Langfuse
by Langfuse · Open-source tracing, evaluation and prompt management for LLM apps
BenchRank score
Screenshots of Langfuse
Homepage
Overview
Langfuse is an open-source platform for tracing and evaluating LLM applications and agents. It records hierarchical traces of LLM calls, tool invocations and retrieval steps, and adds prompt management, datasets, experiments, LLM-as-a-judge scoring and human annotation, with cost and latency dashboards. It runs as a hosted cloud service or self-hosted via Docker, Kubernetes or Terraform.
- Best for
- Engineering teams tracing, evaluating and improving LLM apps who want open source and self-hosting.
- Pricing
- Free Hobby plan, then $29/month (Core), $199/month (Pro) and $2,499/month (Enterprise), each including 100k units with additional usage at $8 per 100k units; self-hosting is free under the MIT licence.
- Runs on
- Self-hosted
Strengths and trade-offs
Strengths
- MIT licensed; self-host via Docker, Kubernetes or Terraform
- OTel-native, with SDKs and 100+ framework integrations
- Tracing, prompts, evals and dashboards in one platform
- Free tier: 50k units/month, no credit card required
Trade-offs
- Data access capped: 30 days on Hobby, 90 on Core, 3 years on Pro
- Included usage stays 100k units on all paid plans; extra is $8/100k
- SSO and fine-grained RBAC need the $300/mo Teams add-on or Enterprise
- Hobby is 2 users with GitHub-only support and no response-time SLO
How Langfuse markets itself
A structured read of the promise, proof and page design on Langfuse’s captured homepage.
Homepage capture
“Open Source Agent Evals & Observability”
- Angle: Open source / ownership
- Hero: Typography only
Pricing
Published plans and prices from Langfuse’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Langfuse pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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