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Langfuse

  • What it is:Langfuse is a open-source LLM engineering platform that provides observability, prompt management, evaluation, and metrics to help teams debug, monitor, and improve AI applications.
  • Best for:LLM engineering teams using multiple frameworks, Startups with variable LLM usage, Teams preferring self-hosting
  • Pricing:Free tier available, paid plans from $29/month
  • Rating:88/100Very Good
  • Expert's conclusion:Any team developing and implementing production-ready LLM applications and/or agents will need this.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Langfuse and What Does It Do?

Langfuse is a free open source LLM engineering platform that enables companies to develop production-ready LLM application faster using analysis, evaluation, tracing, prompt management and observability. Langfuse was founded in 2023 by Marc Klingen, Maximilian Deichmann, and Clemens Rawert and is located in both San Francisco and Berlin. In 2026, LangFuse was acquired by Clickhouse to help speed up development.

Active
📍Berlin, Germany
📅Founded 2022
🏢Private (Acquired)
TARGET SEGMENTS
Engineering teamsStartupsEnterprisesDevelopers building LLM apps

What Are Langfuse's Key Business Metrics?

📊
10,000+
GitHub Stars
🏢
13
Employees
📊
2 (Berlin, San Francisco)
Offices
📊
$4M Seed
Funding Raised
👥
Startups & Enterprises (e.g., Khan Academy)
Customers
📊
W23
YC Batch
Rating by Platforms
4.8/ 5
Product Hunt (500 reviews)

How Credible and Trustworthy Is Langfuse?

88/100
Excellent

Open Source Platform With Strong Backing From YC and Enterprise Adoption Now Accelerated By Acquisition Into Clickhouse.

BREAKDOWN
Product Maturity85/100
Company Stability90/100
Security & Compliance80/100
User Reviews92/100
Transparency95/100
Support Quality85/100
TRUST SIGNALS
Y Combinator W23Acquired by ClickHouse (Jan 2026)10,000+ GitHub starsUsed by Khan Academy and YC companiesProduct Hunt Product of the Day$4M seed funding from Lightspeed, La Famiglia, YC

What is the history of Langfuse and its key milestones?

2022

Founders Unite

Marc Klingen, Max Deichmann, and Clemens Rawert start working together and get accepted into the YC W23 class.

2023

Public Launch

Launch as open source LLM observability platform on Show HN, YC Launch, Product Hunt (Product of the Day).

2023

$4M Seed Round

Raise $4M led by Lightspeed Venture Partners, La Famiglia, and Y Combinator.

2024

Initial Team Build

Marlies Mayerhofer and Hassieb Pakzad become the first engineers at Langfuse.

2025

10k GitHub Stars & SF Office

Get 10,000 GitHub stars and open San Francisco office.

2026

Join ClickHouse

Acquired by Clickhouse to accelerate platform development.

Who Are the Key Executives Behind Langfuse?

Marc Klingen— Co-Founder & CEO
Full Stack Engineer with experience in product, sales and business intelligence; active founder in YC W23 class that pivoted to LLM Observability.. LinkedIn
Maximilian Deichmann— Co-Founder & CTO
Experienced in creating reliable and scalable systems with Computer Science and Management background; handles critical infrastructure.. LinkedIn
Clemens Rawert— Co-Founder
Experience in fundraising, acquiring companies, and growing organizations from a German fintech unicorn; leads SF expansion.. LinkedIn
Marlies Mayerhofer— Founding Engineer
Early Engineer Building Core Platform Development Since Team Formed In 2024.. LinkedIn
Hassieb Pakzad— Founding Engineer
Early Engineer Developing Scalable LLM Infrastructure Since Joined In 2024.. LinkedIn

What Are the Key Features of Langfuse?

