Keywords AI

HoneyHive vs Ragas

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

Quick Comparison

	HoneyHive	Ragas
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	paid	Open Source
Best For	Enterprise teams managing prompts and running evals	Developers building RAG applications who need specialized evaluation metrics
Website	honeyhive.ai	ragas.io
Key Features	Prompt management Regression testing SOC 2 Annotation queues	RAG-specific evaluation framework Component-wise metrics for RAG Synthetic test data generation LLM-as-judge evaluators Open-source Python library
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

When to Choose HoneyHive vs Ragas

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 HoneyHive

Prompt management + regression evals in one platform. SOC 2 compliant with annotation queues.

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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 →