L

Langfuse

by Langfuse (acquired by ClickHouse, Inc. in January 2026)

Developer ToolsAI Agents & OrchestrationData & AnalyticsGovernance & Security

Open source LLM engineering platform

freemium · subscription · usage-based · open-source·Added June 23, 2026·Updated June 23, 2026
Share:
THE DAILY BRIEF
Langfuse

by Langfuse (acquired by ClickHouse, Inc. in January 2026)

Developer ToolsAI Agents & OrchestrationData & AnalyticsGovernance & Security

Open source LLM engineering platform

freemium · subscription · usage-based · open-source

Langfuse is an open-source LLM engineering and observability platform for tracing, evaluating, debugging, and improving AI applications, with both managed cloud and self-hosted deployment options.

At a Glance

Category
Developer Tools
Pricing
freemium, subscription, usage-based, open-source
Target Market
Startups, Enterprises, Individual developers, AI engineering teams
Founded
2022
Headquarters
Berlin, Germany
Customers
Trusted by 19 of the Fortune 50 and 63 of the Fortune 500 (per ClickHouse acquisition announcement)

Key Features

  • LLM Observability & Tracing
  • Prompt Management
  • Evaluations
  • Datasets & Experiments
  • Playground

Capabilities

text generation
image generation
video generation
code generation
workflow automation
api access
audio generation
fine tuning
agent orchestration

Use Cases

  • Debugging multi-step LLM and agent pipelines
  • Monitoring production LLM cost, latency, and quality
  • Evaluating and iterating on prompts before deployment

Ideal For

Best For

  • AI/ML engineering teams building LLM applications
  • Developers debugging multi-step LLM and agent pipelines
  • Teams needing prompt management and evaluation workflows
  • Organizations requiring self-hosted LLM observability

Market Analysis

Leading open-source LLM observability and engineering platformPositioned as an open alternative to LangSmith and other proprietary LLMOps tools

Pros

  • Open-source with free, uncapped self-hosting
  • Detailed hierarchical tracing for debugging complex/multi-step LLM and agent pipelines
  • Clean prompt versioning and experiment workflows
  • Straightforward SDK setup and broad framework integrations

Cons

  • Bulk trace querying can become slow at high traffic volumes
  • UI can get crowded on complex multi-step traces and agents
  • Self-hosting total cost of ownership can be high for teams not already running ClickHouse/Postgres at scale

Pricing

Hobby

$0/month

  • 50k units/month included
  • 30 days data access
  • 2 users
  • Community support
  • No credit card required
  • All core features with limits

Core

$29/month

  • 100k units/month included, additional $8/100k units
  • 90 days data access
  • Unlimited users
  • In-app support

Pro

$199/month

  • 100k units/month included, additional $8/100k units
  • 3 years data access
  • Unlimited annotation queues
  • High rate limits
  • SOC2 & ISO27001 reports, HIPAA-compliant BAA available
  • Optional Teams add-on $300/month (SSO, RBAC, dedicated Slack)

Enterprise

$2,499/month

  • Everything in Pro + Teams
  • Custom rate limits
  • Uptime and support SLA
  • Dedicated support engineer
  • Optional yearly commitment with custom volume pricing

Self-Hosted (Open Source)

$0

  • Self-host all core features for free
  • Open source, no hard cap on events or traces
  • Deploy via Docker, Kubernetes, or cloud templates

Cloud usage is metered in 'units' with graduated overage rates decreasing from $8/100k units (100k-1M) to $6/100k units (50M+). Paid cloud tiers include unlimited users (no per-seat charge). Self-hosting the open-source version is free of license cost, though infrastructure (ClickHouse, Postgres) must be operated by the user.

THE DAILY BRIEF

Enterprise AI insights for technology and business leaders, twice weekly.

beri.net

Subscribe at beri.net/subscribe for twice-weekly AI insights delivered to your inbox.

LinkedIn: linkedin.com/in/rberi  |  X: x.com/rajeshberi

© 2026 Rajesh Beri. All rights reserved.

Langfuse is an open-source LLM engineering and observability platform for tracing, evaluating, debugging, and improving AI applications, with both managed cloud and self-hosted deployment options.