✨
LLM Tracing
Captures Inputs, Outputs, Latency, Costs And Intermediate Steps For End-To-End Tracing Of LLM Applications.
👥
Prompt Management
Version Control And Collaboration Platform For Testing, Iterating On LLM Prompts.
✨
Evaluations & Analytics
Automated and manual assessments to determine LLM output quality, user intent prediction, and performance metrics.
✨
Open Source & Self-Hosted
Enterprise capable fully open source platform that can be deployed on your own infrastructure.
✨
Framework Agnostic
Compatible with all LLM frameworks, cloud providers, and deployment environments
✨
Experiment Tracking
Supports A/B testing, experiment management, and performance comparisons across prompt versions.
✨
Production Analytics
Monitors usage, costs, latency, and quality in real time for enterprise LLM applications.

What Technology Stack and Infrastructure Does Langfuse Use?

Infrastructure

Self-hosted or cloud (ClickHouse backed, multi-region capable)

Technologies

TypeScriptNode.jsPythonPostgreSQLClickHouse

Integrations

LangChainLlamaIndexOpenAIAnthropicAWSVercelCloud Providers

AI/ML Capabilities

LLM observability platform with tracing, evaluation, and analytics for complex LLM chains, agents, and applications across any model/provider

Inferred from documentation mentions and ClickHouse acquisition; specific frontend/backend stack from typical OSS LLM tools

What Are the Best Use Cases for Langfuse?

LLM Engineering Teams
Provides comprehensive tracing, debugging, and optimization capabilities for complex LLM chains and agents with cost/latency analytics.
AI Startups
Allows rapid iteration on LLM applications with the open-source flexibility of evaluations and proven reliability through YC.
Enterprise Platform Teams
Enables self-hosted deployments for enterprise LLM infrastructure with full data control and compliance.
Individual LLM Developers
Offers free open source observability for local debugging and prototyping of LLM experiments.
NOT FORSimple Chatbot Teams
Is overkill for most use cases where simple pass-through responses are needed from a LLM with no additional logic or evaluation requirements.
NOT FORNon-LLM Applications
Does not provide typical application observability; it is only intended for LLM workload-specific monitoring.

How Much Does Langfuse Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
HobbyFree50k units/month, 30 days data access, 2 users, community support via GitHub, all platform features (with limits)—
Core$29/month100k units/month included ($8/100k additional, volume discounts), 90 days data access, unlimited users, in-app support—
Pro$199/month100k units/month included ($8/100k additional, volume discounts), 3 years data access, data retention management, unlimited annotation queues, high rate limits, SOC2 & ISO27001 reports, BAA available (HIPAA), prioritized in-app support—
Teams Add-on$300/monthEnterprise SSO (Okta), SSO enforcement, fine-grained RBAC, dedicated Slack channel. Add-on to Pro—
Enterprise$2499/monthEverything in Pro + Teams, audit logs, SCIM API, custom rate limits, uptime SLA, support SLA, dedicated support engineer—
Self-hosted Open SourceFreeMIT License, all core platform features and APIs, scalability of Langfuse Cloud, deployment docs & Helm chart, community support—
Self-hosted EnterpriseCustom PricingAll Open Source features + management APIs, project-level RBAC, data retention policies, audit logs, ISO27001 reviews, dedicated support, support SLA—
HobbyFree
50k units/month, 30 days data access, 2 users, community support via GitHub, all platform features (with limits)
Core$29/month
100k units/month included ($8/100k additional, volume discounts), 90 days data access, unlimited users, in-app support
Pro$199/month
100k units/month included ($8/100k additional, volume discounts), 3 years data access, data retention management, unlimited annotation queues, high rate limits, SOC2 & ISO27001 reports, BAA available (HIPAA), prioritized in-app support
Teams Add-on$300/month
Enterprise SSO (Okta), SSO enforcement, fine-grained RBAC, dedicated Slack channel. Add-on to Pro
Enterprise$2499/month
Everything in Pro + Teams, audit logs, SCIM API, custom rate limits, uptime SLA, support SLA, dedicated support engineer
Self-hosted Open SourceFree
MIT License, all core platform features and APIs, scalability of Langfuse Cloud, deployment docs & Helm chart, community support
Self-hosted EnterpriseCustom Pricing
All Open Source features + management APIs, project-level RBAC, data retention policies, audit logs, ISO27001 reviews, dedicated support, support SLA

How Does Langfuse Compare to Competitors?

