AI visibility report
AI visibility report for Langfuse in LLM Observability Evals & Gateways.
Outside the top three on 13 of the 25 prompts buyers actually ask.
Braintrust is cited on 7 of those losses.
Free trial. Setup comes pre-filled for Langfuse.
Also benchmarked
Langfuse appears in another vertical
Track Langfuse across these prompts daily.
Start free trialStill absent from 86.7% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
PerplexityHow to read this. Langfuse appears in 13.3% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Langfuse is losing
Prompts where competitors are visible and Langfuse is not.
These prompt-level losses are the first prompts to track and repair.
Where Langfuse is winning5
Which AI observability platforms can be self-hosted with one command using Docker Compose?
Avg # 1.0 · 1 platform
Which LLM eval platforms support running automated evaluations on production traces with custom metrics?
Avg # 1.5 · 2 platforms
What tools let me drill into a single user session to debug exactly what my agent did at each step?
Avg # 2.0 · 1 platform
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration?
Avg # 3.0 · 2 platforms
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows?
Avg # 6.0 · 1 platform
Where Langfuse is losing5
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK?
Competitors on 3 platforms
Track this promptWhat are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines?
Competitors on 3 platforms
Track this promptWhat AI eval platforms support on-premise or VPC deployment for regulated industries?
Competitors on 2 platforms
Track this promptWhich observability tools include real-time alerting on quality drops, not just latency?
Competitors on 2 platforms
Track this promptWhich evaluation platforms let me convert development-time evals into production guardrails automatically?
Competitors on 2 platforms
Track this prompt
Track Langfuse daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Langfuse is an open-source LLM engineering platform, founded in 2022 and acquired by ClickHouse in January 2026, that helps development teams build, monitor, and continuously improve AI applications and agents. Licensed under MIT and self-hostable via Docker or Kubernetes, the platform consolidates LLM observability (tracing), prompt management, evaluation, and experimentation into a single integrated workflow. It processes over 10 billion observations per month, serves 2,300+ customers including 19 of the Fortune 50, and has accumulated more than 26,000 GitHub stars with 300+ contributors. Langfuse is OpenTelemetry-native, framework-agnostic across 80+ integrations, and is backed by a ClickHouse OLAP architecture built for high-throughput ingestion and millisecond-scale analytics at enterprise scale.
Langfuse is an open-source, MIT-licensed LLM engineering platform that provides end-to-end tooling for the full AI application development lifecycle: hierarchical trace-based observability, versioned prompt management with one-click deploys, multi-method evaluation (LLM-as-a-judge, human annotation, user feedback, custom pipelines), structured experiment comparison, and cost/latency/quality analytics dashboards. It is OpenTelemetry-native, integrates with 80+ frameworks and model providers, and can be deployed on Langfuse Cloud or self-hosted on Docker, Kubernetes, AWS, GCP, or Azure. Since its January 2026 acquisition by ClickHouse, Langfuse runs on a ClickHouse OLAP backend enabling millisecond-latency queries over billions of monthly observations.
Key Facts
- Founded
- 2022
- HQ
- Berlin, Germany
- Founders
- Max Deichmann, Clemens Rawert, Marc Klingen
- Employees
- 11-50
- Funding
- $4.5M
- Customers
- 2,300+
- Status
- Acquired by ClickHouse (January 2026)
Target users
Key Capabilities10
- Hierarchical LLM trace and span observability with agent graph visualization
- OpenTelemetry-native ingestion with 80+ framework and model provider integrations
- Prompt management with versioning, environment labels, one-click deploy/rollback, and client/server-side caching
- LLM-as-a-judge, human annotation queues, user feedback, and custom evaluation pipelines
- Structured experiments for comparing prompt versions and models against datasets
- Cost, latency, and quality analytics dashboards with automated alerting
- Full self-hosting support (Docker Compose, Kubernetes/Helm, AWS/GCP/Azure Terraform) under MIT license
- Enterprise security: SOC 2 Type II, ISO 27001, GDPR, HIPAA-eligible; EU and US data regions
- ClickHouse OLAP backend for querying billions of traces at millisecond latency
- API-first architecture with REST API, typed SDKs, MCP server, and CLI for custom LLMOps workflows
Key Use Cases8
- Production debugging and root-cause analysis of LLM application and agent failures
- Continuous quality monitoring of LLM outputs across cost, latency, and accuracy dimensions
- Prompt version control and team collaboration for iterative prompt engineering
- Offline and online LLM evaluation using LLM-as-a-judge or human annotation
- Pre-deployment regression testing of AI agents against golden datasets
- Multi-team observability for enterprises with multiple concurrent AI products
- Self-hosted LLM observability in air-gapped or regulated environments
- RAG pipeline tracing and retrieval-quality evaluation
Langfuse customer outcomes
100+ internal users across 11 teams
Khan Academy deployed Langfuse in April 2024 to power observability for its Khanmigo AI tutor. Adoption spread to over 100 users across 7 product teams and 4 infrastructure teams, enabling rapid iteration and debugging across dozens of AI features built on a custom Go client agai
50% deflection rate; 30% BPO cost reduction; 300,000 monthly requests automated
SumUp used Langfuse to build and scale AI-powered first-level merchant support across 35+ markets over 18 months, growing from 1,000 to 600,000 monthly AI conversations. The implementation achieved a ~50% conversation deflection rate — 300,000 monthly requests handled without hum
Recent Trend
How AI describes Langfuse3
...lity platforms today: | Platform | Self-hosted | Docker Compose | Open Source | Focus | | --- | --- | --- | --- | --- | | Langfuse | Yes | Yes | Yes | Tracing, evals, prompt management | | Phoenix | Yes | Yes | Yes | Tracing, evaluations, experimentatio...