At a Glance

Category
Developer Tools
Pricing
freemium, subscription, usage-based, open-source
Target Market
Startups, Enterprises, Individual developers, AI engineering teams
Founded
2022
Headquarters
Berlin, Germany
Customers
Trusted by 19 of the Fortune 50 and 63 of the Fortune 500 (per ClickHouse acquisition announcement)

Key Features

  • LLM Observability & Tracing

    Hierarchical traces capture every LLM call, tool invocation, retrieval, and embedding step with inputs, outputs, timing, cost, latency, and metadata, filterable by user, session, cost, latency, or custom metadata.

  • Prompt Management

    Version, manage, and deploy prompts separately from code with one-click deployments and rollbacks and strong server- and client-side caching to minimize latency.

  • Evaluations

    Supports LLM-as-a-judge, code-based evaluators, user feedback collection, manual labeling, and custom evaluation pipelines via APIs and SDKs.

  • Datasets & Experiments

    Build test sets and benchmarks to run structured experiments, enable pre-deployment testing, and continuously improve LLM applications.

  • Playground

    Interactive interface for testing and iterating on prompts and model configurations, with the ability to jump from a bad trace directly into the playground.

Capabilities

text generation
image generation
video generation
code generation
workflow automation
api access
audio generation
fine tuning
agent orchestration

Use Cases

  • Debugging multi-step LLM and agent pipelines

    Use hierarchical trace visualization to inspect retrieval, reranking, tool calls, and generation steps separately, reducing debug time in complex chains and agents.

  • Monitoring production LLM cost, latency, and quality

    Track token usage, cost, latency, and quality across LLM applications in production, watch for issues like hallucinations, and analyze request history when things go wrong.

  • Evaluating and iterating on prompts before deployment

    Manage prompt versions, run experiments against datasets, and apply LLM-as-a-judge and code evaluators to test changes before shipping to production.

Ideal For

Best For

  • AI/ML engineering teams building LLM applications
  • Developers debugging multi-step LLM and agent pipelines
  • Teams needing prompt management and evaluation workflows
  • Organizations requiring self-hosted LLM observability

Integrations

SDK Available
SDK:PythonJavaScript/TypeScript

Deployment

On-Premise

Market & Ratings

Estimated Customers

Trusted by 19 of the Fortune 50 and 63 of the Fortune 500 (per ClickHouse acquisition announcement)

Market Analysis

Leading open-source LLM observability and engineering platformPositioned as an open alternative to LangSmith and other proprietary LLMOps tools

Pros

  • Open-source with free, uncapped self-hosting
  • Detailed hierarchical tracing for debugging complex/multi-step LLM and agent pipelines
  • Clean prompt versioning and experiment workflows
  • Straightforward SDK setup and broad framework integrations

Cons

  • Bulk trace querying can become slow at high traffic volumes
  • UI can get crowded on complex multi-step traces and agents
  • Self-hosting total cost of ownership can be high for teams not already running ClickHouse/Postgres at scale

Pricing

Hobby

$0/month

  • 50k units/month included
  • 30 days data access
  • 2 users
  • Community support
  • No credit card required
  • All core features with limits

Core

$29/month

  • 100k units/month included, additional $8/100k units
  • 90 days data access
  • Unlimited users
  • In-app support

Pro

$199/month

  • 100k units/month included, additional $8/100k units
  • 3 years data access
  • Unlimited annotation queues
  • High rate limits
  • SOC2 & ISO27001 reports, HIPAA-compliant BAA available
  • Optional Teams add-on $300/month (SSO, RBAC, dedicated Slack)

Enterprise

$2,499/month

  • Everything in Pro + Teams
  • Custom rate limits
  • Uptime and support SLA
  • Dedicated support engineer
  • Optional yearly commitment with custom volume pricing

Self-Hosted (Open Source)

$0

  • Self-host all core features for free
  • Open source, no hard cap on events or traces
  • Deploy via Docker, Kubernetes, or cloud templates

Cloud usage is metered in 'units' with graduated overage rates decreasing from $8/100k units (100k-1M) to $6/100k units (50M+). Paid cloud tiers include unlimited users (no per-seat charge). Self-hosting the open-source version is free of license cost, though infrastructure (ClickHouse, Postgres) must be operated by the user.

Newsletter

Stay Ahead of the Curve

Weekly enterprise AI insights for technology leaders. No spam, no vendor pitches—unsubscribe anytime.

Subscribe