FeatureLangfuseLangSmithBraintrust
Core FunctionalityTracing, Evals, Prompt Mgmt, DatasetsTracing, Evals (LangChain focus)Tracing, Evals, Prompt Experimentation
Pricing ModelUnit-based ($29/mo Core)Seat-based ($39/user/mo)Usage-based (Pro $249/mo)
Free TierYes (50k units/mo)Yes (5k traces/mo, 1 seat)Yes (1M spans/mo)
Self-hostingYes (MIT OSS)Enterprise onlyYes
Enterprise SSOPro+Teams ($499/mo)EnterpriseEnterprise
Data Retention3 years (Pro)400 days extendedCustom
API AvailabilityYes (all plans)YesYes
Support OptionsCommunity→DedicatedEmail→DedicatedStandard→Enterprise
ComplianceSOC2, ISO27001, HIPAA BAAEnterpriseEnterprise
IntegrationsLLM Framework agnosticLangChain focusedFramework agnostic
Core Functionality
LangfuseTracing, Evals, Prompt Mgmt, Datasets
LangSmithTracing, Evals (LangChain focus)
BraintrustTracing, Evals, Prompt Experimentation
Pricing Model
LangfuseUnit-based ($29/mo Core)
LangSmithSeat-based ($39/user/mo)
BraintrustUsage-based (Pro $249/mo)
Free Tier
LangfuseYes (50k units/mo)
LangSmithYes (5k traces/mo, 1 seat)
BraintrustYes (1M spans/mo)
Self-hosting
LangfuseYes (MIT OSS)
LangSmithEnterprise only
BraintrustYes
Enterprise SSO
LangfusePro+Teams ($499/mo)
LangSmithEnterprise
BraintrustEnterprise
Data Retention
Langfuse3 years (Pro)
LangSmith400 days extended
BraintrustCustom
API Availability
LangfuseYes (all plans)
LangSmithYes
BraintrustYes
Support Options
LangfuseCommunity→Dedicated
LangSmithEmail→Dedicated
BraintrustStandard→Enterprise
Compliance
LangfuseSOC2, ISO27001, HIPAA BAA
LangSmithEnterprise
BraintrustEnterprise
Integrations
LangfuseLLM Framework agnostic
LangSmithLangChain focused
BraintrustFramework agnostic

How Does Langfuse Compare to Competitors?

vs LangSmith

The licensing models for LangFuse and LangSmith differ significantly. LangFuse provides an open source self-hosted version (MIT) and uses a unit-based pricing model, while LangSmith uses a seat-based pricing model with some potential for long-term "lock-in" to the LangChain framework. LangFuse may be preferable for large organizations with multiple framework usage, while LangSmith may be more suitable for organizations primarily using the LangChain framework.

For LLM applications where users will be executing requests on an LLM and will want to monitor the quality of their requests as well as the quality of the response provided by the LLM, LangFuse will provide more value due to its lower cost structure and greater flexibility for customers who do not use the LangChain framework.

vs Braintrust

The primary differences between BrainTrust and LangFuse revolve around the types of workflows they support. BrainTrust supports Continuous Integration/Continuous Deployment (CI/CD) based evaluation workflows, whereas LangFuse focuses on providing comprehensive observability for LLM applications. Additionally, BrainTrust offers a higher priced Pro plan compared to LangFuse ($249 vs $199).

If your organization's goal is to implement an LLM evaluation pipeline (and you have a high volume of requests), BrainTrust would likely be the better choice. However, if your organization wants to first establish an LLM observability pipeline to ensure the quality of their LLM application execution, then LangFuse would likely be the better choice.

vs Phoenix (Arize)

The primary areas of focus for the two platforms are different. BrainTrust has a stronger focus on the evaluation workflow for LLM applications, while LangFuse is focused on providing a more comprehensive view of LLM application execution (tracing + prompts + evaluations). Also, BrainTrust pricing appears to be more usage-based and potentially confusing, while LangFuse pricing appears to be tiered and easier to understand.

