MLOps & experiment tracking · Arize AI, Inc.

Arize AI

Hosted AI observability platform that monitors deployed ML models and LLM applications for drift, quality, and performance issues.

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Arize AI (product name Arize AX) is a production model-monitoring and observability platform, not an experiment tracker: it ingests predictions, features, and outcomes from live models to detect drift, data-quality problems, and performance degradation, and it extends the same approach to LLM applications with tracing, evaluations, and human annotation workflows for agents and prompts. It belongs in the same bucket as Evidently AI, WhyLabs, and Fiddler AI rather than alongside MLflow or W&B. Arize is offered as hosted SaaS with a self-hosted/VPC option for enterprise customers with data-residency requirements. Free and Pro tiers cap monthly trace-span volume, data ingestion, and retention window, while unlimited users, evaluations, experiments, and annotations are included at every tier; Enterprise plans remove the volume caps and add custom retention and deployment options.

At a glance

Vendor Arize AI, Inc.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for AI-native teams needing unified monitoring across both predictive ML models and LLM applications.

Pricing

Free tier covers a limited number of trace spans and issues per month; Pro is a flat monthly rate with higher caps; Enterprise is custom-quoted with unlimited volume and choice of SaaS or self-hosted deployment.

Plan Price Notes
AX Free $0 10 issues/month, 25k trace spans/month, 1GB ingestion/month, 15-day retention; unlimited users, evals, experiments, annotations
AX Pro $50/month 25 issues/month, 50k trace spans/month, 10GB ingestion/month, 30-day retention
AX Enterprise Custom Custom trace spans, ingestion volume and retention; SaaS or self-hosted deployment; startup pricing available

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Production drift and data-quality monitoring
  • LLM/agent tracing and evaluation
  • Human annotation workflows
  • Root-cause and issue detection
  • Experiment comparison for model/prompt versions
  • Self-hosted and SaaS deployment options
  • Alerting and dashboarding

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Evidently AI Open-source and cloud model-monitoring tool that checks deployed ML/LLM systems for drift, data quality, and performance issues. WhyLabs Formerly a hosted AI-observability platform for monitoring model and data drift; the company has since shut down and open-sourced its stack. Fiddler AI Hosted AI observability and guardrails platform for monitoring deployed ML models and LLM/agent applications.

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Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data ingestion Moving data from its source systems into a storage or processing system where it can be transformed and analyzed. LLM observability Monitoring a language model's live behavior in production, tracking its inputs, outputs, latency, cost, and failure patterns. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.