# Langfuse AI visibility in LLM Observability Evals & Gateways

Canonical: https://devtune.ai/verticals/llm-observability-evals-gateways/langfuse

[Website](https://langfuse.com/)

Latest AI responses: 2026-10-29
Prompts: 25
Runs: 5


## Platforms

- google-ai
- google-ai-mode
- chatgpt-search
- perplexity
- bing-copilot-search

Rank: 2
Total brands: 11
Measured responses: 125
Presence percent: 28.000000000000004
Share of voice percent: 23.692307692307693
Average position: 4.298701298701299
Docs presence percent: 16
Blog presence percent: 2.4
Brand mention percent: 64


## Profile

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.
Product summary: 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 capabilities

- 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



### Target users

- AI/ML engineers building and deploying LLM applications and agents
- Platform and infrastructure teams managing LLMOps at enterprise scale
- Prompt engineers and product teams iterating on LLM-powered features
- Data scientists and QA teams running LLM evaluations and benchmarks
- Security-conscious enterprises requiring self-hosted or SOC2/ISO27001-compliant deployments
- Startups and open-source projects needing cost-effective, usage-based LLM observability



### Key use cases

- 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

Integrations ecosystem: Langfuse supports 80+ integrations spanning major agent frameworks (LangChain, LlamaIndex, CrewAI, Vercel AI SDK, Pydantic AI, Google ADK, AutoGen, DSPy, Mastra, OpenAI Agents SDK, Claude Agent SDK), model providers (OpenAI, Anthropic, Amazon Bedrock, Azure OpenAI, Google Gemini, Mistral, Groq, vLLM, xAI, Ollama, Google Vertex AI), no-code platforms (Dify, Langflow, OpenWebUI, n8n), LLM gateways (LiteLLM proxy, OpenRouter), and analytics tools (PostHog, Mixpanel). Native SDKs exist for Python and JavaScript/TypeScript; OpenTelemetry instrumentation supports Go, Java, .NET, Ruby, PHP, and Swift. Data can be exported via REST API, Query SDK, or S3/Blob storage. MCP server and CLI interfaces allow coding agents (Claude Code, Cursor, Codex) to interact with Langfuse programmatically.
Pricing summary: 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.
Review summary: 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.
Competitive positioning: 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.
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.


### Source urls

- https://langfuse.com/
- https://langfuse.com/docs
- https://langfuse.com/pricing
- https://langfuse.com/blog/joining-clickhouse
- https://langfuse.com/users/khan-academy
- https://langfuse.com/users/sumup
- https://github.com/langfuse/langfuse
- https://langfuse.com/blog/announcing-our-seed-round
- https://tracxn.com/d/companies/langfuse/__ShKqRFsp1-W_wOiiPE5R9ZjXs68jfy9uv88xZcE9pP0
- https://posthog.com/spotlight/startup-langfuse
- https://www.producthunt.com/products/langfuse/reviews

Research approved: 2026-05-06T17:53:55.702+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Khan Academy | 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 | 100+ internal users across 11 teams |
| SumUp | 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 | 50% deflection rate; 30% BPO cost reduction; 300,000 monthly requests automated |



### Reviews breakdown





### Review themes



#### 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




### Company facts

Founded year: 2022
Hq: Berlin, Germany


#### Founders

- Max Deichmann
- Clemens Rawert
- Marc Klingen

Employees range: 11-50
Total funding: $4.5M
Valuation: Not available
Arr: Not available
Customer count: 2,300+
Status: Acquired by ClickHouse (January 2026)


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Arize AI | 39 | 125 | 31.2 | 6.011904761904762 |
| Langfuse | 35 | 125 | 28.000000000000004 | 4.298701298701299 |
| Braintrust | 30 | 125 | 24 | 4.615384615384615 |
| LangChain | 20 | 125 | 16 | 3.909090909090909 |
| LiteLLM | 19 | 125 | 15.2 | 4.75 |
| Portkey | 6 | 125 | 4.8 | 3.727272727272727 |
| Confident AI | 5 | 125 | 4 | 4.166666666666667 |
| Galileo | 5 | 125 | 4 | 6.5 |
| Helicone | 3 | 125 | 2.4 | 8.2 |
| Traceloop | 2 | 125 | 1.6 | 1 |
| Patronus AI | 1 | 125 | 0.8 | 5 |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| google-ai | 2 | 8 |
| google-ai-mode | 8 | 32 |
| chatgpt-search | 13 | 52 |
| perplexity | 12 | 48 |
| bing-copilot-search | 0 | 0 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | 1 | 1 |
| Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously? | 2 | 1 |
| What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback? | 1 | 1 |
| Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day? | 1 | 1 |
| 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. | 3 | 1 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams? | 4 |
| Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | 4 |
| I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one? | 3 |
| Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team? | 2 |
| What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support? | 2 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 2 |
| Developer Experience | 5 | 4 |
| Integrations & Ecosystem | 5 | 4 |
| Performance & Reliability | 5 | 3 |
| Setup & First Run | 5 | 3 |



## Prompt results

- Prompt text: Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements?


