Arize AI logo

Arize AI

Challenger

Leading AI observability and evaluation platform. Founded 2020, Berkeley CA. $131M total funding. Open-source Phoenix has 2M+ monthly downloads. Private.

Best for: AI/ML Observability & Evaluation
59
AI Score
Grade C
AI Visibility Score (Beta)
Developer ToolsAI/ML Observability & EvaluationWebsiteUpdated October 2026

Brand Intelligence Graph

Integrates with
Capabilities
AI/ML Observability & Evaluation

Company Overview

About Arize AI

Arize AI is an AI observability and evaluation platform founded in January 2020 by Jason Lopatecki (CEO) and Aparna Dhinakaran (CPO), headquartered in Berkeley, California. It provides tools for monitoring, troubleshooting, and improving AI models in production, covering both traditional ML and LLMs. Key products include the Arize platform and the open-source Phoenix library.

Business Model & Competitive Advantage

Arize operates a hybrid SaaS plus open-source model. Phoenix has become the most widely adopted AI observability library with 2M+ monthly downloads. Clients include Booking.com, Duolingo, Hyatt, PepsiCo, Uber, and Wayfair. Arize competes with Datadog, Weights & Biases, and LangSmith.

Competitive Landscape 2025–2026

In February 2025, Arize raised $70 million Series C led by Adams Street Partners, with participation from M12 (Microsoft Ventures), Datadog, and PagerDuty, bringing total funding to $131 million.

Founded
2020
Headquarters
Berkeley, California
Curated content • Fact-checked and verified

Recent Activity

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blog_post
Prompt caching benchmark: high cache reuse doesn’t always mean lower cost

We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the same multi-turn shopping assistant agent. The post Prompt caching benchmark: high cache reuse doesn’t always mean lower cost appeared first on Arize AI .

blog_post
Introducing Arize AX MCP: When to use MCP, CLI, or skills

MCP, CLI, and skills are three ways to extend an agent with the same platform. The question is no longer which one wins. It is where the agent runs, and who is asking. The post Introducing Arize AX MCP: When to use MCP, CLI, or skills appeared first on Arize AI .

blog_post
Claude’s hillclimb loop for AI agents: start with production traces

Anthropic's Claude hillclimb loop designs evals and improves agents one change at a time. Here's how to run that loop against production traces in Arize AX. The post Claude’s hillclimb loop for AI agents: start with production traces appeared first on Arize AI .

blog_post
How we built long-term memory for Alyx: why we chose a file over a knowledge graph

How we built long-term memory for Alyx: why we chose one 8,000-character file over retrieval and knowledge graphs, and how we tested it. The post How we built long-term memory for Alyx: why we chose a file over a knowledge graph appeared first on Arize AI .

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Alyx now remembers your work across sessions with long-term memory

Alyx now remembers project goals, conventions, and decisions across sessions in Arize AX, helping you continue work on traces, evals, and experiments. The post Alyx now remembers your work across sessions with long-term memory appeared first on Arize AI .

blog_post
NVIDIA proposes an AI agent kill switch in silicon after a year of sandbox escapes

This year agents at OpenAI, Anthropic, and Google escaped environments meant to contain them. What decided severity was time-to-detect and time-to-kill, from 12 minutes to seven months. NVIDIA's Open Agent Safety Platform puts a kill switch in hardware the agent can't reach. The post NVIDIA proposes an AI agent kill switch in silicon after a year of sandbox escapes appeared first on Arize AI .

blog_post
Building production-ready AI agents with Atlas Agent Engine and Arize AX

Agents built and deployed on MongoDB’s Atlas Agent Engine can export OpenTelemetry traces to Arize AX, where teams can inspect each run, evaluate both the outcome and execution path, and test fixes before redeploying. The post Building production-ready AI agents with Atlas Agent Engine and Arize AX appeared first on Arize AI .

blog_post
What everyone was talking about at WeAreDevelopers World Congress North America

After 237 talks across eight stages, code review emerged as the bottleneck for AI-generated code. Here are the production eval, sandbox, context, and agent experience themes that kept showing up. The post What everyone was talking about at WeAreDevelopers World Congress North America appeared first on Arize AI .

blog_post
Are agent harnesses dying? What harness distillation changes

Harness distillation can train general scaffolding into a model. The harness that remains is the part tied to your tools, data, users, and environment. The post Are agent harnesses dying? What harness distillation changes appeared first on Arize AI .

blog_post
Anthropic says it fixed Claude’s writing. I ran the evals to check.

Opus 5.5 dropped em dashes from 12.9 per 1,000 words to two in 57,000 words, and cut the rest of the Claudisms in half. Better, but not fixed. The post Anthropic says it fixed Claude’s writing. I ran the evals to check. appeared first on Arize AI .

8-K
8-K — 8-K

Material Event filed 2026-09-29

blog_post
Evaluate production traces with Jev-as-a-Judge directly in Arize AX

Arize AX now integrates directly with TypeSafe AI, bringing native Jev-as-a-Judge evaluations into the Arize evaluation workflow. The post Evaluate production traces with Jev-as-a-Judge directly in Arize AX appeared first on Arize AI .

Key Differentiators

Strong Challenger

Arize AI is an established challenger with significant market presence and competitive offerings in Developer Tools.

Frequently Asked Questions

Estimated Visibility Trend (Beta)

Simulated 8-week rolling score

59
→ Stable

Based on estimated brand signals. Historical tracking coming soon.

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