# Arize AI AI visibility in LLM Observability Evals & Gateways

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

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

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


## Platforms

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

Rank: 1
Total brands: 11
Measured responses: 125
Presence percent: 31.2
Share of voice percent: 25.846153846153847
Average position: 6.011904761904762
Docs presence percent: 0.8
Blog presence percent: 2.4
Brand mention percent: 4.8


## Profile

Overview: Arize AI is a Berkeley, California-based AI observability and evaluation company founded in 2020 by Jason Lopatecki (CEO) and Aparna Dhinakaran (CPO). Its flagship product, Arize AX, is an enterprise AI and agent engineering platform that unifies tracing, evaluation, prompt management, and production monitoring for LLM applications, AI agents, and traditional ML models. The company also maintains Arize Phoenix, a widely adopted open-source observability and evaluation library built on OpenTelemetry, with over 5 million monthly downloads. Arize's OpenInference instrumentation standard supports auto-instrumentation across major AI frameworks and LLM providers without vendor lock-in. Enterprise customers include PepsiCo, Booking.com, TripAdvisor, Siemens, Uber, Wayfair, and hundreds more. The company has raised $131M in total funding, including a $70M Series C in February 2025.
Product summary: Arize AX is an enterprise AI and agent engineering platform providing end-to-end LLM tracing, online/offline evaluation, prompt management, and production monitoring — complemented by Arize Phoenix, an open-source and self-hostable observability and evaluation toolkit built on OpenTelemetry/OpenInference standards.


### Key capabilities

- OpenTelemetry-native LLM and agent tracing with tree-structured span visualization
- Online and offline LLM-as-a-Judge evaluations at scale
- Prompt management, serving, optimization, and CI/CD experiment tracking
- Real-time production monitoring, alerting, and custom dashboards
- Human annotation queues and golden dataset curation
- Traditional ML model drift, data quality, and embedding monitoring
- Alyx AI assistant (copilot for AI engineers — trace debugging, prompt optimization, dashboard creation)
- adb purpose-built datastore for petabyte-scale observability workloads
- Multi-agent and multi-modal system observability
- Open-source Arize Phoenix (self-hostable AI observability and evaluation)



### Target users

- ML engineers and AI engineers building and operating LLM applications
- MLOps and LLMOps teams at mid-size to large enterprises
- AI-first startups instrumenting generative AI products
- Data scientists monitoring traditional ML and computer vision models
- Platform and infrastructure teams managing multi-agent AI systems
- Government and defense agencies requiring trusted AI deployment



### Key use cases

- Production monitoring of LLM applications and AI agents
- Pre-deployment evaluation and regression testing via CI/CD
- RAG pipeline debugging and retrieval quality measurement
- Prompt engineering iteration and version management
- Traditional ML model performance and drift monitoring
- Multi-agent system tracing and behavior analysis
- Enterprise AI governance, safety, and compliance monitoring
- Voice assistant and audio AI evaluation

Integrations ecosystem: Built on OpenTelemetry and the company's own OpenInference instrumentation standard. Supports auto-instrumentation for major AI frameworks including LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, DSPy, Haystack, Mastra, and Vercel AI SDK. LLM provider integrations cover OpenAI, Anthropic, Google GenAI/ADK, AWS Bedrock, Mistral, Groq, VertexAI, LiteLLM, and OpenRouter. Deep strategic integrations with Microsoft Azure AI Studio and Azure AI Foundry (backed by M12 investment). Available on AWS Marketplace with native Amazon Bedrock Agents support. SDKs available in Python, JavaScript/TypeScript, Java, Go, and R. Eval integrations with Ragas, DeepEval, and Cleanlab. Listed integrations also include Flowise, LlamaCloud, Hugging Face, and UbiOps.
Pricing summary: Four tiers. Phoenix: free and fully open-source, self-hostable with user-managed resources. AX Free: free SaaS tier with 25k spans/month, 1GB ingestion, 15-day retention, and access to Alyx and online evals. AX Pro: $50/month with 50k spans/month, 10GB ingestion, 30-day retention, and email support; additional spans at $10/million, additional GB at $3/GB. AX Enterprise: custom pricing (SaaS or self-hosted) with configurable retention, dedicated support, uptime SLA, SOC2/HIPAA, SSO enforcement, RBAC, adb Data Fabric, and multi-region deployment options. Startup pricing program available. Third-party sources estimate enterprise contracts start at approximately $50,000/year.
Review summary: Reviewers on G2 and AWS Marketplace (28 G2 reviews as of 2026) consistently praise Arize AI for its powerful trace visualization, strong LLM-as-a-Judge evaluation capabilities, and highly responsive customer support team (G2 quality of support scored 9.8/10). Users highlight ease of initial setup, the experiment and annotation features, and the value of offline pre-production evaluations. Criticisms center on a steep learning curve for new users, documentation that can be overwhelming for beginners, and an engineering-centric interface that is less accessible to non-technical stakeholders. Some users request more advanced prompt management features such as BU-level categorization and richer integration with external data sources.
Competitive positioning: Arize AI positions itself as the category-defining, enterprise-grade AI observability and evaluation platform — covering the full lifecycle from pre-deployment experimentation to production monitoring. Its dual product strategy (commercial Arize AX + open-source Arize Phoenix) mirrors a developer-adoption flywheel: Phoenix drives grassroots adoption among individual engineers while AX captures enterprise contracts. Unlike framework-specific competitors (e.g., LangSmith's LangChain dependency), Arize is vendor- and framework-agnostic via OpenTelemetry/OpenInference standards. It is also broader than pure-LLM observability tools, covering traditional ML and computer vision alongside generative AI. The company claims first-mover status (founded 2020), having processed 1+ trillion spans and achieved 5M+ monthly Phoenix downloads. Strategic investment from Microsoft (M12) and Datadog signals intent to integrate across major cloud and observability stacks.
Limitations: Engineering-centric platform with a steep learning curve reported by multiple reviewers; non-technical users (product managers, CX teams) often require engineering support to extract actionable insights. Documentation described as extensive but overwhelming for beginners. Enterprise AX pricing is significant — estimated at ~$50,000/year minimum per third-party analysis, making it difficult to justify for smaller teams. Prompt management lacks advanced organizational features (e.g., BU-level categorization). Platform is monitoring-focused and does not include agent-building capabilities, creating a separation between observability and development workflows. Early integration work may require custom configuration for less common AI stacks.


