Keywords AI

Ragas vs Weights & Biases

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

Quick Comparison

	Ragas	Weights & Biases
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Open Source	Freemium
Best For	Developers building RAG applications who need specialized evaluation metrics	ML engineers and researchers who need comprehensive experiment tracking
Website	ragas.io	wandb.ai
Key Features	RAG-specific evaluation framework Component-wise metrics for RAG Synthetic test data generation LLM-as-judge evaluators Open-source Python library	ML experiment tracking Model and dataset versioning Collaborative dashboards Sweeps for hyperparameter tuning Prompt monitoring and evaluation
Use Cases	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	ML experiment tracking and comparison Model training run management Team collaboration on ML projects Hyperparameter optimization Model registry and versioning

When to Choose Ragas vs Weights & Biases

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

Choose Weights & Biases if you need

ML experiment tracking and comparison
Model training run management
Team collaboration on ML projects

Pricing: Freemium

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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About Weights & Biases

Weights & Biases (W&B) is the leading experiment tracking and ML operations platform, now extended to LLM applications. W&B Traces provides observability for LLM pipelines, while W&B Weave offers evaluation and production monitoring. The platform also supports model training tracking, hyperparameter sweeps, and artifact management, making it a comprehensive MLOps solution.

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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 →