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About
Galileo is an enterprise-grade AI observability and eval engineering platform designed to solve the AI measurement problem for teams shipping GenAI applications and agents. Rather than relying on generic metrics, Galileo lets teams capture ground-truth datasets from synthetic, development, and live production data — then auto-tunes evaluation metrics using live feedback to achieve high-fidelity, domain-specific evaluators. The platform ships with 20+ out-of-the-box evaluators covering RAG pipelines, multi-step agents, safety, and security. Teams can also build custom evaluators to encode domain expertise. What makes Galileo unique is its eval-to-guardrail lifecycle: optimized evaluators are distilled into compact Luna models that run at 97% lower cost than LLM-as-judge approaches, enabling 100% traffic monitoring with low latency. Galileo's insights engine analyzes agent behavior to surface failure modes, identify hidden patterns, and prescribe actionable fixes — accelerating debugging and deployment cycles. Pre-production evals seamlessly become production governance policies that automatically control agent actions, tool access, and escalation paths without any glue code. Ideal for AI engineers, platform teams, and enterprises building RAG systems, autonomous agents, or safety-critical GenAI products, Galileo brings the rigor of unit testing and CI/CD pipelines to the AI development lifecycle.
Key Features
- 20+ Out-of-the-Box Evaluators: Pre-built evals covering RAG quality, multi-step agent behavior, safety, and security — ready to use without custom configuration.
- Luna Model Distillation: Distills expensive LLM-as-judge evaluators into compact Luna models that monitor 100% of production traffic at 97% lower cost and with low latency.
- Eval-to-Guardrail Lifecycle: Automatically promotes offline evaluation scores into production guardrail policies that govern agent actions, tool access, and escalation paths — no glue code required.
- AI Insights Engine: Analyzes agent behavior to detect failure modes, surface hidden patterns, and prescribe concrete fixes, accelerating debugging and iteration cycles.
- Ground-Truth Dataset Management: Capture and manage datasets from synthetic data, development runs, and live production traffic, enriched by subject matter expert annotations.
Use Cases
- Evaluating RAG pipeline accuracy and grounding quality before and after deployment to production.
- Monitoring multi-step AI agent behavior in real time to detect hallucinations, incorrect tool calls, and reasoning failures.
- Implementing safety and security guardrails for customer-facing GenAI applications to block harmful or policy-violating responses.
- Running automated LLM evaluation as part of CI/CD pipelines to catch regressions before shipping new model versions or prompt changes.
- Building domain-specific evaluators and distilling them into cost-efficient monitoring models for enterprise GenAI systems.
Pros
- Comprehensive Eval Coverage: Supports RAG, agent, safety, and security evaluations out of the box, with the ability to add custom evaluators for domain-specific needs.
- Massive Cost Reduction for Monitoring: Luna model distillation enables full-traffic production monitoring at 97% lower cost compared to running LLM-as-judge evaluators continuously.
- Unified Dev-to-Production Pipeline: Eliminates the gap between offline testing and online safety by making the same evals work as both development checks and real-time guardrails.
- Actionable Failure Diagnostics: The insights engine goes beyond surfacing errors — it prescribes specific fixes, reducing time-to-resolution for AI failures.
Cons
- Steeper Learning Curve for Smaller Teams: The breadth of features and enterprise focus may make initial setup and configuration challenging for small or early-stage teams.
- Pricing Lacks Full Transparency: Enterprise pricing details require contacting sales, making it harder for teams to estimate costs without booking a demo.
- Custom Evaluators Require Domain Expertise: While custom evals are supported, building high-quality domain-specific evaluators still requires significant subject matter expertise and effort.
Frequently Asked Questions
What is Galileo AI?
Galileo is an AI observability and evaluation platform that helps teams build, evaluate, and monitor GenAI applications and agents. It provides tools to run offline evaluations, detect failures in production, and enforce guardrails — all within a unified platform.
What types of evaluations does Galileo support?
Galileo supports 20+ out-of-the-box evaluators for RAG pipelines, multi-step agents, safety, and security. Teams can also build fully custom evaluators tailored to their specific domain or product requirements.
How does the eval-to-guardrail process work?
After evaluators are tuned and validated offline, Galileo distills them into lightweight Luna models. These models run in production at low latency and low cost, monitoring 100% of traffic and automatically enforcing policies on agent behavior and tool access.
What are Luna models?
Luna models are compact, distilled versions of LLM-as-judge evaluators created by Galileo. They replicate the accuracy of larger models but run on L4 GPUs with low latency and at 97% lower cost, making them practical for full-traffic production monitoring.
Is there a free tier available?
Yes, Galileo offers a free tier to get started. Teams can sign up and begin building evaluations without upfront cost, with paid and enterprise plans available for larger-scale production needs.
