AI visibility report
AI visibility report for Langfuse in AI/ML Infrastructure & LLM Tools.
Outside the top three on 14 of the 25 prompts buyers actually ask.
Braintrust is cited on 11 of those losses.
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Track Langfuse across these prompts daily.
Start free trialStill absent from 95.3% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
ChatGPT
Bing Copilot
Google AI Mode
Perplexity
GrokHow to read this. Langfuse appears in 4.7% 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 winning2
Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options?
Avg # 1.0 · 1 platform
Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week?
Avg # 6.0 · 1 platform
Where Langfuse is losing5
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates?
Competitors on 3 platforms
Track this promptWhich LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps?
Competitors on 3 platforms
Track this promptWhich LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours?
Competitors on 3 platforms
Track this promptWhat are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure?
Competitors on 2 platforms
Track this promptWhat tools let you set up a RAG pipeline evaluation framework to measure retrieval quality and answer accuracy before going to production?
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 (YC W23) and acquired by ClickHouse in January 2026. It provides a unified suite for LLM observability, prompt management, evaluation, and experiment tracking, enabling engineering and product teams to debug, monitor, and iteratively improve AI applications and agents in production. Built on OpenTelemetry with a ClickHouse OLAP backend, it processes over 10 billion observations per month and serves 2,300+ customers including 19 of the Fortune 50. The platform is MIT-licensed, supports full self-hosting across major cloud providers, and integrates with 80+ frameworks and model providers. It claims 26,000+ GitHub stars and 100,000+ engineers building on the platform.
Langfuse is an open-source LLM engineering platform that covers the full AI application development lifecycle: hierarchical tracing and agent observability (OTel-native), prompt management with versioning and caching, automated and human evaluation pipelines, structured experiments, and production cost/latency dashboards. It is framework- and model-agnostic, self-hostable under MIT license, and integrates with 80+ tools including LangChain, LiteLLM, LlamaIndex, OpenAI, and Anthropic. Following its January 2026 acquisition by ClickHouse, its ClickHouse-backed data layer supports billions of monthly observations at enterprise scale.
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 (Jan 2026)
Target users
Key Capabilities10
- Hierarchical LLM and agent tracing with OpenTelemetry support
- Prompt management with versioning, caching, and one-click deployment/rollback
- LLM-as-a-judge automated evaluation with boolean and scored outputs
- Human annotation queues and collaborative labeling workflows
- Dataset management for offline evals and structured experiments
- Cost, latency, and quality dashboards with custom metadata filtering
- Prompt playground for testing on real production traces
- Structured experimentation framework with side-by-side comparison
- Full self-hosting (MIT-licensed) on Docker, Kubernetes, AWS, GCP, Azure
- REST API, Query SDK, and S3/blob storage export for data portability
Key Use Cases8
- Production LLM application debugging and root-cause analysis
- AI agent observability and multi-step trace inspection
- Prompt optimization and version-controlled iteration
- Automated and human-in-the-loop evaluation pipelines
- RAG pipeline monitoring and retrieval quality assessment
- LLM cost attribution and optimization across models and teams
- Continuous improvement loops from production data to prompt/model changes
- Compliance-sensitive deployments requiring on-premises or VPC self-hosting
Langfuse customer outcomes
30% reduction in external BPO cost
Merck's Chief Data & AI Officer credited Langfuse-powered AI with deflecting 50% of support conversations to AI, reducing reliance on external BPO providers.
< 8 minutes average customer support resolution time
Khan Academy uses Langfuse to debug and monitor its Khanmigo AI tutor across 7 product teams and 4 infrastructure teams, enabling rapid issue diagnosis when customer issues arise.
35+ market rollout in 18 months
SumUp used Langfuse tracing, prompt management, and evaluation to roll out an AI-powered merchant support assistant across 35+ global markets serving 4 million merchants.
Recent Trend
How AI describes Langfuse3
Langfuse : An open-source, flexible tool that supports asynchronous logging to ensure zero latency on the main thread, with strong tracing and prompt management.
What ML experiment tracking tools handle multi-user collaboration well — so multiple data scientists can work on the same project without stepping on each other's runs?
Langfuse (Top Choice for Observability + Evals) Langfuse is an open-source platform that brings together prompt management, tracing, and evaluation.
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps?
Langfuse : An open-source, flexible alternative (can be self-hosted) that excels at logging nested tool usage, LLM calls, and intermediate outputs.
I'm evaluating managed LLM inference platforms versus self-hosted GPU instances for a high-traffic workload — what are the key trade-offs and what should I look at?
Most cited sources8
11Langfuse
langfuse.com·Documentation
- 4
Parquet exports to blob storage - Langfuse
langfuse.com·Product Page
- 4
Open Source LLM API & Data Platform - Langfuse
langfuse.com·Product Page
- 3
Export to Blob Storage - Langfuse
langfuse.com·Product Page
- 3
Blob Storage Export Field Reference - Langfuse
langfuse.com·Product Page
- 2
AI Agent Observability, Tracing & Evaluation with Langfuse - Langfuse
langfuse.com·Blog Post
Alternatives in AI/ML Infrastructure & LLM Tools6
Langfuse positions itself as the most widely adopted open-source LLM engineering platform, differentiating on MIT-licensed self-hosting, framework and model agnosticism (OpenTelemetry-native), and a unified platform covering the full dev loop—tracing, prompt management, evals, and experiments—without vendor lock-in.
- Its primary foil is LangSmith (LangChain's proprietary observability layer), against which Langfuse competes on infrastructure control, usage-based pricing transparency, and open community.
- After being acquired by ClickHouse in January 2026, it gains enterprise-scale data infrastructure backing while maintaining open-source commitments.