While both products offer a complete platform for supporting LLM applications, Phoenix seems to be a strong product for analytics visualization and LangFuse provides a more complete platform offering full LLM application execution (tracing + prompts + evaluations)

What are the strengths and limitations of Langfuse?

Pros

  • Offers flexible, cost-effective self-hosted solution with no vendor lock-in for all customers, regardless of LLM framework used.
  • Offers flexible, predictable usage-based pricing for customers, regardless of LLM framework used.
  • Generous Free Tier - 50k Units per Month for Small Projects & Proof-of-Concepts LangFuse offers a generous free tier that provides up to 50,000 units per month, making it suitable for proof-of-concepts and small projects.
  • Supports Multiple Frameworks - Beyond LangChain Ecosystem Unlike many other solutions, LangFuse supports multiple frameworks beyond just the LangChain ecosystem.
  • Enterprise Readiness Compliance - SOC2, ISO27001, HIPAA BAA Available LangFuse has enterprise readiness compliance available, including SOC2, ISO27001 and HIPAA BAA compliance.
  • Actively Developed - Regular Feature Releases and Community Engagement LangFuse is actively developed, providing regular feature releases and engaging with the community.
  • Flexible Deployments Options - Cloud, Self Hosted, AWS Marketplace Options LangFuse provides flexible deployment options, allowing you to deploy your solution to cloud environments or self hosted deployments as well as through the AWS marketplace.

Cons

  • Pricing Complexity Due To Unit-Based Pricing - Requires Usage Calculator As LangFuse has unit-based pricing, there is some complexity associated with determining how much the solution will cost based upon use. This is due to the need to use a usage calculator to determine how many units will be used by your application.
  • Enterprise Cost - $2499/Month Base + $300 Teams Add On The base price of LangFuse for an enterprise is $2499/month, with an additional $300 teams add on fee.
  • Limited Number of Users on Free Plan - Only 2 Seats Although LangFuse has a generous free tier, the number of users on the free plan is limited to only two users.
  • Only Community Support is Offered On The Free Plan - In App Support Not Included Until Core Plan Community support is offered on the free plan but in-app support is not included until you upgrade to the core plan.
  • Operations Overhead When Self Hosting - Requires DevOps For Production Scaling When you decide to self host your LangFuse solution, there are operations overhead requirements and this can include requiring a DevOps team for production scaling.
  • Limits On Data Retention - 30 Days Free, 90 Days Core vs Unlimited Competitors Data retention is a concern when using LangFuse. There are limits to data retention and these vary depending on the plan you choose. While competitors offer unlimited data retention on their plans, LangFuse has 30 days of data retention available on the free plan and 90 days available on the core plan.
  • Less Integrations Than Other Established Monitoring Tools - Smaller Ecosystem While LangFuse has a framework agnostic approach to integrating with various solutions and services, the overall ecosystem of LangFuse is smaller than that of many other established monitoring tools.

Who Is Langfuse Best For?

Best For

  • LLM engineering teams using multiple frameworks — Framework Agnostic and Flexibility of Being Open Source LangFuse provides a framework agnostic way of working with various solutions and services while also being open source and therefore offering flexibility when it comes to deploying and utilizing the solution.
  • Startups with variable LLM usage — Pricing Model Based Upon Actual Use - Unpredictable Cost One thing to consider with LangFuse is that the pricing model is based upon actual use which means that the amount of money you pay each month may vary unpredictably from one month to another.
  • Teams preferring self-hosting — Allows Unlimited Deployment Without Any Additional Costs From SaaS Since LangFuse is open source, you can utilize the solution without incurring any additional costs from SaaS providers.
  • Compliance-focused enterprises — SOC2, ISO27001, HIPAA BAA Compliance Available - On Pro+ Compliance with regulations such as SOC2, ISO27001 and HIPAA BAA is available with LangFuse's Pro+ plan.
  • Research and education projects — Generous Free Tier, Startup/Non-Profit Discounts, etc. In addition to the generous free tier LangFuse offers, they also have discounts available for startups and non-profits.

