As AI engineers, we believe in total control and transparency.
Just the tools you need to do your job, interoperable with the rest of your stack.
Unified Observability and Evaluation Platform for AI
Arize AX is the single platform built to help you accelerate development of AI apps and agents – then perfect them in production.
Deployed by thousands of AI teams.
Explore Arize AI Observability for:
One platform.
Close the loop between AI development and production.
Integrate development and production to enable a data-driven iteration cycle—real production data powers better development, and production observability aligns with trusted evaluations.
One platform.
Close the loop between AI development and production.
Integrate development and production to create a data-driven iteration loop—real production data fuels better model improvements, while continuous monitoring and observability ensures alignment with trusted ML and CV performance.
GenAI Tracing
Instant visibility, without the overhead.
Get instant, end-to-end AI visibility with seamless OTEL instrumentation—no complex setup. Automate observability across top AI frameworks and trace prompts, variables, tool calls, and agents to debug faster.
Auto-Instrumentation
Prompt Playground
Dataset Curation
Experiment Tracking
Evaluation
Continuous evaluation, from dev to prod.
Automate AI evaluation at every stage. Run offline and online checks as you push code, with LLM-as-a-Judge insights and code-based tests catching failures early. Scale evaluations in production to ensure reliability and performance.
Offline evals
Online evals
Intelligent dataset curation
Experiment runs
Eval hub
Production Monitoring
Smarter monitoring for smarter AI.
Get real-time AI monitoring with automated anomaly detection, failure simulation, and root cause analysis. Stay ahead with auto-thresholding, smart alerts, and customizable metrics. Scale monitoring with analytical dashboards and AI-powered insights to keep your models reliable.
Automated monitoring
Online evaluations
Dashboards
Prompt & eval IDE
LLM guardrails
Prompt & Evaluation IDE
Your integrated AI improvement engine.
Turn production into your greatest feedback loop with real-time insights and shared tools that help AI teams gain visibility, iterate together, and - ultimately - deliver better AI outcomes at scale.
Prompt playground
Prompt hub
Evals builder
Dataset curation
Experiments
Annotations & Labeling
Scale up quality annotations.
Combine human expertise with automated workflows to generate high-quality labels and annotations. Quickly identify edge cases, refine datasets, and enhance your AI applications with smarter, more reliable data inputs.
Auto-labeling
Annotations queue
Dataset curation
RCA
Pinpoint model failures and root causes.
Quickly surface failure modes with heatmaps, identify underperforming slices, and understand why models make specific decisions to optimize performance and reduce bias.
Performance Tracing
Explainability
AI Copilot
Production Monitoring
Detect and address model drift early.
Continuously monitor feature and model drift across training, validation, and production environments to catch unexpected shifts before they impact performance.
Model Drift
Feature Drift
Custom Dashboards
Automated Monitors
Exploratory Data Analysis
Find and analyze critical data patterns.
Leverage AI-driven cluster search to uncover anomalies, identify edge cases, and curate datasets for deeper analysis and model improvement.
Cluster Search
Dataset Curation
AI Copilot
EDA Workflows
Embeddings Monitoring
Monitor embeddings to prevent silent failures.
Track embedding drift across NLP, computer vision, and multi-modal models to maintain stable, high-quality feature representations.
Embeddings Analyzer
Embeddings Monitoring
UMAP Visualizer
Performance Optimization
Improve model performance with better data.
Augment your datasets with human feedback, labels, and metadata while systematically curating data for experimentation, A/B testing, and iterative model improvements.
Annotations
Dataset Management
A/B Comparisons
Building & Evaluating AI Agents.
Continue your journey into AI Specialization with advanced learning hubs.
Exploring Agent Architectures
Understand key considerations when achitecting your AI agent to ensure utmost flexibility and control as tooling—and your business—evolves.
Evaluating AI Agents
Best practices and research on evaluating AI agents—from simple single-function agents to complex multi-agent routers.
Agents in the Wild
Learn about agents in production today from AI teams at the forefront of development.