#### Brand position by platform

Google-ai: 6
Google-ai-mode: 2
Chatgpt-search: 1
Perplexity: 2
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Langfuse | 6 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 4 |
| Braintrust | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |
| Arize AI | 7 |



##### Bing-copilot-search




- Prompt text: Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 1
Perplexity: 2
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 5 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Traceloop | 1 |
| Arize AI | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LiteLLM | 3 |
| Arize AI | 6 |
| Braintrust | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| Arize AI | 4 |



##### Bing-copilot-search




- Prompt text: What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 1
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Galileo | 1 |
| Braintrust | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 4 |


- Prompt text: Which LLM eval platforms support async evaluation at scale without blocking the inference path or adding latency for end users?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 1
Perplexity: 3
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 4 |
| Arize AI | 8 |
| Galileo | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 2 |
| Langfuse | 3 |
| Arize AI | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 4 |
| Galileo | 6 |


- Prompt text: Which LLM observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 1
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 3 |
| Arize AI | 15 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 2 |



##### Bing-copilot-search




- Prompt text: I'm evaluating LLM gateway solutions for a startup — which ones have the simplest self-hosted setup with a working UI on day one?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 5 |



##### Bing-copilot-search




- Prompt text: Which evaluation platforms for LLM outputs are easiest to plug into an existing CI pipeline for a five-engineer team?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 6 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Arize AI | 2 |


- Prompt text: What are the most production-hardened LLM gateway options for an enterprise team needing 99.9% uptime with circuit-breaker support?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Google-ai-mode





##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Bing-copilot-search




- Prompt text: Which LLM tracing platforms make it easiest to replay a failed multi-step agent run and pinpoint exactly where reasoning went wrong?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 1
Chatgpt-search: 5
Perplexity: 4
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 2 |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 3 |
| Langfuse | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Arize AI | 2 |
| Langfuse | 4 |



##### Bing-copilot-search




- Prompt text: What LLM observability tools do ML engineering teams typically use to annotate and review production traces for quality feedback?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Confident AI | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |
| Braintrust | 5 |



##### Bing-copilot-search




- Prompt text: Which LLM gateways add the least latency overhead when routing between LLM providers — safe to use in production for sub-500ms SLAs?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Bing-copilot-search




- Prompt text: What LLM gateway tools integrate best with secret managers and internal auth systems for enterprise teams rolling out to multiple product teams?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 4 |



##### Bing-copilot-search




- Prompt text: Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 3
Perplexity: 6
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Arize AI | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| LangChain | 3 |
| Langfuse | 6 |



##### Bing-copilot-search




- Prompt text: Which LLM evaluation platforms support custom rubric-based scoring for domain-specific correctness beyond generic faithfulness metrics?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Galileo | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Galileo | 3 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 4 |



##### Bing-copilot-search




- Prompt text: Looking for an eval platform that supports automated safety and toxicity scoring on LLM outputs at scale — what are my options?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 3 |
| Patronus AI | 4 |



##### Bing-copilot-search




- Prompt text: Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 3
Chatgpt-search: 3
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| LangChain | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 2 |
| Langfuse | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 3 |
| Arize AI | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| LangChain | 3 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Arize AI | 2 |


- Prompt text: I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 2
Perplexity: 3
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 2 |
| LangChain | 8 |
| Arize AI | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Braintrust | 1 |
| Langfuse | 3 |
| LangChain | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 1 |
| Arize AI | 5 |


- Prompt text: What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 3
Chatgpt-search: 6
Perplexity: 3
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Langfuse | 3 |
| Arize AI | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |
| Arize AI | 4 |
| Langfuse | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |
| Langfuse | 3 |



##### Bing-copilot-search




- Prompt text: What platforms support end-to-end tracing of multi-agent pipelines including tool calls, retrieval steps, and sub-agent spawning?


#### Brand position by platform

Google-ai: 4
Google-ai-mode: 1
Chatgpt-search: 1
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Arize AI | 2 |
| Langfuse | 4 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |
| Braintrust | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Braintrust | 2 |
| Arize AI | 3 |


- Prompt text: Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Portkey | 4 |
| Helicone | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Portkey | 1 |
| LiteLLM | 5 |



##### Bing-copilot-search




- Prompt text: What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: 2
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Traceloop | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 2 |
| Arize AI | 5 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Arize AI | 1 |



##### Bing-copilot-search




- Prompt text: Which LLM observability platforms can a small team get running against a production RAG pipeline in under a day?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Braintrust | 1 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Arize AI | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| LangChain | 2 |
| Arize AI | 3 |



##### Bing-copilot-search




- Prompt text: Which LLM tracing platforms export trace data to a data warehouse so analysts can run custom eval queries alongside product metrics?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 4
Chatgpt-search: 1
Perplexity: 2
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Braintrust | 2 |
| Langfuse | 4 |
| Helicone | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Arize AI | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| LangChain | 1 |
| Langfuse | 2 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Confident AI | 4 |


- Prompt text: Which LLM gateway tools give developers the best real-time cost and token usage visibility across multiple LLM providers during development?


