Keywords AI

Ragas vs Traceloop

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

Quick Comparison

	Ragas	Traceloop
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Open Source	open-source
Best For	Developers building RAG applications who need specialized evaluation metrics	Teams already using Datadog/Splunk wanting LLM observability
Website	ragas.io	traceloop.com
Key Features	RAG-specific evaluation framework Component-wise metrics for RAG Synthetic test data generation LLM-as-judge evaluators Open-source Python library	OpenTelemetry OpenLLMetry Vendor-neutral OTel integration
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 Ragas vs Traceloop

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 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 Traceloop

The OpenTelemetry standard for AI. Pipes LLM telemetry into Datadog, Splunk, or any OTel backend.

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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.

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