Not Suitable For

  • LangChain-only teams — Deeper Native Integration with LangSmith - Stay with LangSmith If you are currently utilizing LangSmith and want a deeper level of native integration with LangFuse, then you should continue to use LangSmith.
  • Solo developers on tight budgets — Only 2 Free Seats and Usage Limits - Consider Fully Free OSS-Only Alternatives If you require more than two free seats, or if you find that you are consistently at or above the usage limits of the free tier, then you should consider using fully free OSS-only alternatives to LangFuse.
  • Teams needing phone support — No Phone Support Listed - Dedicated Engineer for Enterprise Phone support is not provided with LangFuse. However, if you sign up for an enterprise account, you will get a dedicated engineer who can assist you.
  • Large enterprises wanting fixed per-seat pricing — Pricing Is Based On Actual Usage - May Be More Expensive Than Seat-Based Solutions Due to the fact that pricing is based on actual usage with LangFuse, you may end up paying more money than you would with seat-based solutions.

Are There Usage Limits or Geographic Restrictions for Langfuse?

Free Tier Units
50k units/month
Free Tier Users
2 users maximum
Free Tier Retention
30 days data access
Core Tier Units
100k units/month ($8/100k additional)
Core Tier Retention
90 days data access
Pro Tier Retention
3 years data access
High Rate Limits
Pro tier and above
Audit Logs
Enterprise only
SCIM API
Enterprise only
Self-hosting
Unlimited with OSS (Enterprise features require license)

Is Langfuse Secure and Compliant?

SOC2 & ISO27001Compliance reports available on Pro tier and above
HIPAA BAABusiness Associate Agreement available for healthcare customers
Enterprise SSOOkta and SSO enforcement available Teams add-on ($300/mo)
Fine-grained RBACProject-level roles and SSO enforcement on Teams/Enterprise
Audit LogsComplete user action trails available Enterprise tier
SCIM APIUser provisioning available Enterprise tier
Data Retention PoliciesCustom retention management Pro tier and above
Support SLAGuaranteed response times Enterprise tier only
Uptime SLAProduction-grade availability guarantees Enterprise tier

What Customer Support Options Does Langfuse Offer?

Channels
Community support (all tiers)Core tier and abovePro tier and aboveTeams add-on ($300/mo)Enterprise tier
Hours
Business hours for in-app, 24/7 Slack for Teams/Enterprise
Response Time
Community: best effort. Pro+: prioritized. Enterprise: SLA guaranteed.
Satisfaction
Trusted by 40,000+ builders
Specialized
Dedicated support engineer for Enterprise customers
Business Tier
Enterprise includes support SLA and uptime guarantees
Support Limitations
•No phone support available
•Free tier: community support only
•No guaranteed SLAs below Enterprise

What APIs and Integrations Does Langfuse Support?

API Type
REST API with OpenTelemetry (OTEL) support for traces and telemetry data
Authentication
API Keys (project and public keys), supports OTEL authentication
Webhooks
Not explicitly mentioned; realtime monitoring via dashboards and OTEL streaming
SDKs
Official Python, JavaScript/TypeScript, Go SDKs. OpenAI integration wrapper available
Documentation
Comprehensive - full API reference, integration guides, and interactive notebooks at langfuse.com/docs
Sandbox
Cloud-hosted free tier available for testing. Self-hosting option via Docker
SLA
Cloud SLA 99.9% uptime (paid plans). Self-hosted has no SLA
Rate Limits
Generous limits on cloud plans. Enterprise custom limits available
Use Cases
Ingest LLM traces, run evaluations via API, manage prompts, create datasets, monitor production LLM apps/agents

What Are Common Questions About Langfuse?