Reviews
Praised
- Ease of integration and 'just works' SDK experience
- Detailed hierarchical tracing with cost and latency visibility
- Open-source and self-hosting flexibility
- Strong prompt management and version control
- Responsive and knowledgeable support team
- Framework and model agnosticism
- Competitive pricing versus LangSmith and Helicone
Criticized
- Native UI-based alerting less mature than proprietary competitors
- Free tier limited to 2 users and 50k monthly observations
- SSO and fine-grained RBAC gated behind paid add-ons
- Self-hosting requires managing multiple infrastructure dependencies (ClickHouse, Redis, S3)
Langfuse has no verified reviews on G2 at time of research. On Product Hunt, user sentiment is strongly positive: reviewers consistently praise ease of integration, detailed hierarchical tracing, strong cost and latency analytics, open-source flexibility, and responsive support. Common themes include 'just works' SDK experience, valuable self-hosting control, and meaningful comparisons favoring Langfuse over LangSmith and Helicone for infrastructure control and pricing. No significant negative themes appear in public Product Hunt reviews; noted gaps in third-party comparisons include less mature native UI alerting versus LangSmith.
Pricing
Langfuse Cloud offers four tiers: Hobby (free, 50k units/month, 2 users, 30-day data retention); Core ($29/month, 100k units included, $8/100k additional, 90-day retention, unlimited users); Pro ($199/month, 100k units included, $8/100k additional with volume discounts down to $6/100k at 50M+ units, 3-year retention, SOC2/ISO27001 reports, HIPAA-eligible); Enterprise ($2,499/month, custom rate limits, audit logs, SCIM, uptime SLA, dedicated support engineer, AWS Marketplace billing). A Teams add-on ($300/month) unlocks SSO, RBAC, and dedicated Slack support on Pro. Self-hosting is fully free under the MIT license. Discounts available for early-stage startups (50% off first year), researchers/students, non-profits, and open-source projects.
Limitations
- Free Hobby tier caps at 50k observations/month and 2 users with only 30 days of data access.
- Native UI-based alerting is less mature than some proprietary competitors (e.g., LangSmith offers out-of-box Slack/email threshold alerts without requiring API or webhook setup).
- Enterprise SSO, fine-grained RBAC, and dedicated Slack support require paid add-ons.
- Self-hosting requires managing ClickHouse, Redis, and S3-compatible blob storage dependencies.
- No built-in LLM gateway or proxy; depends on integrations such as LiteLLM for that layer.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Bing Copilot | Google AI Mode | ChatGPT | Perplexity | Gemini Search | Grok |
|---|---|---|---|---|---|---|
Capability0/5 cited (0%) | ||||||
Which AI observability tools are best at detecting prompt injection attempts and guardrail violations in production LLM apps? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What ML platforms handle dataset versioning alongside model versioning so you can reliably reproduce a training run from six months ago? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating managed LLM inference platforms versus self-hosted GPU instances for a high-traffic workload — what are the key trade-offs and what should I look at? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience3/5 cited (60%) | ||||||
Which AI infrastructure platforms support running the same orchestration logic locally against a mock LLM before deploying to production? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What ML experiment tracking tools handle multi-user collaboration well — so multiple data scientists can work on the same project without stepping on each other's runs? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited |
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Integrations & Ecosystem1/5 cited (20%) | ||||||
What AI infrastructure platforms handle multi-model setups well — letting you switch between LLM providers and open-source models without rewriting application code? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which AI/ML platforms have the best compliance story for SOC 2 and data residency — ensuring training data and model outputs stay in a specific region? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM observability platforms support exporting trace data to BigQuery or Snowflake for custom analysis? | A competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Performance & Reliability1/5 cited (20%) | ||||||
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited |
Which managed LLM inference platforms handle cold starts well — is there a way to keep a model warm without paying for idle GPU time? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM infrastructure platforms give the best cost-to-latency balance for a high-throughput app doing 10,000 requests per hour? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Setup & First Run1/5 cited (20%) | ||||||
What platforms can affordably serve a fine-tuned 7B parameter model with low latency for a production app without requiring a dedicated ML team? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited |
What tools let you set up a RAG pipeline evaluation framework to measure retrieval quality and answer accuracy before going to production? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What are the best ML experiment tracking tools for a team currently logging metrics to spreadsheets — which ones get you value fast with minimal setup? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 13.3% | 38.2% | 0.0% | 0.7% | 16.7% | #4.0 | +0.45 |
| 2 | LangChain | 4.7% | 11.8% | 2.0% | 0.0% | 26.7% | #3.2 | +0.50 |
| 3 | MLflow | 4.7% | 15.8% | 0.0% | 0.0% | 14.0% | #4.0 | +0.56 |
| 4 | Langfuse | 4.7% | 18.4% | 1.3% | 1.3% | 16.7% | #5.6 | +0.46 |
| 5 | Weights & Biases | 2.0% | 3.9% | 0.7% | 0.0% | 14.7% | #4.0 | +0.50 |
| 6 | Fireworks AI | 1.3% | 2.6% | 0.7% | 0.7% | 5.3% | #1.0 | -0.08 |
| 7 | Comet ML | 1.3% | 2.6% | 0.0% | 0.0% | 2.0% | #2.5 | +0.20 |
| 8 | Modal | 1.3% | 2.6% | 0.0% | 1.3% | 0.0% | #3.0 | +0.25 |
| 9 | Helicone | 1.3% | 3.9% | 0.7% | 0.7% | 11.3% | #6.3 | +0.69 |
| 10 | Anyscale | 0.0% | 0.0% | 0.0% | 0.0% | 1.3% | — | — |
| 11 | LiteLLM | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 12 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 4.0% | — | — |
| 13 | Together AI | 0.0% | 0.0% | 0.0% | 0.0% | 8.7% | — | — |
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