Keywords AI

Galileo AI vs Langfuse

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

Quick Comparison

Galileo AI
Galileo AI
Langfuse
Langfuse
CategoryObservability, Prompts & EvalsObservability, Prompts & Evals
PricingFreemiumOpen Source
Best ForAI teams who need to measure and improve the quality of their LLM outputsTeams who want open-source LLM observability they can self-host and customize
Websiterungalileo.iolangfuse.com
Key Features
  • LLM output quality evaluation
  • Hallucination guardrails
  • RAG evaluation metrics
  • Data-centric AI debugging
  • Automated error detection
  • Open-source LLM observability
  • Detailed trace and span tracking
  • Prompt management
  • Evaluation scoring
  • Self-hosted and cloud options
Use Cases
  • Monitoring LLM output quality
  • Detecting and preventing hallucinations
  • Evaluating RAG pipeline accuracy
  • Debugging data quality issues
  • Continuous quality assurance
  • Self-hosted LLM monitoring
  • Open-source tracing for AI applications
  • Prompt versioning and management
  • Cost tracking across providers
  • Community-driven observability

When to Choose Galileo AI vs Langfuse

Galileo AI
Choose Galileo AI if you need
  • Monitoring LLM output quality
  • Detecting and preventing hallucinations
  • Evaluating RAG pipeline accuracy
Pricing: Freemium
Langfuse
Choose Langfuse if you need
  • Self-hosted LLM monitoring
  • Open-source tracing for AI applications
  • Prompt versioning and management
Pricing: Open Source

About Galileo AI

Galileo is a data intelligence platform for AI that helps teams evaluate, debug, and improve LLM applications. It provides metrics for hallucination detection, context adherence, chunk quality, and response completeness. Galileo's guardrails can be deployed in production to catch quality issues in real-time.

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.

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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