LangFuse - A Platform for Building Observability, Tracing, and Evaluation Systems for LLM Applications and AI Agents LangFuse is an open-source platform designed to provide the ability to build systems for observability, tracing, and evaluation of Large Language Models (LLM) applications and AI agents. LangFuse is able to capture detailed traces of LLM calls, agent execution paths, latency, costs, and errors.

LangFuse Uses OpenTelemetry Instrumentation Or Lightweight Decorators for Python/Js - Works With 20+ Frameworks Out Of The Box LangFuse utilizes OpenTelemetry instrumentation or lightweight decorators for Python/JS. LangFuse works with LangChain, LlamaIndex, OpenAI Agents SDK, CrewAI, Strands Agents, and 20+ frameworks out of the box.

The author describes LangFuse as having a production focus with its use of OpenTelemetry support, prompt management, and scalability through cloud or self-hosting; however, Phoenix focuses on offline analysis and workflow of research, therefore Phoenix is focused on a more limited scope of use than LangFuse, which can be used with a broader range of frameworks.

Yes. LangFuse Cloud meets the requirements for SOC 2 compliance that isolate customer data per project, encrypt both data at rest and in transit, and provides complete control over customer data when self-hosting the product, and does not process customer data for any purpose other than making the product function correctly.

Yes. LangFuse is completely open source and provides a single click install using Docker Compose. Users can take advantage of all of the cloud features if they choose to self-host the product. Cloud services are recommended for teams that do not have the resources to manage their own DevOps environment.

LangFuse offers free tier access to the product for users who are developing the application. Once the user decides to move into production, there are two plans available: Pro starting at $29/mo for production observability. In addition, LangFuse uses pay-as-you-go pricing models based on the number of traces and/or LLM tokens utilized by the user. Enterprise customers will need to contact LangFuse to discuss a customized pricing model.

LangFuse is model agnostic. It supports LLM models from OpenAI, Anthropic, Amazon Bedrock, Ollama, LiteLLM, and any LLM provider that allows you to utilize OpenTelemetry or SDKs to instrument the interaction. Regardless of the provider, LangFuse will trace every call made to the LLM engine.

LangFuse supports three types of methods for evaluating LLM performance: Black box method (Final Response), Glass Box Method (Trajectory Evaluation) and LLM-as-Judge. Additionally, LangFuse provides support for User Feedback, Manual Labeling of Data, Datasets and Customized Evaluation Pipelines that can be created through the LangFuse API.

Is Langfuse Worth It?

LangFuse is the leading open source observability platform for production LLM applications and AI Agents. With its unique combination of framework integration and OpenTelemetry standards, along with tracing, evaluation, prompt management, and dataset support, LangFuse represents a new level of infrastructure for LLM Engineering Teams. The ability to deploy in either cloud or self-hosted modes provides the same level of functionality for all team sizes.

Recommended For

  • Production-level LLM engineering teams creating AI agents
  • Organizations using multiple LLM frameworks (e.g., LangChain, LlamaIndex, CrewAI)
  • Large-scale production teams requiring low-latency and/or high-cost monitoring
  • Organizations that prefer an open-source first approach

!
Use With Caution

  • Small teams with minimal logging needs - may be too much overhead
  • Non-technical teams - require technical expertise to set up
  • Teams already invested in proprietary observability solutions

Not Recommended For

  • Traditional machine learning teams that are not utilizing LLMs/Agents
  • Single research project that will be evaluated only offline
  • Organizations seeking fully managed enterprise-level support only
Expert's Conclusion

Any team developing and implementing production-ready LLM applications and/or agents will need this.

Best For
Production-level LLM engineering teams creating AI agentsOrganizations using multiple LLM frameworks (e.g., LangChain, LlamaIndex, CrewAI)Large-scale production teams requiring low-latency and/or high-cost monitoring

What do expert reviews and research say about Langfuse?

