Galileo AI — Observability, Prompts & Evals Platform
Observability, Prompts & EvalsLayer 4FreemiumWhat is Galileo AI?
Galileo is an AI observability and evaluation platform designed to provide AI reliability for teams across the entire development lifecycle. The platform offers real-time observability that continuously evaluates systems in production, sending alerts if something goes wrong or if interactions drift from training data. Galileo provides powerful, research-backed metrics and evaluation-powered development workflows to help teams build, scale, monitor, and protect AI applications in real-time. The platform is recognized as a Gartner Cool Vendor and serves as a comprehensive solution for AI teams looking to ensure reliability and performance of their LLM applications. With the Agent Reliability Platform available as part of their free tier, Galileo makes advanced AI observability accessible to teams of all sizes. The platform emphasizes scalability, security, and premium support for enterprise customers while maintaining an approachable entry point through their generous free tier.
Key Features
- ✓LLM output quality evaluation
- ✓Hallucination guardrails
- ✓RAG evaluation metrics
- ✓Data-centric AI debugging
- ✓Automated error detection
Pros & Cons
Pros
- +Generous free tier with 5,000 traces/month including Agent Reliability Platform
- +Recognized as Gartner Cool Vendor demonstrating industry validation
- +Research-backed metrics providing scientific rigor to evaluations
- +Real-time observability with automated drift detection and alerting
- +Established team of 142 employees providing solid support capacity
Cons
- -Limited public information about advanced features and capabilities
- -Pro plan pricing jumps from free to $100/month with potential gaps for small teams
- -Less community-driven compared to open-source alternatives like Langfuse
Galileo AI Pricing
Common Use Cases
AI teams who need to measure and improve the quality of their LLM outputs
- •Monitoring LLM output quality
- •Detecting and preventing hallucinations
- •Evaluating RAG pipeline accuracy
- •Debugging data quality issues
- •Continuous quality assurance
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