AI visibility report
Arize AI ranks #6 in LLM Observability Evals & Gateways AI search.
Outside the top three on 21 of the 25 prompts buyers actually ask.
Braintrust is cited on 14 of those losses.
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Track Arize AI across these prompts daily.
Start free trial#6 among 11 vendors · still absent from 91.2% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Perplexity
Bing Copilot
Google AI Mode
Gemini SearchNarrower footprint, stronger tone. Arize AI ranks #6 on presence but #3 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
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 winning
No clear strengths identified yet.
Where Arize AI 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 promptI'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.
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 observability platforms stay reliable under traffic spikes from batch eval jobs running thousands of LLM calls simultaneously?
Competitors on 4 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
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.
Arize AI (Phoenix) : Provides a comprehensive framework for "LLM-as-a-judge" that supports custom rubric-based evaluations for complex reasoning and domain-specific accuracy.
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.
Arize AI / Phoenix (Arize): Strong open-source (Phoenix) and commercial (Ax) solutions focusing on ML observability, drift detection, and toxicity checks.
What's the fastest LLM tracing platform for instrumenting a Python-based agent framework without rewriting existing code?
Most cited sources8
8Top LLM Tracing Tools - Arize AI
arize.com·Blog Post
- 8
Arize Phoenix | Phoenix
arize.com·Blog Post
- 7
Prompt Playground - Arize AX Docs
arize.com·Documentation
- 6
Data Fabric: Querying agent traces in BigQuery - Arize AI
arize.com·Blog Post
- 4
8 Top Prompt Testing and Optimization Tools for LLMs and Multiagent Systems (2025) - Arize AI
arize.com·Blog Post
- 4
Braintrust Open Source Alternative? LLM Evaluation Platform Comparison | Arize Phoenix
arize.com·Documentation
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 | Bing Copilot | Google AI Mode | Perplexity | ChatGPT |
|---|---|---|---|---|---|
Capability2/5 cited (40%) | |||||
Which LLM observability tools handle PII redaction and data masking in traces for teams with HIPAA or GDPR compliance requirements? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand 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 | A competitor was 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 | Your brand 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 Experience1/5 cited (20%) | |||||
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 | A competitor was cited |
Which LLM eval platforms have the best prompt playground experience for iterating on system prompts against a saved test dataset? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were 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. | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was 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 | A competitor was cited | A competitor was cited | A competitor was cited |
Integrations & Ecosystem3/5 cited (60%) | |||||
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 | Your brand and a competitor were 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 | A competitor was 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? | 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 |
Which LLM observability tools work with OpenTelemetry-compatible backends so we can consolidate LLM traces alongside existing service traces? | 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 |
Which LLM observability platforms integrate natively with the most popular agent frameworks so traces appear automatically with no manual instrumentation? | A competitor was 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 | Your brand and a competitor were cited | Your brand 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? | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were 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 | A competitor was cited |
Setup & First Run0/5 cited (0%) | |||||
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 | A competitor 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 | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 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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