Langfuse
FreemiumOpen-source LLM observability platform — traces, evals, prompts, and datasets for production agents.
By Tanmay Verma, Founder · Last verified 15 May 2026
6.4k views
Added 24d ago
80/100Safe BetOpen-source LLM observability platform — traces, evals, prompts, and datasets for production agents.
By Tanmay Verma, Founder · Last verified 15 May 2026
Affiliate disclosure: We earn a commission when you use our links. Editorial picks are independent. How we choose.
Langfuse is the strongest open-source LLM observability platform in 2026. If you ship LLM features in production, this is the first instrumentation to add. Its 80+ integrations, full-featured self-hosted option, and prompt management make it a no-brainer for teams needing control. For lighter needs, consider Helicone or Arize Phoenix.
Compare with: Langfuse vs Comet
Last verified: May 2026
Langfuse has built a comprehensive, open-source LLM engineering platform that covers the full loop: tracing, prompt management, evaluation, experimentation, and human annotation. Its deep integrations with 80+ tools (LangChain, LlamaIndex, Vercel AI SDK, LiteLLM, and many agent frameworks) make it easy to adopt regardless of your stack. The self-hosted option (MIT licensed) gives you full data control, while the cloud tiers offer a smooth path from hobby to enterprise. Recent innovations like Experiments as a first-class concept, CI/CD integration, and a new Japan region show active development. Strengths include hierarchical tracing, a built-in playground, and cost/latency dashboards. Weaknesses: self-hosting requires ops discipline (ClickHouse isn't trivial), cloud pricing jumps sharply above the Pro tier ($199/month), and the evaluation system, while solid, is less deep than dedicated eval platforms like Braintrust. Best for teams running production LLM applications that need both observability and experimentation. Not ideal for single-developer prototypes or high-volume workloads unwilling to sample traces.
Skip Langfuse if Skip Langfuse if you are still in a single-developer prototype phase and don't need production-grade tracing, evaluation, or prompt management.
Organization admins on Langfuse Cloud can now verify domains and configure Enterprise SSO directly in settings.
Run langfuse experiments in GitHub Actions to catch quality regressions before releasing changes to production.
How likely is Langfuse to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
Langfuse is the open-source observability and experimentation platform for LLM applications. It provides structured tracing of every LLM call (inputs, outputs, tokens, cost, latency), conversation-level session views, prompt management with versioning, evaluations (LLM-as-judge, user feedback, heuristic), datasets for regression testing, and user-level analytics. Integration is straightforward: wrap your LLM calls with a Langfuse decorator (Python/TS/LangChain/LlamaIndex/LiteLLM integrations), and traces appear in the dashboard. For agents built in LangGraph, AutoGen, or the OpenAI Agents SDK, dedicated integrations capture the hierarchical step structure automatically. Self-hosting is first-class — the entire platform runs in Docker Compose with Postgres + ClickHouse + Redis. The managed cloud version has a free hobby tier (50k events/month) and paid tiers starting at $29/month (Core) or $199/month (Pro). Enterprise offers SSO, audit logs, regional data residency, and priority support. It is MIT-licensed and used by 19 of the Fortune 50 and over 100,000 engineers. Recent additions include experiments as a first-class feature, CI/CD integration, self-service Enterprise SSO setup, and a Japan cloud region.
Concrete scenarios for the personas Langfuse actually fits — and what changes day-one when you adopt it.
An agent built with LangGraph starts returning incorrect responses. The engineer opens Langfuse, filters traces by session ID, and replays the exact trace highlighting each LLM call, tool invocation, and retrieval step.
Outcome: Identifies a hallucination in the tool call step; fixes the prompt and rolls it back via Langfuse prompt management.
The PM creates two versions of a system prompt in the Langfuse Playground, runs them on a dataset of 50 real user inputs, and compares the LLM-as-judge scores in the experiment view.
Outcome: Selects the higher-scoring prompt and deploys it with one click via prompt management.
