Keywords AI

Arize AI vs Ragas

Compare Arize AI and Ragas side by side. Both are tools in the Observability, Prompts & Evals category.

Quick Comparison

	Arize AI	Ragas
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Open Source
Best For	ML teams who need comprehensive observability spanning traditional ML models and LLM applications	Developers building RAG applications who need specialized evaluation metrics
Website	arize.com	ragas.io
Key Features	ML observability with LLM support Embedding drift detection Performance dashboards Automatic monitors and alerts Open-source Phoenix companion	RAG-specific evaluation framework Component-wise metrics for RAG Synthetic test data generation LLM-as-judge evaluators Open-source Python library
Use Cases	Production ML and LLM monitoring Embedding quality monitoring Model performance tracking Drift detection for AI systems Root cause analysis for AI failures	Evaluating RAG pipeline quality end-to-end Measuring retrieval precision and recall Testing faithfulness and answer relevance Generating synthetic evaluation datasets Benchmarking RAG across configurations

When to Choose Arize AI vs Ragas

Choose Arize AI if you need

Production ML and LLM monitoring
Embedding quality monitoring
Model performance tracking

Pricing: Freemium

Choose Ragas if you need

Evaluating RAG pipeline quality end-to-end
Measuring retrieval precision and recall
Testing faithfulness and answer relevance

Pricing: Open Source

About Arize AI

Arize AI provides an ML and LLM observability platform for monitoring model performance in production. For LLM applications, Arize offers trace visualization, prompt analysis, embedding drift detection, and retrieval evaluation. Their open-source Phoenix library provides local tracing and evaluation. Arize helps teams identify quality issues, debug failures, and continuously improve AI system performance.

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About Ragas

Ragas is an open-source evaluation framework specifically designed for RAG (Retrieval-Augmented Generation) pipelines. It provides metrics for context precision, context recall, faithfulness, and answer relevancy, helping teams measure and improve the quality of their RAG systems. Ragas has become the standard evaluation toolkit for teams building production RAG applications.

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What is Observability, Prompts & Evals?

Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.

Browse all Observability, Prompts & Evals tools →