Galileo

Galileo is an AI evaluation and observability platform that helps engineering and AI teams measure, monitor, and safeguard generative AI applications and agents at enterprise scale.

It addresses the hard problem of knowing whether an LLM app actually works by letting teams build datasets, create custom evaluations, and run more than twenty pre-built evaluations covering RAG, agents, safety, and security use cases.

A standout capability is converting offline evaluations into production-ready safety guardrails, unifying pre-deployment testing with live runtime governance so the same quality and safety checks follow an application into production.

Galileo's Luna models are designed to monitor effectively all production traffic at a fraction of typical cost by distilling evaluations, and its insights engine identifies failure modes such as hallucinations and prescribes fixes.

The platform offers flexible deployment as SaaS, in a virtual private cloud, or on-premises, making it suitable for regulated and security-conscious organizations.

Typical users are developers and ML teams shipping LLM features, agents, and chatbots who need objective metrics, regression testing across prompt and model changes, and runtime protection.

Pros include a broad library of research-backed metrics, an eval-to-guardrail lifecycle that bridges testing and production, and enterprise-friendly deployment options; cons are that it is a specialized platform with a learning curve for teams new to systematic LLM evaluation, and full enterprise capabilities are priced for organizations rather than hobbyists.

Pricing changes often, so check the official site for current plans.

Galileo's core capabilities include 20+ pre-built evaluations for RAG, agents, and safety, Custom evaluations and dataset building, Eval-to-guardrail lifecycle for runtime protection, Luna models for low-cost full-traffic monitoring, Insights engine that diagnoses failure modes and SaaS, VPC, and on-premises deployment options.

20+ pre-built evaluations for RAG, agents, and safety is built in, Custom evaluations and dataset building is built in, Eval-to-guardrail lifecycle for runtime protection is built in, Luna models for low-cost full-traffic monitoring is built in, so you get a rounded toolkit rather than a single trick.

Each feature is designed to take the manual effort out of the task and help you reach a usable result faster, which is what makes Galileo worth a place on your shortlist.

On the plus side, users consistently highlight Broad library of research-backed metrics, Bridges offline testing and production guardrails and Enterprise-friendly deployment flexibility as the reasons they keep using Galileo.

It isn't perfect, though — Learning curve for teams new to LLM evaluation and Full capabilities are priced for organizations are the trade-offs people most often mention, so weigh those against your own priorities before you commit.

As with any AI tool, the output still benefits from a quick human review, but Galileo gets you most of the way there with far less effort.

Galileo runs on a freemium pricing model, so you can start for free and only pay once you outgrow the free tier — handy for testing it on a real task before spending anything.

AI-tool pricing changes often, so always check the current plans, seats and add-ons on the official site for the latest details before you buy. Who is Galileo for? It's best suited for evaluation and observability for llm apps and agents.

Whether you're a beginner trying this kind of AI tool for the first time or a professional who'll use it every day, it's a credible option to consider.

If you're still deciding, compare Galileo against the alternatives and the head-to-head comparisons linked below — looking at features, pricing and real user ratings side by side is the fastest way to find the right fit for your workflow and budget.