### Source urls

- https://arize.com/
- https://arize.com/pricing/
- https://arize.com/blog/arize-ai-raises-70m-series-c-to-build-the-gold-standard-for-ai-evaluation-observability/
- https://www.prnewswire.com/news-releases/arize-ai-secures-70m-series-c-to-fix-ais-biggest-problem-making-llms-and-ai-agents-work-in-the-real-world-302381601.html
- https://techcrunch.com/2025/02/20/arize-ai-hopes-it-has-first-mover-advantage-in-ai-observability/
- https://github.com/arize-ai
- https://github.com/Arize-ai/phoenix
- https://github.com/Arize-ai/openinference
- https://www.adamsstreetpartners.com/insights/why-we-invested-in-arize-ai/
- https://arize.com/customers
- https://arize.com/gartner-enterprise-ai-observability/
- https://aws.amazon.com/marketplace/pp/prodview-kjmocii4mcw4s
- https://www.g2.com/products/arize-ai/reviews
- https://phoenix.arize.com/

Research approved: 2026-05-06T17:53:55.334+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Handshake | Deployed and scaled 15+ LLM use cases in under six months using Arize for tracing, monitoring, and evaluation from day one across production AI systems. | 15+ LLM use cases in <6 months |
| Clearcover | Deployed a new insurance scoring model from inception to production in 46 days, with Arize providing confidence in model performance for high-volume inference workloads. | 46 days inception-to-production |
| Booking.com | Automated full multi-agent interaction logging across their AI Trip Planner, using Arize to monitor agent configuration, model selection, and tool usage correctness in production. | Not available |
| Radiant Security | Adopted Arize as a core part of their AI agent development workflow; CTO stated it saved 'countless hours' and enabled shadow-mode testing to identify improvement areas with precision. | Not available |



### Reviews breakdown





### Review themes



#### Praised

- Powerful trace and span visualization
- Strong LLM-as-a-Judge evaluation capabilities
- Highly responsive customer support team (G2 support score 9.8/10)
- Easy initial setup and onboarding
- Useful offline and online evaluation workflows
- Effective experiment and annotation features
- Flexible filtering of traces and sessions
- Open-source Phoenix as a free self-hosted alternative



#### Criticized

- Steep learning curve for new users
- Documentation extensive but overwhelming for beginners
- Engineering-centric UI less accessible to non-technical stakeholders
- Prompt management lacks advanced organizational features
- Enterprise pricing is significant for smaller teams
- Limited flexibility for LLM judge model selection
- Playground dataset row selection inconsistency
- Early integration required custom configuration workarounds




### Company facts

Founded year: 2020
Hq: Berkeley, California, USA


#### Founders

- Jason Lopatecki
- Aparna Dhinakaran

Employees range: 101-250
Total funding: $131M
Valuation: Not available
Arr: Not available
Customer count: Not available
Status: Private


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 | 6 | 24 |
| chatgpt-search | 14 | 56.00000000000001 |
| perplexity | 12 | 48 |
| bing-copilot-search | 5 | 20 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | 4 | 2 |



## 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 |
| I'm evaluating LLM eval platforms — which ones integrate with version control to tie prompt regressions back to specific code or config changes? | 4 |
| Which LLM gateways handle multi-provider fallback and automatic retries while preserving full trace context across the switch? | 4 |
| 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. | 4 |
| Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | 3 |



## Topic scores

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



## 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: Not available
Google-ai-mode: Not available
Chatgpt-search: 4
Perplexity: 7
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: 3
Chatgpt-search: 6
Perplexity: 4
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: 2
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: 8
Perplexity: 4
Bing-copilot-search: 4



#### 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: 15
Perplexity: 2
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: 2



