Galileo is an AI observability and evaluation engineering platform that bridges the gap between offline evaluations and production guardrails, enabling developers to build, monitor, and improve reliable LLM applications and AI agents at scale. It solves the challenge of ensuring AI application quality by providing proprietary Evaluation Foundation Models (EFMs) that deliver research-backed metrics specifically designed for assessing LLM outputs, RAG pipeline quality, and agentic workflow performance. Founded by AI veterans from Google AI, Apple Siri, and Google Brain, Galileo has raised $68 million and serves enterprises including HP, Twilio, Reddit, and Comcast.
Galileo provides comprehensive tracing for multi-step agent completions with visualizations that help developers pinpoint inefficiencies and errors, automatic RAG monitoring that tracks chunk-level metrics like Context Adherence and Chunk Utilization without additional setup, agentic evaluations launched in 2025 for measuring AI agent performance at every decision level, and customizable guardrails that can be deployed as production safety nets. The platform offers a streamlined observability experience specifically designed for LLM use cases, with structured insights for understanding latency, token costs, failure modes, and output quality without requiring complex trace propagation or external backend configuration.
Galileo targets AI engineering teams, MLOps practitioners, and enterprises building production LLM applications who need specialized observability and evaluation tools beyond generic monitoring solutions. It integrates with major LLM providers, agent frameworks, and deployment platforms through lightweight SDK instrumentation, making it easy to add evaluation and monitoring to existing AI applications. Galileo is particularly valuable for teams deploying RAG systems, multi-agent workflows, and customer-facing AI applications where output quality, safety, and reliability directly impact business outcomes, providing the evaluation infrastructure needed to confidently ship and iterate on AI-powered features.