AI visibility report
Langfuse ranks #3 in LLM Observability Evals & Gateways AI search.
Outside the top three on 13 of the 25 prompts buyers actually ask.
Braintrust is cited on 10 of those losses.
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Also benchmarked
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Track Langfuse across these prompts daily.
Start free trial#3 among 11 vendors · still absent from 83.2% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
Google AI Mode
Perplexity
Bing CopilotVisible, but narrative can improve. Langfuse ranks #3 on presence but #4 on sentiment. The brand appears relatively often, but competitors may be getting more favorable language when they appear.
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
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?
Avg # 1.0 · 1 platform
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?
Avg # 1.0 · 2 platforms
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics?
Avg # 1.0 · 1 platform
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?
Avg # 1.0 · 1 platform
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?
Avg # 1.0 · 2 platforms
Where Langfuse is losing5
Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day?
Competitors on 5 platforms
Track this promptWhich evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team?
Competitors on 4 platforms
Track this promptI'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?
Competitors on 4 platforms
Track this promptWhich LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?
Competitors on 4 platforms
Track this promptWhat are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?
Competitors on 3 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
Short answer: Platforms like Confident AI, Braintrust, Future AGI, Langfuse, and Promptfoo integrate directly with version control (Git-style branching, CI/CD hooks, regression gates) so prompt regressions can be tied back to specific commits or...
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?
No SQL or custom wiring required — unlike Langfuse or Arize AI, which route quality workflows through engineering.confident-ai.comconfident-ai.com.
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge.
Vendor lock-in: Proprietary observability platforms (Langfuse, LangSmith, Helicone) offer richer LLM features but require sending data to their backend.
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?
Most cited sources8
11Langfuse
langfuse.com·Documentation
- 10
AI Agent Observability, Tracing & Evaluation with Langfuse - Langfuse
langfuse.com·Blog Post
- 9
OpenTelemetry (OTEL) for LLM Observability - Langfuse
langfuse.com·Documentation
- 8
Migrate from Arize Phoenix to Langfuse - Langfuse
langfuse.com·Product Page
- 6
Playground - Langfuse
langfuse.com·Documentation
- 5
PII masking patterns for LLM applications - 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 | Bing Copilot | Google AI Mode | Perplexity | ChatGPT |
|---|---|---|---|---|---|
Capability3/5 cited (60%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | Your brand 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 and a competitor were cited |
Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | 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 |
What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning? | A competitor was cited | 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 |
Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options? | 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 | A competitor was cited |
Developer Experience4/5 cited (80%) | |||||
Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development? | 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 | Your brand and a competitor were cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm looking for an LLM observability platform with a great team collaboration workflow — where engineers and PMs can both review trace quality without SQL knowledge. | Your brand was cited | A competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited |
What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | A competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited |
Integrations & Ecosystem4/5 cited (80%) | |||||
I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | 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 | A competitor was cited |
Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | Your brand was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | Your brand and a competitor were cited |
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Performance & Reliability2/5 cited (40%) | |||||
What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs? | 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 | A competitor was cited |
Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users? | Your brand was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited |
Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Your brand and a competitor were cited |
Setup & First Run2/5 cited (40%) | |||||
I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | 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 LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Your brand was cited |
What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Your brand 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 | 31.2% | 28.3% | 2.4% | 0.0% | 43.2% | #4.0 | +0.43 |
| 2 | LangChain | 20.8% | 13.7% | 3.2% | 0.0% | 51.2% | #4.4 | +0.45 |
| 3 | Langfuse | 16.8% | 13.3% | 4.8% | 3.2% | 56.8% | #2.8 | +0.48 |
| 4 | Confident AI | 16.0% | 17.3% | 0.0% | 0.0% | 12.8% | #5.7 | +0.42 |
| 5 | Galileo | 12.8% | 7.5% | 0.0% | 12.8% | 13.6% | #3.5 | +0.38 |
| 6 | Arize AI | 8.8% | 8.0% | 0.0% | 4.0% | 8.0% | #4.3 | +0.53 |
| 7 | Traceloop | 5.6% | 4.0% | 0.0% | 4.8% | 6.4% | #4.4 | +0.37 |
| 8 | Portkey | 4.0% | 2.7% | 0.8% | 0.0% | 18.4% | #3.0 | +0.26 |
| 9 | LiteLLM | 4.0% | 2.7% | 0.8% | 0.0% | 0.0% | #3.2 | +0.56 |
| 10 | Helicone | 1.6% | 0.9% | 0.8% | 0.8% | 24.8% | #5.0 | +0.45 |
| 11 | Patronus AI | 1.6% | 1.8% | 1.6% | 0.8% | 3.2% | #8.5 | +0.79 |
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