Ground-truth dataset creation
Build datasets from synthetic, development, and live production data, and add subject matter expert annotations so evaluation criteria can reflect the target environment.
Galileo
Freemium
Galileo is an AI observability and evaluation platform for testing, debugging, and governing LLM and agent systems across development and production. It helps teams turn evaluation results into production guardrails and monitor AI behavior at scale.
What is Galileo?
Galileo is an AI observability and evaluation platform for teams building LLM applications and AI agents. It supports the evaluation lifecycle from dataset creation and expert annotation through offline testing, production monitoring, failure analysis, and guardrail deployment. Its stated focus is helping teams identify and address issues such as hallucinations, policy drift, security leaks, and agent failures before they affect users at scale.
The platform combines evaluation and observability rather than treating them as separate workflows. Teams can create ground-truth datasets from synthetic, development, and live production data, run prebuilt or custom evaluations, inspect agent behavior, and use evaluation results to define production controls. Galileo describes Luna models as a way to run optimized evaluations with lower latency and cost across production traffic.
Galileo Signals analyzes production traces for failure patterns that may not be covered by existing evaluations or manual searches. Its Insights engine adds analysis of agent behavior and related execution data to support root-cause investigation and corrective action. Deployment options listed by Galileo include SaaS, virtual private cloud, and on-premises environments.
What can Galileo do?
Prebuilt and custom evaluations
Start with more than 20 evaluations for RAG, agents, safety, and security, then create custom evaluators to encode domain-specific requirements.
Production signal detection
Galileo Signals analyzes production traces to surface unknown failure patterns, including issues such as security leaks, policy drift, and cascading failures, without requiring teams to search for a problem first.
Agent behavior analysis
The Insights engine examines agent behavior and related execution data—including traces, prompts, functions, context, datasets, and signals—to identify failure modes and support debugging.
Evaluation-to-guardrail workflow
Optimized evaluations can be distilled into Luna models for low-latency production monitoring, while evaluation scores can control agent actions, tool access, and escalation paths through guardrail policies.
Use Cases
“Validate RAG and agent applications before release”
Development teams can assemble datasets, add expert annotations, and run RAG- or agent-focused evaluations before deploying an application.
“Investigate failures in production”
AI engineers can use Signals to detect patterns across production traces and use Insights to examine likely causes instead of relying only on manual log searches.
“Monitor safety and security behavior”
Teams can apply safety and security evaluations to identify issues such as harmful behavior, data leaks, or policy drift in live AI interactions.
“Govern multi-step agent execution”
Teams operating agents can use evaluation scores to define controls for tool access, agent actions, and escalation paths as part of production governance.
Frequently Asked Questions
What does Galileo monitor?
Galileo monitors LLM and agent systems through evaluations, production traces, and agent execution data. The site describes coverage for RAG, agents, safety, security, prompts, functions, context, datasets, and related signals.
Can Galileo detect issues that existing evaluations do not cover?
Galileo Signals is designed to find unknown failure patterns in production traces, including patterns that teams may not know to search for or have not yet encoded as evaluations. An identified signal can be used to generate an LLM judge.
How does Galileo connect evaluations with production guardrails?
Galileo describes a workflow in which optimized evaluations are distilled into Luna models for production monitoring. Evaluation scores can then be used in guardrail policies to control agent actions, tool access, and escalation paths.
How can Galileo be deployed?
The site lists three deployment options: SaaS, virtual private cloud, and on premises. The appropriate option depends on the team's deployment and enterprise requirements.
Is there a free Galileo plan?
Yes. The pricing page lists a Free plan at $0 per month with 5,000 traces per month, unlimited users, and unlimited custom evaluations. It also lists Pro and custom Enterprise options.
Quick Facts
- Category
- AI observability and evaluation platform
- Primary users
- Developers, AI engineers, data scientists, and teams operating LLM or agent applications
- Evaluation coverage
- RAG, agents, safety, security, and custom evaluations
- Deployment options
- SaaS, virtual private cloud, and on premises
- Free plan
- $0/month; 5,000 traces per month, unlimited users, and unlimited custom evaluations
- Production workflow
- Offline evaluations can be connected to monitoring and guardrail policies
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