#### 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: Not available
Chatgpt-search: 3
Perplexity: 2
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: 3
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: 4
Google-ai-mode: 1
Chatgpt-search: Not available
Perplexity: Not available
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: 7
Perplexity: 3
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: 1
Chatgpt-search: 4
Perplexity: 1
Bing-copilot-search: 2



#### 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: 10
Perplexity: Not available
Bing-copilot-search: 5



#### 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: 5
Chatgpt-search: 4
Perplexity: 1
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: 2
Google-ai-mode: 3
Chatgpt-search: 3
Perplexity: 1
Bing-copilot-search: 3



#### 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: 5
Perplexity: 1
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: 3
Perplexity: 3
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: Not available
Chatgpt-search: 9
Perplexity: Not available
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: Not available
Chatgpt-search: 6
Perplexity: Not available
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://arize.com/resources/llm-and-agent-evaluation-platforms/ | Compare 7 LLM Evaluation Platforms (2026): Agents, Tracing & CI/CD | arize.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4a5de90f465a400dcec1e149ccba286cff6cb798.png | commercial | comparison | 45 | 45 |
| https://arize.com/docs/phoenix/ | What is Arize Phoenix? - Phoenix | arize.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4a5de90f465a400dcec1e149ccba286cff6cb798.png | commercial | documentation | 22 | 22 |
| https://arize.com/docs/phoenix | What is Arize Phoenix? - Phoenix | arize.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4a5de90f465a400dcec1e149ccba286cff6cb798.png | commercial | documentation | 19 | 19 |
| https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/ | Best AI Observability Tools for Autonomous Agents in 2026 | arize.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4a5de90f465a400dcec1e149ccba286cff6cb798.png | commercial | blog_post | 13 | 13 |
| https://github.com/Arize-ai/phoenix/ | GitHub - Arize-ai/phoenix: AI Observability & Evaluation | github.com | Not available | commercial | documentation | 11 | 11 |
| https://arize.com/docs/phoenix/learn/tracing/how-tracing-works | How Tracing Works - Phoenix | arize.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4a5de90f465a400dcec1e149ccba286cff6cb798.png | commercial | documentation | 11 | 11 |
| https://github.com/arize-ai/phoenix | GitHub - Arize-ai/phoenix: AI Observability & Evaluation | github.com | Not available | commercial | documentation | 8 | 8 |
| https://arize.com/docs/phoenix/tracing/llm-traces | Overview: Tracing - Phoenix - Arize AI | arize.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/b47f69ae-9962-4274-829c-bf8a1c5e86b4/4a5de90f465a400dcec1e149ccba286cff6cb798.png | commercial | documentation | 6 | 6 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| What are the best OpenTelemetry-compatible tracing backends for LLM apps that work out of the box without custom span parsing? | google-ai-mode | Translating Semantic Conventions - Phoenix - Arize AI * To help narrow down the best choice for your stack, could you share: * What languages and frameworks (e.g., Python, LangChain, LlamaIndex, custom HTTP) your LLM app uses? |
| What LLM tracing platforms handle high-throughput production workloads — millions of traces per day — without degrading query performance? | google-ai-mode | ...loyments](https://langfuse.com/self-hosting/configuration/scaling)Compare Arize: AI Observability & Evaluation Platform \| Arize AI * * * Top LLM Tracing Platforms for High-Throughput Production 1. Langfuse (Self-Hoste... |
| Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | google-ai-mode | [Arize AI] * Dataset & Scoring Loop: It binds template variables (like `{customer_input}`) directly to dataset columns, scores outputs via the Eval Hub, logs every run as an experiment, and lets you promote winning prompt variants back to the Pro... |



## 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 | Braintrust | Braintrust * How it works with Version Control: Braintrust offers a bidirectional sync workflow. |
| chatgpt-search | Braintrust | ## 1. Braintrust Best for commit-level traceability Braintrust records evaluation experiments and can associate them with Git branches and commit SHAs. |
| chatgpt-search | Langfuse | Documentation: GitHub Actions eval pipeline ## 2. Langfuse Best for prompt-to-Git synchronization Langfuse versions prompts immutably, supports prompt diffs and environment labels, and can sync prompt versions to GitHub. |
| perplexity | Braintrust | ...Git-based changes, but they do it at different levels: \| Platform \| How it ties regressions to changes \| Best fit \| \|---\|---\|---\| \| Braintrust \| Run evals on every pull request with GitHub Actions, gate merges on results, and post scores in the PR. |
| perplexity | Langfuse | \| \| Langfuse \| Its GitHub Action runs experiments in CI, posts scores as a PR comment, and can fail the pipeline when thresholds are missed. |
| bing-copilot-search | Confident AI | The strongest options are Confident AI, LangSmith, Langfuse, Promptfoo, and Arize Phoenix, each offering CI/CD hooks and git-style workflows. ## 🔑 Key Platforms with Version Control Integration Platform\| Version Control Integration\| CI/CD Regressio... |
| 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:... |



## Trend

Visibility delta: -3.1999999999999993
Avg position delta: 0.0586337338673788
Citation count delta: -23