Arize AI
by Arize AI
Arize AI is an ML and LLM observability platform that monitors model performance, data drift, and output quality in production, extending from traditional machine learning monitoring into large language model tracing and evaluation via its…
Definition
Arize AI is an ML and LLM observability platform that monitors model performance, data drift, and output quality in production, extending from traditional machine learning monitoring into large language model tracing and evaluation via its open-source Phoenix project. It helps teams detect and diagnose degradation in deployed models.
Overview
Arize AI was founded to address a gap in the machine learning lifecycle: once a model is deployed, most teams have limited visibility into how it performs on real-world data over time, making it hard to catch silent failures like data drift, feature skew, or declining accuracy before they cause business impact. Arize's core platform ingests model predictions, ground truth (when available), and feature data to continuously monitor performance metrics, detect drift between training and production data distributions, and surface explanations for why a model's behavior changed. As generative AI adoption grew, Arize extended its observability approach to LLM and RAG applications, adding capabilities for tracing multi-step LLM calls, evaluating retrieval quality, detecting hallucinations, and scoring output quality using both heuristic and LLM-as-judge methods. A significant part of this expansion is Phoenix, Arize's open-source LLM observability and evaluation library, which developers can run locally or self-hosted to trace, debug, and evaluate LLM applications (including RAG pipelines and agents) without necessarily adopting the full commercial platform. Arize's platform integrates with common ML and LLM frameworks and supports both structured ML models (classification, regression, ranking) and unstructured LLM workloads within a single observability product, which differentiates it from tools built exclusively for either traditional ML monitoring or LLM-specific evaluation. It competes with WhyLabs and Fiddler in ML observability, and with tools like Braintrust and Galileo in the LLM evaluation space, positioning itself as a platform that spans both traditional and generative AI monitoring needs for enterprise ML teams.
Key Features
- Production monitoring for both traditional ML models and LLM applications
- Data drift and feature skew detection between training and production data
- Root-cause analysis tooling for diagnosing model performance degradation
- LLM tracing, evaluation, and RAG-specific quality metrics via Phoenix
- Open-source Phoenix library for local or self-hosted LLM observability
- Hallucination and retrieval-quality detection for RAG pipelines
- Support for structured (classification, regression, ranking) and generative model types
- Integrations with common ML frameworks and LLM orchestration tools