Keywords AI

Datadog LLM vs Ragas

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

Quick Comparison

	Datadog LLM	Ragas
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Enterprise	Open Source
Best For	Enterprise teams already using Datadog who want to add LLM monitoring	Developers building RAG applications who need specialized evaluation metrics
Website	datadoghq.com	ragas.io
Key Features	LLM monitoring within Datadog platform Unified APM + LLM observability Automatic instrumentation Cost and token tracking Integration with existing Datadog dashboards	RAG-specific evaluation framework Component-wise metrics for RAG Synthetic test data generation LLM-as-judge evaluators Open-source Python library
Use Cases	Unified monitoring for AI and traditional services Enterprise LLM monitoring at scale Correlating LLM performance with infrastructure Compliance and audit logging Large-scale production monitoring	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 Datadog LLM vs Ragas

Choose Datadog LLM if you need

Unified monitoring for AI and traditional services
Enterprise LLM monitoring at scale
Correlating LLM performance with infrastructure

Pricing: Enterprise

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 Datadog LLM

Datadog's LLM Observability extends its industry-leading APM platform to AI applications. It provides end-to-end tracing from LLM calls to infrastructure metrics, prompt and completion tracking, cost analysis, and quality evaluation—all integrated with Datadog's existing monitoring, logging, and alerting stack. Ideal for enterprises already using Datadog who want unified observability across traditional and AI workloads.

View Datadog LLM profile →Visit website

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.

View Ragas profile →Visit website

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 →