Keywords AI

Datadog LLM vs Langfuse

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

Quick Comparison

	Datadog LLM	Langfuse
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Enterprise	Open Source
Best For	Enterprise teams already using Datadog who want to add LLM monitoring	Teams who want open-source LLM observability they can self-host and customize
Website	datadoghq.com	langfuse.com
Key Features	LLM monitoring within Datadog platform Unified APM + LLM observability Automatic instrumentation Cost and token tracking Integration with existing Datadog dashboards	Open-source LLM observability Detailed trace and span tracking Prompt management Evaluation scoring Self-hosted and cloud options
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	Self-hosted LLM monitoring Open-source tracing for AI applications Prompt versioning and management Cost tracking across providers Community-driven observability

When to Choose Datadog LLM vs Langfuse

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 Langfuse if you need

Self-hosted LLM monitoring
Open-source tracing for AI applications
Prompt versioning and management

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 Langfuse

Langfuse is an open-source LLM observability platform that provides tracing, analytics, prompt management, and evaluation for AI applications. It captures detailed traces of LLM calls, supports custom scoring, and integrates with LangChain, LlamaIndex, Vercel AI SDK, and raw API calls. Langfuse can be self-hosted for data privacy or used as a managed cloud service. Its open-source model and generous free tier make it popular with startups and developers.

View Langfuse 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 →