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Langfuse

Open-source LLM engineering platform for observability

Langfuse is an open-source LLM engineering platform with 29K+ GitHub stars for tracing, evaluating, and monitoring AI applications. Acquired by ClickHouse, it provides detailed traces of LLM calls, prompt management with versioning, dataset-based evaluation, user feedback collection, and cost tracking. Framework-agnostic with native integrations for LangChain, LlamaIndex, OpenAI SDK, and Vercel AI SDK. Offers both self-hosted deployment and a managed cloud service.

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About Langfuse

Langfuse is the most popular open-source LLM observability platform, providing tracing, evaluation, and monitoring for AI applications. With over 29,000+ GitHub stars and acquired by ClickHouse for its analytical capabilities, it has become a critical part of the LLM engineering stack.

The tracing system captures detailed information about every LLM call including inputs, outputs, latency, token usage, and costs. Complex agent workflows with multiple LLM calls are visualized as nested traces, making it easy to debug and optimize multi-step applications.

Prompt management with versioning allows teams to iterate on prompts, track changes, and deploy specific versions to production. Dataset-based evaluation enables systematic testing with custom metrics and LLM-as-judge evaluators. User feedback collection creates ground truth for continuous improvement.

Langfuse is framework-agnostic with native integrations for LangChain, LlamaIndex, OpenAI SDK, Vercel AI SDK, and more. Cost tracking aggregates LLM spending across models and provides breakdowns by user, feature, or time period.

Both self-hosted and managed cloud deployment are available. The open-source version can be deployed via Docker with full feature parity. The managed cloud offers additional convenience with automatic updates and scaling.

Pricing & Platform Specs

Pricing Summary

Langfuse is open-source under MIT for self-hosting with full features. Langfuse Cloud provides a free Hobby tier (50k units/month, 2 users), a Core plan at $29/month (100k units, unlimited users), a Pro plan at $199/month (3-year retention, SSO, SOC2), and an Enterprise plan at $2,499/month with custom SLAs.

full pricing breakdown →

Supported Platforms

Web, Self-hosted, Docker, Python, JS/TS SDK

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Alternatives

All Langfuse alternatives →
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Laminar

Open-source observability for AI agents

Laminar is an open-source observability platform for AI agents providing tracing, evaluation, and analytics for LLM applications. It integrates with Vercel AI SDK, LangChain, OpenAI, and Anthropic with a single line of code. Features include OpenTelemetry-native SDKs, an extensible evaluation framework with CI/CD support, SQL access to traces and metrics, and a visual debugging timeline for agent reasoning and actions.

freemiumOpen Source
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Weights & Biases

ML experiment tracking and model monitoring

Weights & Biases is an AI developer platform for experiment tracking, artifact and model lineage, model monitoring, and Weave-based LLM evaluation. It helps teams log runs, compare metrics, manage datasets and model artifacts, and collaborate through dashboards, reports, alerts, SSO/RBAC controls, and hosted or self-managed deployment options.

freemium
Braintrust logo

Braintrust

LLM evaluation and prompt engineering platform

Braintrust is an AI observability and evaluation platform for tracing LLM applications, building datasets, running prompt/model experiments, scoring outputs and turning production feedback into regression tests. It fits teams that need repeatable quality gates for AI releases rather than one-off prompt demos.

freemium
TraceRoot logo

TraceRoot

Open-source observability and self-healing layer for AI agents

TraceRoot is a YC S25-backed open-source observability platform purpose-built for AI agents and LLM apps. It combines OpenTelemetry-compatible tracing with an agentic debugging runtime that reads your source code, correlates failures with recent commits, and proposes fix PRs automatically. BYOK support spans seven LLM providers; the entire stack runs self-hosted via Docker Compose, with TraceRoot Cloud available for managed deployments.

freemiumOpen Source
Judgeval logo

Judgeval

Open-source post-building layer for agents — tracing, evals, and online monitoring

Judgeval is the open-source post-building layer for AI agents from Judgment Labs, providing OpenTelemetry-based tracing, hosted and custom evaluation scorers, and online behavior monitoring for LLM-powered applications. Instrument any function with a single decorator, score live production traffic against faithfulness and instruction-adherence checks, and feed real-world failures back into reinforcement learning or supervised fine-tuning loops.

