About
Galileo is an AI observability and evaluation engineering platform designed for teams building and deploying large language model (LLM) applications and autonomous agents. Unlike generic monitoring tools, Galileo solves the AI measurement problem end-to-end—from dataset curation to production guardrails—without requiring separate toolchains. At its core, Galileo lets teams capture ground-truth datasets from synthetic, development, and live production data, enriched with subject matter expert annotations. It ships with 20+ out-of-box evaluators covering RAG quality, agent behavior, safety, and security, and lets teams build custom evaluators tuned to their specific domain. Galileo auto-tunes these metrics from live feedback to achieve high accuracy (beyond generic 70% F1 baselines). What sets Galileo apart is its eval-to-guardrail lifecycle: optimized evaluators are distilled into lightweight Luna models that can monitor 100% of production traffic at 97% lower cost than full LLM-as-judge approaches. These guardrails can automatically control agent actions, tool access, and escalation paths with no glue code required. Galileo's insights engine analyzes agent traces to surface failure modes, hidden patterns, and prescribes actionable fixes—accelerating debugging and shipping cycles. It integrates with CI/CD pipelines to bring software engineering rigor to AI development. Trusted by enterprise teams and loved by developers, Galileo is the platform for organizations serious about reliable, safe, and scalable AI.
Key Features
- Eval-to-Guardrail Lifecycle: Pre-production evaluations automatically become production guardrails that control agent actions, tool access, and escalation paths—no glue code required.
- Luna Model Distillation: Distill expensive LLM-as-judge evaluators into compact Luna models that monitor 100% of production traffic at 97% lower cost and with low latency.
- 20+ Out-of-Box Evaluators: Pre-built evals for RAG quality, agent behavior, safety, and security, plus the ability to build custom evaluators encoding domain expertise.
- AI Insights Engine: Automatically analyzes agent traces to identify failure modes, surface hidden patterns, and prescribe specific fixes to accelerate debugging.
- Ground-Truth Dataset Management: Capture and curate datasets from synthetic, development, and live production data with subject matter expert annotations for continuous AI grounding.
Use Cases
- Evaluating RAG pipeline quality before and after production deployment to catch hallucinations and context failures early.
- Monitoring autonomous AI agent behavior in production to detect tool misuse, unexpected escalations, or off-policy actions in real time.
- Running automated safety and security evaluations as part of CI/CD pipelines to block harmful or policy-violating model outputs before release.
- Building domain-specific custom evaluators trained on subject matter expert annotations to accurately measure quality in specialized industries like finance, healthcare, or legal.
- Distilling expensive evaluation models into low-latency Luna guardrails that enforce quality and safety standards at enterprise traffic scale without breaking the budget.
Pros
- End-to-End Evaluation Pipeline: Unifies offline testing and online monitoring in a single platform, eliminating the need to stitch together separate tools for evals and production safety.
- Dramatic Cost Reduction: Luna model distillation enables full-traffic production monitoring at 97% lower cost compared to running LLM-as-judge evaluators on every request.
- Enterprise-Ready at Scale: Designed for enterprise workloads with auto-tuned metrics, CI/CD integration, and guardrail policies that scale with production traffic volumes.
- Actionable Failure Insights: The insights engine doesn't just detect failures—it prescribes specific fixes, reducing mean time to resolution for AI reliability issues.
Cons
- Enterprise Focus May Overwhelm Smaller Teams: The platform's depth and breadth of features can be complex to onboard for small teams or solo developers without dedicated AI ops resources.
- Pricing Transparency: Enterprise pricing and full feature access require contacting sales, making it harder for teams to evaluate total cost without a demo conversation.
- Vendor Lock-In Risk: Adopting Galileo's Luna model distillation and guardrail pipeline deeply integrates your AI workflow with the platform, creating switching costs over time.
Frequently Asked Questions
What types of AI applications does Galileo support?
Galileo supports RAG (Retrieval-Augmented Generation) pipelines, autonomous AI agents, and general GenAI applications. It provides specialized evaluators for each use case covering quality, safety, and security dimensions.
How does Galileo reduce the cost of production monitoring?
Galileo distills expensive LLM-as-judge evaluators into lightweight Luna models. These smaller models can evaluate 100% of production traffic at 97% lower cost while maintaining high accuracy, making full-coverage monitoring economically feasible.
Can I build custom evaluators for my domain?
Yes. In addition to 20+ out-of-box evaluators, Galileo lets you build custom evaluators that encode your specific domain expertise. The platform also auto-tunes metrics from live production feedback to improve accuracy over time.
How does Galileo integrate with existing CI/CD pipelines?
Galileo is designed to bring unit testing and CI/CD rigor into the AI development lifecycle. Evaluation runs can be incorporated into deployment pipelines so that quality gates are enforced before new model versions or prompts reach production.
Is there a free tier available?
Yes, Galileo offers a free tier to get started. Enterprise plans with full-scale monitoring, guardrail policies, and advanced features are available and require contacting the Galileo team for pricing.