Self-hosting requires real ops discipline — ClickHouse is not a set-and-forget database. Evals are good but less deep than dedicated eval platforms like Braintrust. High-volume workloads may need to sample traces to keep costs manageable. Cloud pricing jumps sharply above the Pro tier ($199/month).
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
For each published Langfuse tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Self-hosted (Open Source)
Free (MIT)
Ideal for
Teams requiring complete data control, compliance, or unlimited scale without per-event costs.
What this tier adds
Free MIT-licensed; you manage infrastructure (Docker Compose, Kubernetes, cloud Terraform).
Hobby
Free
Ideal for
Solo developer or small team exploring LLM observability with low volume (under 50k events/month) and need for a free tier.
What this tier adds
Free entry point with 50k units/month, 2 users, 30-day data access, and community support.
Pro
$59/mo
Ideal for
Scaling projects requiring full feature set, high rate limits, SOC2/ISO27001 reports, and optional Teams add-on.
What this tier adds
The company stage and team size where Langfuse's pricing actually pencils out — and where peers do it cheaper.
Langfuse's pricing fits mid-size to large teams running production LLM apps. The free Hobby tier (50k units/month) is generous for POCs. Core at $29/month (100k units) is competitive with Helicone Pro ($20/month but fewer features) and cheaper than Datadog LLM Observability. Pro at $199/month adds unlimited annotation queues and SOC2 reports, but Datadog charges per host plus ingestion. Self-hosted is free (MIT) but you pay for ops.
How long it actually takes to get something useful out of Langfuse — broken out by persona, not the marketing-page minute.
For a Python or TypeScript developer already using OpenAI/LangChain: add the Langfuse SDK and wrap your LLM call with the decorator—traces appear within 5 minutes. For OTel setup with other languages: 10–20 minutes. Self-hosted Docker deployment takes 30–60 minutes for a single instance; Kubernetes Helm chart adds another 30 minutes.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
Common stack mates teams adopt alongside Langfuse, with the specific reason each pairing earns its keep.
Langfuse vs Promptfoo
Promptfoo vs Langfuse addresses two complementary needs: Langfuse is the better choice for teams that need production observability, tracing, prompt versioning, and eval in one integrated platform — especially for debugging agent behavior and monitoring cost/latency. Promptfoo wins for engineering teams that prioritize lightweight, CI-integrated offline eval and red-teaming with a scriptable CLI. Choose Langfuse if you need a holistic observability platform; choose Promptfoo if your primary workflow is automated prompt testing and security scanning in a CI pipeline.
Langfuse vs Langgraph
Langfuse vs LangGraph address different layers of the LLM stack. Langfuse wins for teams needing observability and evaluation of LLM applications in production – it provides structured tracing, prompt management, evals, and datasets with easy instrumentation. LangGraph wins for engineers building complex, stateful agents that require durable execution, time-travel debugging, and human-in-the-loop. In practice, they are complementary: many production stacks use LangGraph to build agents and Langfuse to observe them. For most teams, the decision depends on whether the primary pain is debugging agent behaviour (LangGraph) or monitoring LLM performance and quality (Langfuse).
Langfuse vs Litellm
Langfuse vs LiteLLM are complementary rather than direct competitors. Langfuse wins for teams that need deep observability, debugging, and prompt management for production LLM applications. LiteLLM wins as a central AI gateway for organizations managing multi-provider access and cost control. The deciding factor: if you need traces and evals, choose Langfuse; if you need a unified API proxy with virtual keys and budgets, choose LiteLLM. Many teams use both together — LiteLLM proxy logs to Langfuse for observability.
Used Langfuse? Help shape our editorial sentiment research.
© 2026 RightAIChoice. All rights reserved.
Built for the AI community.
New dedicated cloud region in Tokyo keeps traces, prompts, and evaluation data inside Japan.
Last calculated: May 2026
How we score →Adds 3-year data access, unlimited annotation queues, high rate limits, SOC2 & ISO27001 reports, HIPAA BAA, and optional Teams add-on ($300/mo).
Team
$499/mo
Google's multimodal AI assistant with Gemini Intelligence across apps and devices.