Arize AI is a unified AI observability platform designed to help engineers monitor, troubleshoot, and optimize machine learning and generative AI models at scale. It supports traditional ML models (like classification, regression) and modern AI systems such as generative AI and retrieval-augmented generation (RAG) chatbots.

What types of models does Arize support?

Arize natively supports binary classification, multi-class classification, regression, ranking, natural language processing (NLP), and computer vision (CV) models. It also supports various data types including tabular/structured data (strings, floats, booleans) and embeddings for unstructured data like images and text.

How do I get started with Arize?

To start, you set up your model by sending in training, validation, and/or production data (or a subset of these) for ingestion. You verify data ingestion in the ‘Data Ingestion’ tab, then set a performance baseline and choose metrics to monitor in the ‘Config’ tab. The platform processes and indexes data in about 10 minutes before full visibility.

What kind of data can I send to Arize?

You can send historical data with prediction timestamps up to 2 years old. Arize accepts null values in predictions or actuals as long as each record contains at least one non-null prediction, actual, or feature importance. New features in production are handled by monitoring data quality and drift.

What performance metrics does Arize support?

Arize supports many standard metrics across model types including Accuracy, Precision, Recall, F1, AUC, Log Loss, RMSE, MAE, R-squared, and also supports custom metrics. Metrics can be evaluated in aggregate or across cohorts defined by filters.

How does Arize help identify issues with my models?

Arize provides data quality checks, drift detection, and feature performance heatmaps to surface outliers, anomalous data, and poorly performing feature slices. It also offers root cause analysis workflows to drill down from symptoms to the underlying cause of model failures.

What is Arize Phoenix?

Phoenix is the open-source AI observability tool by Arize, focusing on introspecting and debugging interactions with LLMs and generative AI workflows. It complements Arize AX, the enterprise SaaS platform. Phoenix enables tracing of prompts, retriever performance, and pinpointing failure causes in complex AI systems.

Is Arize AI free or paid?

Arize Phoenix is completely open-source and free under Apache-2.0 license. The full commercial SaaS platform, Arize AX, is enterprise-grade and paid.

How do I send data and troubleshoot ingestion?

Data should be correctly formatted with model IDs, prediction IDs, and timestamps. If sending delayed actuals, ensure the matching prediction row exists for proper joining. Null or missing values in features or predictions are generally accepted but must conform to minimum row completeness. The Data Ingestion tab helps verify data receipt, and troubleshooting guides are available if issues arise.

Can Arize monitor AI agents built with tools like Langflow?

Yes, Arize can monitor performance, debug issues, and provide observability into AI agents, such as those built with Langflow, by integrating Arize Platform and Phoenix for comprehensive insights across workflows.

Arize AI

4.6

• 39 views

Arize AI helps engineers build and manage reliable AI agents and models. It offers a single platform to develop, evaluate, and monitor all types of AI, from traditional machine learning to modern generative AI, ensuring they work well in the real world.

Visit Website

Free Option

Overview Features Use Cases FAQ Integrations Pricing

About Arize AI

Who It's For

This platform is made for AI engineers and teams who build and manage machine learning and generative AI models or agents. It's for those who need to understand how their AI performs in the real world and quickly fix issues.

What You Get

You get a unified platform for AI development, evaluation, and monitoring. This includes tools for prompt optimization, automated evaluations, real-time dashboards, and tracing to quickly find and address performance problems like drift.

How It Works

The platform connects your AI's development and production phases. You send your model's data, and Arize monitors its behavior and evaluates its outputs. This creates a feedback loop, using real-world performance insights to continuously improve your AI applications.

Visit Website

Your Agent Here

Reach users actively searching for AI tools. 50,000+ monthly targeted views available.

Become a Sponsor

Features & Capabilities

⚙️ AI Agent Development

Prompt Optimization

Automatically optimize agents using evaluations and annotations to make them self-improving.

Replay in Playground

Debug and perfect prompts in a dedicated playground environment designed for development.

Prompt Serving and Management

Manage prompts, rapidly serve optimizations, and enable collaborative changes across teams.

🔬 AI Evaluation & Quality Assurance

CI/CD Experiments

Detect prompt and agent regressions early with continuous integration/continuous delivery driven by evaluations.

LLM as a Judge Evaluation

Automatically evaluate prompts and agent actions at scale using LLM-as-a-Judge for robust development.

Human Annotation & Labeling

Manage labeling queues, production annotations, and golden dataset creation in a centralized platform.

📊 Real-time AI Observability

Open Standard Tracing

Trace agents and frameworks with speed and flexibility using OpenTelemetry for comprehensive visibility.

Online Evaluation

Catch problems instantly with real-time AI evaluating AI to ensure continuous performance.

Monitoring and Dashboards

Monitor AI performance in real time with advanced analytical dashboards for deep insights.

📈 ML Model Performance Monitoring

Pinpoint Model Failures

Quickly identify failure modes, underperforming slices, and root causes to optimize model performance and reduce bias.

Detect Model Drift

Continuously monitor feature and model drift across environments to anticipate and address performance shifts.

Monitor Embeddings

Track embedding drift in NLP, computer vision, and multi-modal models to maintain high-quality feature representations.

🔗 Open Platform & Interoperability

Open-Source Evaluation Models

Provides open-source evaluation libraries and models for full transparency and customization.

Open Standard Tracing (OpenTelemetry)

Built on OpenTelemetry, offering vendor, framework, and language-agnostic LLM observability.

Standard Data Formats

Ensures unparalleled interoperability and data control through the use of standard data file formats.

Use Cases

Ensuring Reliable Performance of Generative AI Agents

Generative AI agents are inherently complex and can exhibit non-deterministic behavior, making it challenging to understand and fix issues like hallucinations or suboptimal responses in production. Arize provides end-to-end LLM observability, including tracing, automated evaluations (LLM as a Judge), and prompt optimization tools, allowing AI engineers to quickly debug agent interactions, identify root causes, and continuously improve output quality at scale.

B2B SaaSFor: AI/ML Engineers

Proactive Detection and Resolution of ML Model Failures

Traditional machine learning models frequently encounter performance degradation due to data quality issues, concept drift, or subtle shifts in feature distributions in production. Arize offers robust ML and Computer Vision observability, enabling teams to pinpoint model failures, detect drift across various data types (including embeddings), and perform root cause analysis to maintain high accuracy and reliability for critical business applications.

Financial ServicesFor: Data Scientists

Streamlining AI Agent and Prompt Development through Rapid Evaluation

Iterating on AI agents and optimizing prompts can be a time-consuming process without efficient feedback mechanisms and robust testing. Arize accelerates the development lifecycle by providing a prompt playground for debugging, CI/CD experiments to detect regressions, and human annotation queues, allowing developers to rapidly evaluate new agents and prompts and ensure they are production-ready.

TechnologyFor: AI/ML Engineers

Gaining Business Visibility and Trust in Enterprise AI Deployments

Business leaders and product managers require clear, actionable insights into the performance and business impact of AI models, often struggling to connect technical metrics with strategic outcomes. Arize provides enterprise-grade dashboards and monitoring that bridge the gap between technical AI performance and business KPIs, enabling stakeholders to assess trustworthiness, manage costs, and articulate the value of their AI investments.

Large EnterprisesFor: Go-to-Market Leaders

Frequently asked questions

Similar Agents

Agenta

Category mock

Agenta helps teams build, test, and manage reliable AI applications.

AgentOps

Category mock

Build, debug, and deploy reliable AI agents with full visibility.

Phoenix

Category mock

Open-source tool to trace, evaluate, and fix your AI models.