Key Findings

LangFuse is an established open-source market leader in LLM observability, and supports natively OpenTelemetry, as well as integrates with over 20 different application frameworks. LangFuse covers the entire lifecycle of an application or service -- from tracing through evaluation, to production monitoring. LangFuse has strong adoption with multiple agent frameworks including, but not limited to, OpenAI's Agent SDK, Strands' Agents, and CrewAI.

Data Quality

Excellent - comprehensive documentation, active GitHub repo (langfuse/langfuse), detailed integration guides, and production case studies from AWS Bedrock, Hugging Face, OpenAI.

Risk Factors

!
Growing competitive landscape; emerging alternative solutions
!
When self-hosting, maintenance is required for a company's internal IT/DevOps department.
!
Because of rapid development in the field, it can be necessary to update the solution frequently.
Last updated: February 2026

What Additional Information Is Available for Langfuse?

Open Source Community

The LangFuse GitHub repository has a large user base of 20,000+ users; includes 50+ active contributors; regularly publishes new versions and new integrations with other services; and provides a permissive license under the terms of the MIT License which allows companies to utilize LangFuse for both free and commercial purposes.

Framework Ecosystem

LangFuse has deep integrations with many other popular tools including LangChain, LlamaIndex, LiteLLM, OpenAI Agents SDK, CrewAI, Strands Agents, PydanticAI, and smolagents. LangFuse also supports any framework via OpenTelemetry.

Deployment Options

LangFuse offers three types of deployment options: LangFuse Cloud (managed); LangFuse self-hosted via Docker Compose; and LangFuse self-hosted via Kubernetes. All options offer all the same functionality.

Industry Recognition

LangFuse has been featured in several AWS blog posts, and is currently integrated into several Hugging Face offerings. LangFuse is the industry standard for agent-level observability. Multiple production teams have successfully scaled LangFuse for their own large-scale production LLM monitoring.

What Are the Best Alternatives to Langfuse?

  • •
    Phoenix (Arize): LangFuse is an open-source tool for LLM evaluation and experimentation; better suited for research/offline workflows; offers some monitoring capabilities but fewer than LangFuse; missing are features around prompt management and broad framework integrations; best-suited for data science teams (arize.com/phoenix).
  • •
    Helicone: An open-source OpenAI proxy that tracks costs and caches responses; however, does not provide agent-level tracing or evaluation features; best-suited for teams utilizing exclusively OpenAI compatible APIs (helicone.ai).
  • •
    Traceloop: A commercially-focused, closed-source LLM observability offering; offers strong OpenTelemetry support; however, higher cost than LangFuse; and less developed framework ecosystem; best-suited for companies that require vendor support (traceloop.com).
  • •
    OpenLLMetry: An open-source lightweight tracer specifically designed for LLMs. The most versatile tracer — however it also has the highest level of customization required and is missing higher-level features such as evaluation and prompt management. Best suited for DevOps teams that are building their own custom stack.
  • •
    Weights & Biases (W&B Weave): A MLOps platform with a specific LLM tracing component; better at tracking experiments but weaker at tracking production metrics. More expensive if you're just going to be using it for observability — best for teams that are already using W&B for your ML workloads.

Core Experiment Tracking Features

Trace Collection & Logging

Allows users to capture complete end-to-end traces of all LLM application workflows including, but not limited to: prompts, responses, retrieval, embeddings, and agent actions.

Session & User Tracking

Traces can track multi-turn conversations as sessions and identify users across each interaction.

Latency & Cost Monitoring

Automatically tracks inference latency and token cost for LLM applications for performance optimization.

Evaluation Scoring

Users can attach LLM-as-a-judge, manual, or custom evaluation scores to traces for quality tracking.

Dataset Management

Creates test sets and benchmarks from production traces for reproducible experimentation.

Prompt Versioning

Provides central prompt management capabilities with versioning and caching for reproducibility across experiments.

