AI visibility report
AI visibility report for Arize AI in LLM Observability Evals & Gateways.
Outside the top three on 15 of the 25 prompts buyers actually ask.
Braintrust is cited on 7 of those losses.
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Track Arize AI across these prompts daily.
Start free trialStill absent from 88% of tracked prompt responses
Top-3 citations across 75 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Gemini Search
PerplexityHow to read this. Arize AI appears in 12% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Arize AI is losing
Prompts where competitors are visible and Arize AI is not.
These prompt-level losses are the first prompts to track and repair.
Where Arize AI is winning1
Which observability tools include real-time alerting on quality drops, not just latency?
Avg # 1.0 · 1 platform
Where Arize AI is losing5
Which LLM eval platforms support running automated evaluations on production traces with custom metrics?
Competitors on 3 platforms
Track this promptWhat are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines?
Competitors on 3 platforms
Track this promptWhat AI eval platforms support on-premise or VPC deployment for regulated industries?
Competitors on 2 platforms
Track this promptWhich evaluation platforms let me convert development-time evals into production guardrails automatically?
Competitors on 2 platforms
Track this promptWhat's the fastest way to start tracing my LLM application calls without rewriting my code?
Competitors on 2 platforms
Track this prompt
Track Arize AI daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
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.
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 Facts
- Founded
- 2020
- HQ
- Berkeley, California, USA
- Founders
- Jason Lopatecki, Aparna Dhinakaran
- Employees
- 101-250
- Funding
- $131M
- Status
- Private
Target users
Key Capabilities10
- 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)
Key Use Cases8
- 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
Arize AI customer outcomes
15+ LLM use cases in <6 months
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.
46 days inception-to-production
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.
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.
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.
Recent Trend
How AI describes Arize AI3
Arize AI (via Phoenix) — strong trace + eval + feedback loops * Humanloop — very strong on continuous human feedback loops * Weights & Biases (Weave) — experiment tracking + eval logging * * * How they differ in practice (importan...
Which LLM platforms have the best workflows for human annotation and labeling of model outputs?
* 🟢 Platforms proven or designed for billion-scale trace volumes --------------------------------------------------------------- ### Arize AI (Phoenix + AX) One of the strongest known players at extreme scale.
Which LLM observability platforms scale to billions of traces per month at enterprise volumes?
...are the strongest options: | Tool | Real-time quality alerts | What it alerts on besides latency | | --- | --- | --- | | Arize AI | Yes | Model performance degradation, drift, embedding drift, LLM evaluation metrics, data quality issues, cohort-specifi...
Which observability tools include real-time alerting on quality drops, not just latency?
Most cited sources8
- 7
Braintrust Open Source Alternative? LLM Evaluation Platform Comparison | Arize Phoenix
arize.com·Landing Page
- G4
GitHub - Arize-ai/phoenix: AI Observability & Evaluation · GitHub
github.com·Documentation
- 4
LLM Tracing: From Automatically Collecting Traces To ... - Arize AI
arize.com·Blog Post
- 3
Docker - Phoenix
arize.com·Blog Post
- 3
Self-Hosting | Arize Phoenix
arize.com·Blog Post
- 3
LLM Observability & Evaluation Platform
arize.com·Blog Post
Alternatives in LLM Observability Evals & Gateways6
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.
Reviews
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
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.
Pricing
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.
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.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Evaluation2/5 cited (40%) | |||
Which LLM platforms have the best workflows for human annotation and labeling of model outputs? | |||
What tools provide model-graded evaluation with calibrated reference-free scoring for chatbots? | |||
Which LLM eval platforms support running automated evaluations on production traces with custom metrics? | |||
What are the best tools for detecting hallucinations and faithfulness issues in RAG pipelines? | |||
Which evaluation platforms let me convert development-time evals into production guardrails automatically? | |||
Gateways & Routing0/5 cited (0%) | |||
What gateways have the lowest latency overhead when routing high-volume LLM traffic? | |||
Which LLM gateways are open-source and self-hostable for teams that don't want a SaaS dependency? | |||
Which AI gateways let me route between OpenAI, Anthropic, and open-source models with a single API call? | |||
What LLM gateway platforms support automatic fallbacks, retries, and load balancing across providers? | |||
Which AI proxies handle rate limiting, key rotation, and cost tracking across teams centrally? | |||
Production Readiness2/5 cited (40%) | |||
What AI eval platforms support on-premise or VPC deployment for regulated industries? | |||
What LLM monitoring platforms integrate with PagerDuty, Slack, or Datadog for alerting workflows? | |||
Which observability tools include real-time alerting on quality drops, not just latency? | |||
Which AI guardrail platforms provide pre-execution intervention to block unsafe agent actions before they run? | |||
Which LLM observability platforms scale to billions of traces per month at enterprise volumes? | |||
Setup & First Run3/5 cited (60%) | |||
Which AI observability platforms can be self-hosted with one command using Docker Compose? | |||
Which LLM observability tools work with OpenTelemetry so I don't have to add yet another vendor SDK? | |||
I want to add eval tracking to my agent — which platforms have the simplest Python decorator-style integration? | |||
What's the easiest way to log every LLM call my app makes for debugging without changing my application architecture? | |||
What's the fastest way to start tracing my LLM application calls without rewriting my code? | |||
Tracing & Debugging0/5 cited (0%) | |||
Which LLM observability tools show token usage, latency, and cost per step in an agent pipeline? | |||
What platforms support replaying production traces in development for reproducible debugging? | |||
Which observability platforms offer the best agent execution tracing for multi-step LLM workflows? | |||
What tools let me drill into a single user session to debug exactly what my agent did at each step? | |||
Which AI observability tools surface unknown failure patterns I wouldn't have written tests for? | |||
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 26.7% | 26.4% | 2.7% | 0.0% | 26.7% | #8.5 | +0.39 |
| 2 | Confident AI | 13.3% | 8.0% | 0.0% | 4.0% | 13.3% | #5.0 | +0.37 |
| 3 | LangChain | 13.3% | 6.9% | 5.3% | 0.0% | 13.3% | #9.3 | +0.44 |
| 4 | Langfuse | 13.3% | 18.4% | 6.7% | 2.7% | 13.3% | #12.1 | +0.51 |
| 5 | Galileo | 12.0% | 10.9% | 0.0% | 12.0% | 12.0% | #5.5 | +0.52 |
| 6 | Arize AI | 12.0% | 13.8% | 0.0% | 0.0% | 12.0% | #12.9 | +0.45 |
| 7 | BerriAI (LiteLLM) | 5.3% | 2.3% | 4.0% | 0.0% | 2.7% | #9.0 | +0.40 |
| 8 | Helicone | 5.3% | 10.3% | 1.3% | 5.3% | 5.3% | #18.2 | +0.32 |
| 9 | Traceloop | 4.0% | 1.7% | 0.0% | 4.0% | 4.0% | #3.7 | +0.20 |
| 10 | Portkey | 2.7% | 1.1% | 0.0% | 0.0% | 2.7% | #11.0 | +0.42 |
| 11 | Patronus AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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