Open Source

Side-by-Side Comparisons

Opik logo
Opik
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Langfuse logo
Langfuse

Opik vs Langfuse: AI Optimization Suite or Open LLM Platform?

Opik and Langfuse are two credible open-source choices for tracing, evaluating, and improving LLM applications and agents. Opik, from Comet, combines observability, test suites, assertions, prompt experiments, production monitoring, and automatic prompt optimization. Langfuse combines agent and application tracing, prompt management, datasets, online and offline evaluation, feedback, and mature self-hosting. Langfuse serves as the more practical daily standard because it has the broader adoption base, a particularly complete prompt-and-observability workflow, and flexible free or managed deployment. Opik is the better specialist when built-in optimization algorithms and the Comet ecosystem are decisive.

OpikLangfuse
AgentOps logo
AgentOps
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Langfuse logo
Langfuse

AgentOps vs Langfuse: Agent Sessions or Full LLM Engineering?

AgentOps and Langfuse both help teams understand production AI agents, but AgentOps is centered on agent sessions and events while Langfuse spans agents, general LLM applications, prompt management, datasets, evaluation, and feedback. AgentOps offers a focused path to session replay, timelines, cost and error analysis across popular agent frameworks. Langfuse provides the more cohesive complete solution because it provides comparable tracing plus a broader quality and prompt lifecycle, an MIT-licensed self-hosted edition, and a transparent managed-cloud ladder. AgentOps is still a strong specialist for teams that want agent-specific monitoring with minimal platform breadth.

AgentOpsLangfuse
MLflow logo
MLflow
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Langfuse logo
Langfuse

MLflow vs Langfuse: Full ML Lifecycle or LLM-Native Engineering?

MLflow and Langfuse are both open-source platforms that can trace and evaluate generative AI systems, but they come from different operating centers. MLflow manages the full machine-learning lifecycle, including experiments, models, registry, deployment, and increasingly capable GenAI tracing and evaluation. Langfuse is built specifically for LLM applications and agents, joining traces, prompts, datasets, feedback, and online or offline evaluation. Langfuse serves as the more practical daily standard for an LLM-first team because its workflows and pricing units match production AI applications directly. MLflow is stronger when a company already runs MLflow or needs one governance layer across classical ML and GenAI.

MLflowLangfuse
Braintrust logo
Braintrust
vs
Langfuse logo
Langfuse

Braintrust vs Langfuse: Managed Eval Workflow or Open LLM Platform?

Braintrust and Langfuse both connect tracing, datasets, experiments, scoring, and production feedback, but they make different platform tradeoffs. Braintrust emphasizes a polished managed workflow for evaluation-heavy AI teams, while Langfuse combines observability, prompt management, online and offline evaluation, and a free MIT-licensed self-hosted deployment. Langfuse provides the more cohesive complete solution for most teams because it offers a credible managed cloud path without surrendering deployment control or core features. Braintrust remains attractive when a team prioritizes its integrated experiment and annotation workflow and is comfortable standardizing on the managed product.

BraintrustLangfuse
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FAQ

What is Langfuse?

Langfuse is an open-source LLM engineering platform with 29K+ GitHub stars for tracing, evaluating, and monitoring AI applications. Acquired by ClickHouse, it provides detailed traces of LLM calls, prompt management with versioning, dataset-based evaluation, user feedback collection, and cost tracking. Framework-agnostic with native integrations for LangChain, LlamaIndex, OpenAI SDK, and Vercel AI SDK. Offers both self-hosted deployment and a managed cloud service.

Is Langfuse free?

Langfuse offers a free tier alongside paid plans. Langfuse is open-source under MIT for self-hosting with full features. Langfuse Cloud provides a free Hobby tier (50k units/month, 2 users), a Core plan at $29/month (100k units, unlimited users), a Pro plan at $199/month (3-year retention, SSO, SOC2), and an Enterprise plan at $2,499/month with custom SLAs.

Is Langfuse open source?

Yes — Langfuse is open source.

Is Langfuse still maintained?

Yes — Langfuse is active. Its listing was last verified on August 26, 2026.

What are the best Langfuse alternatives?

The first editor-selected Langfuse alternatives are Laminar, Weights & Biases, Braintrust, and more.

How does Langfuse score in our review?

The published editorial review lists Langfuse at 87/100 overall across speed, privacy, and developer experience. Check the review's evidence status and test metadata for its verification level.