Built on open source & open standards.
No black box eval models.
From evaluation libraries to eval models, it’s all open-source for you to access, assess, and apply as you see fit.
See the evals libraryNo proprietary frameworks.
Built on top of OpenTelemetry, Arize’s LLM observability is agnostic of vendor, framework, and language—granting you flexibility in an evolving generative landscape.
OpenInference conventionsNo data lock-in.
Standard data file formats enable unparalleled interoperability and ease of integration with other tools and systems, so you completely control your data.
Arize Phoenix OSSCreated by AI engineers, for AI engineers.
“Arize observability is pretty awesome!”
Andrei Fajardo
Founding Engineer, LlamaIndex
"We found that the platform offered great exploratory analysis and model debugging capabilities, and during the POC it was able to reliably detect model issues."
Mihail Douhaniaris & Martin Jewell
Senior Data Scientist and Senior MLOps Engineer, GetYourGuide
"From Day 1 you want to integrate some kind of observability. In terms of prompt engineering, we use Arize to look at the traces [from our data pipeline] to see the execution flow … to determine the changes needed there."
Kyle Weston
Lead Data Scientist, GenAI, Geotab
"We love Arize for rapid prototyping of LLM projects including Agentic AI Agents. The seamless integration of AI traces, and instrumentation for building evals for LLMOps are a force multiplier for us."
Keller Williams
“Tripadvisor's billion-plus reviews and contributions are becoming even more important in a world of AI search and recommendations where travel experiences are more conversational, personal and even agentic. As we build out new AI products and capabilities, having the right infrastructure in place to evaluate and observe is important. Arize has been a valuable partner on that front.”
Rahul Todkar
Head of Data and AI, TripAdvisor
“Our big use case in Arize was around observability and being able to show the value that our AIs bring to the business by reporting outcome statistics into Arize so even non-technical folks can see those dashboards — hey, that model has made us this much money this year, or this client isn’t doing as well there — and get those insights without having to ask an engineer to dig deep in the data.”
Lou Kratz, PhD.
Principle Research Engineer, BazaarVoice
"At Handshake, ensuring students see the most relevant and qualified job opportunities is core to our mission. Arize gives us the observability we need to understand how these models behave in the wild — tracing outputs, monitoring quality, and managing cost. That visibility lets us iterate quickly and confidently, ensuring our AI systems are both effective and efficient at scale."
Kyle Gallatin
Technical Lead Manager, Handshake
"Working with Arize on our telemetry projects has been a genuinely positive experience. They are highly accessible and responsive, consistently providing valuable insights during our weekly meetings. Despite the ever-changing nature of the technology, their guidance on best practices—particularly for creating spans to address emergent edge cases—has been incredibly helpful. They've gone above and beyond by crafting tailored documentation to support our implementation of Arize with OpenTelemetry, addressing specific use cases we've presented."
Priceline
“You have to define it not only for your models but also for your products…There are LLM metrics, but also product metrics. How do you combine the two to see where things are failing? That’s where Arize has been a fabulous partner for us to figure out and create that traceability.”
Anusua Trivedi
Head of Applied AI, U.S. R&D, Flipkart
"From Day 1 you want to integrate some kind of observability. In terms of prompt engineering, we use Arize to look at the traces [from our data pipeline] to see the execution flow … to determine the changes needed there."
Kyle Weston
Lead Data Scientist, GenAI, Geotab
"The U.S. Navy relies on machine learning models to support underwater target threat detection by unmanned underwater vehicles ... After a competitive evaluation process, DIU and the U.S. Navy awarded five prototype agreements to Arize AI [and others] ... as part of Project Automatic Target Recognition using MLOps for Maritime Operations (Project AMMO).”
Defense Innovation Unit
“Arize... is critical to observe and evaluate applications for performance improvements in the build-learn-improve development loop..”
Mike Hulme
General Manager, Azure Digital Apps and Innovation, Microsoft
“For exploration and visualization, Arize is a really good tool.”
Rebecca Hyde
Principal Data Scientist, Atropos Health