Galileo AI is an AI observability and evaluation platform designed to prevent AI failures. It helps teams turn offline evaluations into production guardrails that monitor live AI systems. The focus is on reliability across the eval engineering lifecycle.

What does it mean to turn offline evals into production guardrails?

Galileo AI lets teams develop evaluation metrics during testing and then apply those same checks to monitor AI in production. This means the criteria used to assess a model offline become live safeguards once it is deployed. The approach aims to catch failures before they affect users.

How does Galileo AI help with AI agents?

Galileo AI provides insights into agent behavior, which helps teams understand and debug how their AI systems act. This visibility is meant to accelerate deployments by surfacing issues early. Observability into agents supports more confident releases.

Who should use Galileo AI?

Galileo AI is aimed at teams building and deploying AI systems who need to ensure reliability and avoid failures. It serves developers and engineers responsible for AI quality in production. The platform supports the full evaluation engineering workflow.

What is eval engineering in Galileo AI's context?

Eval engineering refers to systematically building, running, and maintaining evaluations of AI systems across their lifecycle. Galileo AI frames its platform around this discipline, from creating accurate metrics to monitoring production behavior. The goal is dependable AI through rigorous, continuous evaluation.

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Galileo AI

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The AI observability and eval engineering platform where offline evals become production guardrails.

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About Galileo AI

What Galileo AI does

Galileo is an AI observability and evaluation platform for building and operating generative AI applications and agents. It helps teams measure quality offline and then turn those evaluations into production guardrails that monitor and control live AI behavior.

Key capabilities

20+ built-in evaluation metrics for RAG, agents, safety, and security, plus custom evaluators
Production guardrails that control agent actions, tool access, and escalation paths
Efficient evaluation models that monitor full production traffic at lower cost
Insights and debugging that identify failure patterns and root causes
Deployment as SaaS, virtual private cloud, or on-premises

Who it's for

Galileo is built for enterprise AI and development teams shipping agents and RAG applications who need reliability at production scale. It supports evaluating model and agent behavior, debugging failures faster, and enforcing safety and security guardrails, making it a fit for organizations that must monitor and safeguard generative AI in production.

Key capabilities

Offline evals to production guardrails

Groundtruth data capture and annotation

Auto-tuned evaluation metrics (F1 scores > 70%)

Luna models for low-cost, low-latency monitoring

20+ out-of-box evals (RAG, agents, safety, security)

Custom evaluator building

Insights engine for failure mode identification and fixes

Eval-to-guardrail lifecycle for CI/CD rigor

Technology stack

2detected May 30, 2026

Est. monthly stack spend ~$500/mo

Framework

Framer 8a3d870

LLM

GPT-4o

Agent readiness

30/100

Early

MCP server

Public API

Webhooks

OAuth 2.0

SDKs

No public agent surfaces detected yet.

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Frequently asked

What is Galileo AI?: Galileo AI is an AI observability and evaluation platform designed to prevent AI failures. It helps teams turn offline evaluations into production guardrails that monitor live AI systems. The focus is on reliability across the eval engineering lifecycle.
What does it mean to turn offline evals into production guardrails?: Galileo AI lets teams develop evaluation metrics during testing and then apply those same checks to monitor AI in production. This means the criteria used to assess a model offline become live safeguards once it is deployed. The approach aims to catch failures before they affect users.
How does Galileo AI help with AI agents?: Galileo AI provides insights into agent behavior, which helps teams understand and debug how their AI systems act. This visibility is meant to accelerate deployments by surfacing issues early. Observability into agents supports more confident releases.
Who should use Galileo AI?: Galileo AI is aimed at teams building and deploying AI systems who need to ensure reliability and avoid failures. It serves developers and engineers responsible for AI quality in production. The platform supports the full evaluation engineering workflow.
What is eval engineering in Galileo AI's context?: Eval engineering refers to systematically building, running, and maintaining evaluations of AI systems across their lifecycle. Galileo AI frames its platform around this discipline, from creating accurate metrics to monitoring production behavior. The goal is dependable AI through rigorous, continuous evaluation.

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About Galileo AI

What Galileo AI does

Key capabilities

Who it's for

Key capabilities

Technology stack

Agent readiness

Alternatives

OpenAI

Anthropic

Safe Superintelligence

xAI

Thinking Machines Lab

Upscale AI

Frequently asked

Explore more around Galileo AI

Similar to Galileo AI

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