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| LiteLLM | 2 |
| Portkey | 4 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| LiteLLM | 1 |
| Helicone | 6 |



##### Bing-copilot-search




- Prompt text: 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.


#### Brand position by platform

Google-ai: Not available
Google-ai-mode: 1
Chatgpt-search: 1
Perplexity: 1
Bing-copilot-search: Not available



#### Platform rows



##### Google-ai

| Display name | Position |
| --- | --- |
| Confident AI | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 3 |
| LangChain | 5 |
| Arize AI | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Langfuse | 1 |
| Braintrust | 4 |



##### Bing-copilot-search







## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://langfuse.com/integrations/native/opentelemetry | OpenTelemetry (OTEL) for LLM Observability | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 22 | 22 |
| https://langfuse.com/ | Langfuse: Open Source Agent Evals & Observability | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 11 | 11 |
| https://langfuse.com/docs | Open Source AI Engineering Platform - Langfuse | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 10 | 10 |
| https://langfuse.com/self-hosting/security/data-masking | Data Masking (self-hosted) | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 9 | 9 |
| https://langfuse.com/self-hosting | Self-host Langfuse (Open Source LLM Observability) - Langfuse | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 9 | 9 |
| https://langfuse.com/integrations | LLM Observability Integrations - Langfuse | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 9 | 9 |
| https://langfuse.com/docs/observability/features/masking | Masking | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 8 | 8 |
| https://langfuse.com/docs/observability/get-started | Get Started with LLM Tracing - Langfuse | langfuse.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4f043c0041d8d2e21158da045d66d3bd203d0136.png | commercial | documentation | 8 | 8 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | perplexity | \| \| Langfuse \| Its GitHub Action runs experiments in CI, posts scores as a PR comment, and can fail the pipeline when thresholds are missed. |
| 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. | perplexity | Langfuse looks like a strong fit, especially if you want PMs and engineers to review traces together in a visual workflow rather than rely on SQL. |
| What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | perplexity | Langfuse — Strong choice for a hosted or self-hosted LLM observability product. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| google-ai | LiteLLM | ...\| Gateway Tool \| Best Suited For \| Secret Manager Integration \| Internal Auth & IAM Integration \| \| --- \| --- \| --- \| --- \| \| LiteLLM Proxy (Self-Hosted / Enterprise) \| Highly customizable, open-source proxy with robust virtual key mapping per team. |
| google-ai-mode | LiteLLM | LiteLLM Enterprise/Proxy and Kong AI Gateway are the top LLM gateway tools for multi-team enterprise rollouts that require deep integration with corporate secret managers and internal authentication (SSO / OIDC / RBAC) systems. |
| chatgpt-search | LiteLLM | LiteLLM — a strong starting point for a shared, self-hosted gateway with virtual keys, team budgets, model access controls, and integrations with enterprise infrastructure. |
| perplexity | LiteLLM | For an enterprise rollout across multiple product teams, LiteLLM and Portkey are the clearest fits in the results. |
| google-ai | LiteLLM | LiteLLM Proxy * How it handles fallback & retries: LiteLLM supports extensive fallbacks, load balancing, and automatic retries on failure (e. g., 5xx errors, rate limits) across dozens of LLM providers using a unified OpenAI-compatible API format. |
| google-ai-mode | LiteLLM | litellm.ai/docs/observability/opentelemetry_integration)Tracing - Portkey (Prisma AIRS AI Gateway) Docs The primary tools handling this capability include: * * [Portkey AI Gateway](https:... |
| google-ai-mode | Portkey | OpenTelemetry v1Tracing - Portkey (Prisma AIRS AI Gateway) Docs The primary tools handling this capability include:... |
| chatgpt-search | LiteLLM | The strongest candidates are LiteLLM, Portkey, and LLM Gateway, with Cloudflare AI Gateway also worth evaluating. |
| perplexity | Portkey | Portkey is the clearest match for all three requirements: it supports automatic retries and provider fallbacks, and logs the attempts in a shared trace so you can inspect the failed and successful calls across the switch. |
| google-ai-mode | LiteLLM | LiteLLM Proxy (The Industry Standard for Speed) LiteLLM is hands-down the fastest to spin up with an out-of-the-box UI, robust database backing, and broad model support (OpenAI, Anthropic, Vertex, Ollama, etc.). [Quickstart \| liteLLM](https://docs.... |
| chatgpt-search | LiteLLM | If your priority is self-hosted, minimal setup, and a usable web UI on day one, I’d start with LLM Gateway and LiteLLM, then evaluate Bifrost if throughput and operational simplicity matter more than having the most mature admin experience. |
| perplexity | LiteLLM | LiteLLM looks like the strongest fit if “self-hosted plus a useful UI on day one” is your main criterion. Its Docker quickstart brings up the gateway, a Postgres database, and the Admin UI; you can add models, create keys, and test requests in a brow... |



## Trend

Visibility delta: -2.399999999999995
Avg position delta: -1.079347481786506
Citation count delta: -5