLM models on client data and does not repurpose customer traces or prompts for external benchmarking, though it points to a separate AI features page for how optional AI-powered product features handle data, which was not independently verified in this pass. Note that langfuse.com/privacy is a general privacy notice for ClickHouse, Inc., Langfuse's parent operator, covering Langfuse-branded services broadly rather than a Langfuse-specific document.
- Compliance
- SOC 2 Type II, ISO 27001, GDPR
- Retention
- Project-level configurable retention from 3 days to unlimited, purged nightly; account data is permanently deleted within 30 days of voluntary account closure; client data is returned or destroyed within 30 days of contract end.
- Export
- Full export
Sources: langfuse.com/security, langfuse.com/privacy faq, langfuse.com/security faq, langfuse.com/privacy
As published by the vendor. Verify independently before purchase decisions.
Our Take
It's open source and works with any LLM provider rather than locking you into one. The honest cost is integration: setup requires real SDK work inside your codebase, and the dashboard can feel complex for straightforward use cases. This is the right tool for an engineering team running an LLM application in production who needs to debug agent behavior and track quality over time, not for initial prototyping.
Workflows Using This Tool
Step-by-step playbooks whose entries name Langfuse. The step shown is where the workflow uses it.