Arize's open source LLM observability framework providing tracing and evaluation for LLM applications — available for free and runs on customer infrastructure.

When input data distributions change from training data — the most common cause of gradual ML model performance degradation. Arize detects feature distribution shifts that predict accuracy degradation.

What is SHAP explainability?

SHapley Additive exPlanations — a method that quantifies how much each input feature contributes to each model prediction, providing interpretable explanations required by regulated industries.

Verified July 24, 2026AI ML Observability and LLM Evaluation Platform

Arize AI

Arize AI / arize.com

AI/ML observability platform that monitors machine learning models and LLMs in production, detecting performance degradation, data drift, and model quality issues with explainable AI analysis.

Visit Arize AI

Pricing

Free

Free plan

Yes

What is Arize AI?

Arize AI is a machine learning observability platform that monitors deployed ML models and LLMs in production — detecting when model performance degrades, input data drifts from training distribution, or model outputs change in unexpected ways. As AI deployments expand, knowing when models are underperforming is as critical as the initial model deployment.

Phoenix, Arize's open source LLM observability framework, provides tracing and evaluation for LLM applications — tracking which prompts produce which outputs, evaluating response quality, detecting hallucinations, and monitoring latency. For teams building production LLM applications, Phoenix provides the observability infrastructure that debugging AI systems requires.

Model performance monitoring tracks accuracy, precision, recall, and custom business metrics for deployed ML models — alerting when performance metrics deviate from baseline. Performance degradation often occurs gradually and invisibly without dedicated monitoring.

Data drift detection identifies when input data distributions change from training data — the most common cause of silent ML model degradation in production. When input feature distributions shift (new customer demographics, changed product catalogue, seasonal patterns), model accuracy can degrade without obvious error signals.

Explainable AI analysis uses SHAP values to explain model predictions — identifying which input features most influence each prediction. For regulated industries requiring model explanation for adverse decisions, SHAP-based explainability provides the required interpretability layer.

How Arize AI works

Arize AI runs as ml platform software built around data and text workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on web, python, and api, with API access for teams that want to embed it into their own products.

Key Features

What makes it worth shortlisting

The capabilities that matter most for teams evaluating Arize AI.

Phoenix LLM observability

Open source tracing and evaluation framework for LLM applications tracking prompts, outputs, quality metrics, and hallucination detection — free and runs on customer infrastructure.

Model performance monitoring

Tracks ML model accuracy and custom metrics in production, alerting on performance degradation that occurs gradually without obvious error signals.

Data drift detection

Identifies when input feature distributions shift from training data — detecting the condition that most commonly causes silent ML model performance degradation.

Phoenix (open source LLM observability and tracing)LLM evaluation (hallucination, relevance, toxicity)

Best use cases

ML model performance monitoring in production

LLM application tracing and evaluation

Data drift detection

AI explainability for regulated models

LLM hallucination and quality monitoring

Who should use it

ML engineers

Data scientists

LLM application developers

AI/ML platform teams

MLOps engineers

Pros

Phoenix open source LLM observability is free and runs on customer infrastructure for privacy-sensitive workloads
Free plan 500K predictions enables genuine monitoring evaluation without payment
LLM evaluation (hallucination detection, relevance scoring) addresses the growing need for LLM quality monitoring
Explainable AI SHAP analysis provides regulated industry interpretability requirements

Cons

Production ML monitoring requires ML team investment to set up baseline metrics and configure alerts
LLM evaluation metrics are still maturing — no universally accepted ground truth for hallucination detection
Competing with WhyLabs, Evidently AI (rank 613), and Fiddler AI (covered) for ML observability

Pricing Analysis

Is it worth the price?

Free plan (500K predictions/month). Growth $200/month. Enterprise custom.

Model

Freemium

Starting price

Free

Free trial

Similar Tools

Tools like Arize AI

WhyLabs provides competing ML monitoring. Evidently AI (rank 613) provides open source ML monitoring. Fiddler AI (covered) provides ML observability with explainability. Langfuse (covered) focuses specifically on LLM observability.

Weights & Biases AI

Weights & Biases / AI ML Experiment Tracking and Model Management

Developer Tools

AI/ML experiment tracking, model registry, and evaluation platform that helps teams track ML experiments, compare model versions, collaborate on training runs, and evaluate LLM outputs.

ML experiment tracking and comparisonLLM application observability and evaluationModel version management

PythonCLIWebData

Comparison

Arize AI vs Weights & Biases AI

A side-by-side look at the closest alternative in this category.

Full comparison

Arize AI

Weights & Biases AI

Weights & Biases

Overview

Rating

—

Technical & deployment info

Key facts about model providers, platforms, and team support.

Model Provider

OpenAI, Arize

Platforms

Web, Python, API

Deployment

SaaS, Open Source

Integrations

OpenAI, Anthropic, LangChain, Hugging Face, MLflow, API

Team Collaboration

Yes

Launch Year

2023

Trust

Security & privacy

Compliance signals and data-handling notes as reported by the vendor.

SOC 2 Type II. ISO 27001. GDPR compliant. Enterprise data handling agreements.

Model prediction and input data processed on Arize's infrastructure for monitoring. Phoenix open source runs on customer infrastructure. Review Arize's data handling for production ML data sharing.

Reviews

What users are saying

FAQ

Common questions about Arize AI

Yes, free plan with 500K predictions/month. Growth $200/month.

Editorial Verdict

Should you use Arize AI?

Arize is the best AI ML observability platform for ML teams wanting model performance monitoring, data drift detection, and LLM evaluation with an open source Phoenix option for sensitive deployments.

Last verified July 24, 2026.