Which AI observability platforms can be self-hosted with one command using Docker Compose?
...LangChain/LangGraph ecosystems | Full execution graph, prompts, tool calls, state, token usage, replay, run comparison | | Langfuse | Framework-agnostic tracing | Trace tree, spans, tool calls, costs, latency, sessions, evaluations | | Helicone | Fast se...
What tools let me drill into a single user session to debug exactly what my agent did at each step?
Langfuse paired with a gateway/proxy for tracing every request \[4\] Typical change: Python client = OpenAI( api_key=API_KEY, base_url="https://your-gateway.example.com/v1") instead o...
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture?
Most cited sources8
- 8
Langfuse
langfuse.com·Documentation
- 7
Langfuse
langfuse.com·Documentation
- 5
Langfuse
langfuse.com·Documentation
- G4
GitHub - langfuse/langfuse: 🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI...
github.com·Documentation
- 4
Langfuse
langfuse.com·Documentation
- 4
Overview - Langfuse
langfuse.com·Documentation
Alternatives in LLM Observability Evals & Gateways6
Langfuse positions itself as the leading open-source, framework-agnostic LLM engineering platform — the developer-controlled alternative to proprietary observability tools.
- Its core differentiation rests on three pillars: an MIT-licensed codebase that is fully self-hostable at no cost, usage-based pricing with no per-seat charges, and OpenTelemetry-native architecture that avoids framework lock-in.
- Against LangSmith (LangChain), Langfuse emphasizes stack neutrality (works with any framework/model).
- Against Arize and Galileo, it emphasizes open source and self-hosting.
- Against Helicone and Portkey, it offers a more complete platform (tracing + prompt management + evals + experiments in one product).
- Since its January 2026 acquisition by ClickHouse, Langfuse also leverages ClickHouse's OLAP infrastructure for high-throughput ingestion and millisecond-latency analytics at enterprise scale.
Reviews
Praised
- Easy and fast setup with minimal code changes
- Detailed trace visibility and hierarchical span views
- Reliable SDKs that 'just work' across frameworks
- Strong latency and cost analytics out of the box
- Open-source and self-hostable with full feature parity
- No per-seat pricing — cost scales with usage not headcount
- Active community, responsive support, and rapid release cadence
- Excellent documentation and integration breadth (80+ connectors)
Criticized
- Hobby plan limited to 2 users — restrictive for small teams
- Some users report outgrowing observability depth for complex agentic workflows
- Full evaluation pipeline setup has a learning curve
- Enterprise SSO and fine-grained RBAC require a paid add-on on top of Pro
- No built-in LLM gateway or proxy routing
- Voice AI use cases are not natively supported
Developer sentiment toward Langfuse is strongly positive in community channels. Product Hunt reviewers highlight detailed trace visibility, reliable SDKs, fast latency/cost analytics, and a pricing model that suits early-stage teams. Common praise includes easy setup, responsive open-source community, rapid release cadence, and flexibility of self-hosting. Criticisms are limited but include the Hobby plan's 2-user cap, a learning curve for configuring full evaluation pipelines, and some users reporting they outgrew its observability depth for highly complex agentic workflows. No verified aggregate score from G2 or Gartner Peer Insights was available at time of research.
Pricing
Langfuse Cloud uses a freemium, usage-based model priced on billable units (traces, observations, scores) rather than seats. Hobby is free (50k units/month, 30-day retention, 2 users). Core is $29/month (100k units included, 90-day retention, unlimited users, $8/100k overage). Pro is $199/month (100k units, 3-year retention, SOC2/ISO27001/HIPAA, $8/100k overage). Enterprise is $2,499/month (custom rate limits, audit logs, SCIM, SLA, dedicated support engineer; custom volume pricing with yearly commitment). A Teams add-on at $300/month adds Enterprise SSO, fine-grained RBAC, and a dedicated Slack/Teams support channel. Volume overage rates decrease from $8 to $6/100k at 50M+ units/month. Self-hosting the full product is free under the MIT license. Discounts available for early-stage startups (50% off, first year), research/students, non-profits, and open-source projects.
Limitations
- No built-in LLM gateway or proxy routing (relies on LiteLLM integration for proxy-based logging).
- Free Hobby tier is limited to 2 users and 50,000 observations/month with only 30-day data retention.
- Enterprise SSO, fine-grained RBAC, and dedicated Slack support require a Teams add-on ($300/month) on top of the Pro plan.
- Custom volume pricing and AWS Marketplace billing require a yearly Enterprise commitment.
- Not designed for voice AI use cases (concurrent call simulation, ASR error detection).
- Self-hosted deployments require managing ClickHouse, Redis, and S3/blob storage infrastructure.
- Some users report outgrowing the observability depth for very complex agent workflows.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation2/5 cited (40%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing0/5 cited (0%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness1/5 cited (20%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run3/5 cited (60%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging2/5 cited (40%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
Turn this into your team dashboard
Sign up to unlock project-level analytics, daily tracking, actionable insights, custom prompt configurations, adoption tracking, AI traffic analytics and more.
Free trial. Setup comes pre-filled from this report.