Galileo AI Evals

Galileo is the rare eval platform that actually cuts costs while improving accuracy. The core proposition—distilling expensive LLM-as-judge evaluators into compact Luna models—is what makes it stand out. For teams shipping AI agents to production, the eval-to-guardrail lifecycle is a genuine timesaver: you build evals once, then deploy them as real-time guardrails without glue code. The insights engine goes beyond dashboards by surfacing failure modes and prescribing fixes, which means less time debugging and more time shipping. When should you pick Galileo? If your team runs agent-based systems at scale and needs continuous evaluation without latency blowout. The 96% cost reduction on inference for monitoring is real—tested by enterprise customers like Writer and Cisco. New features like Luna Studio (May 2026) make evaluations more trustworthy, and Eval Engineer brings eval expertise directly into Claude and Codex workflows. The April 2026 auto-improvement mechanism that learns from manual reviews is a nice touch, closing the feedback loop. When should you pass? Small teams that just need basic LLM monitoring might find the setup overhead too high. The pricing scales with trace volume, so startups with massive trace loads on a tight budget should watch costs. If you don't need production guardrails or can't afford vendor lock-in, a simpler observability tool may suffice. Compared to LangSmith, Galileo is more expensive at the low end but offers guardrail deployment and Luna models that LangSmith lacks. Weights & Biases is stronger for experimentation tracking but doesn't do production guardrails. For agent-heavy enterprises that care about reliability, Galileo's lifecycle approach is a clear winner—just be ready for the investment in setup and tuning. Real-world