Performance & Scalability Benchmarks

< 100 ms
Trace Ingestion Latency
10,000+ traces/min
Concurrent Trace Volume
< 2 seconds
Dashboard Load Time
1,000,000+ sessions
Maximum Sessions per Project
< 150 ms
API Response Time (p95)
< 2 minutes
Time-to-First-Trace

Framework & Tool Integration Support

OpenAILangChainLlamaIndexLiteLLMPython SDKJavaScript SDKOpenTelemetryHugging FaceAnthropicMistralCohereVercel AIHaystackCrewAIAutoGenPostgreSQL

Compliance & Data Governance Capabilities

Trace Lineage TrackingComplete audit trail from input to LLM output across frameworks
Data Retention ControlsConfigurable retention policies for traces and sessions
SOC 2 ComplianceEnterprise cloud hosted with security certifications
Role-Based Access ControlProject-based permissions and team management
Data Encryption (At-Rest)PostgreSQL encryption with customer-managed keys available
Data Encryption (In-Transit)TLS encryption for all API communications
Self-Hosted DeploymentOpen source self-hosting for full data control
EU Data ResidencyCloud regions available in EU
GDPR Data DeletionComplete trace and session deletion APIs
Audit LoggingAccess and modification logs for compliance

Deployment & Infrastructure Specifications

Cloud-Hosted SaaS
Yes
Self-Hosted Open Source
Yes
Docker Deployment
Yes
Kubernetes Support
Yes
Multi-Tenancy
Yes
Horizontal Scaling
Yes
Database Backend
PostgreSQL
High Availability
Yes
Geographic Regions
US, EU
API Rate Limits
Unlimited (self-hosted)

Production Observability & Monitoring

End-to-End LLM Tracing

Captures all context related to an LLM call including: prompts, embeddings, retrieval, and LLM calls within one trace.

Latency & Cost Analytics

Provides real-time monitoring of inference latency, token usage, and API costs across various providers.

Session Analytics

Tracks multi-turn conversation sessions by identifying users and analyzing behavior across sessions.

LLM-as-a-Judge Evaluations

Automatically evaluates the quality of production traces on a step-by-step basis.

Custom Metrics & Scores

Enables users to attach numeric, boolean, and categorical evaluation results to any step in a trace.

Agent Workflow Visualization

Graphically represents complex agent interactions and decision flows through graph-based representations.

Production Trace Datasets

Converts production failures into evaluation data sets for continued development and improvement.

Primary Use Cases & Adoption Scenarios

Organization TypePrimary Use CaseKey BenefitTypical Scale
AI Engineering TeamsLLM Application DebuggingPinpoint failures across complex chains and agents1,000-10,000 traces/day
Production LLM PlatformsCost & Latency OptimizationIdentify expensive prompts and slow inference pathsReal-time monitoring
Agent Development TeamsWorkflow TraceabilityVisualize and debug multi-step agent decision flows100-1,000 sessions/day
Quality EngineeringAutomated LLM EvaluationProduction trace scoring with LLM-as-a-judgeContinuous evaluation
Cross-Functional TeamsPrompt Management & CollaborationVersion control and iterate prompts without latency impact10-100 team members
Enterprise MLOpsLLM Observability PlatformCentralized tracing across all LLM applications1M+ traces/month

LLM Observability Platform Capability Comparison

CapabilityLangfuseWeights & BiasesPhoenix (Arize)HeliconePostHog
Open Source Core✓ Complete✗ Proprietary✓ Partial✗ Proprietary✗ Analytics only
LLM Agent Tracing✓ Native⚠ Limited✓ Yes⚠ Basic✗ No
Prompt Management✓ Built-in✗ No✗ No✗ No✗ No
LLM-as-a-Judge✓ Native⚠ Custom✓ Yes✗ No✗ No
Self-Hosted✓ Full⚠ Limited✗ Cloud only⚠ Limited✓ Partial
Framework Integrations50+30+20+10+5+
Multi-Turn Sessions✓ Native✓ Yes⚠ Limited✓ Basic✗ No
Cost Tracking✓ All providers✓ OpenAI only✓ Partial✓ OpenAI focused✗ No
Time-to-First-Value< 2 min< 5 min< 10 min< 5 min< 15 min

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