Galileo AI

Galileo is an AI observability, evaluation, and production-guardrail platform designed for teams building and operating generative AI applications and AI agents. It helps engineering and AI teams measure model and application quality, investigate failures, evaluate agent behavior, and turn evaluation results into safeguards that can operate in production.

The platform covers the AI reliability lifecycle from development to production. Teams can create evaluation datasets from synthetic, development, and live production data, incorporate subject-matter-expert annotations, run evaluations, analyze traces, identify failure patterns, and use evaluation results to establish production guardrails.

A major part of Galileo’s platform is its support for GenAI and agent evaluation. The platform provides more than 20 out-of-the-box evaluations covering areas such as RAG, agents, safety, and security. Teams can also create custom evaluators that encode their own business or domain requirements.

Galileo’s Insights Engine is designed to help developers understand why an AI system is failing rather than simply showing that a failure occurred. It analyzes agent behavior and signals across models, prompts, functions, context, datasets, traces, and other components to surface patterns and potential fixes. This can be particularly valuable for applications involving multi-step agents, where failures may originate from tool selection, context, prompts, or intermediate steps.

Another differentiating component is Luna, Galileo’s family of specialized evaluation models. Galileo can distill expensive LLM-as-a-judge evaluations into smaller Luna models for lower-cost and lower-latency evaluation. The company positions this capability as a way to extend evaluation from occasional testing into continuous production monitoring.

Galileo also connects offline evaluation with runtime protection. Evaluation scores can be used to influence agent actions, tool access, and escalation paths, allowing teams to create guardrail policies that operate as part of production AI workflows. This creates a workflow in which teams can test an AI application before deployment and then continue monitoring and protecting it after deployment.

For observability, Galileo supports traces and AI-specific telemetry. Its integrations include OpenTelemetry and OpenInference, allowing teams to capture information from AI applications using established observability standards. Galileo also provides direct integrations and wrappers for popular AI and agent frameworks.

The platform is model-agnostic and works with major providers including OpenAI, Azure OpenAI, Anthropic, and LLaMA. Its documentation also supports integrations with frameworks such as LangChain, LangGraph, CrewAI, Google ADK, OpenAI SDK, OpenAI Agents SDK, A2A, Pydantic AI, Strands Agents, and Vercel AI SDK.

Galileo is designed primarily for developers, AI engineers, data scientists, product teams, and organizations operating AI applications at scale. Enterprise deployments can be hosted as SaaS, in a virtual private cloud, or on-premises, providing additional flexibility for organizations with security, privacy, or regulatory requirements.

Pricing

Galileo currently uses a freemium and usage-based subscription model with three main plans:

Free — $0/month: Includes 5,000 traces per month, unlimited users, and unlimited custom evaluations.
Pro — $100/month when billed annually: Includes 50,000 traces per month, standard RBAC, advanced analytics and insights, and dedicated Slack support. Pricing scales according to trace volume.
Enterprise — Custom pricing: Includes unlimited traces, custom rate limits, hosted/VPC/on-premises deployment, enterprise RBAC and SSO, dedicated customer success management, real-time guardrails, 24/7 support, dedicated inference servers, and forward-deployed engineering support.

The Free plan is available without a separate time-limited trial. Galileo’s pricing is primarily based on usage and the volume of traces handled by the platform.

Review

Galileo is a strong platform for organizations that need more than basic logging for their AI applications. Its main strength is the combination of evaluation, observability, agent monitoring, and runtime guardrails in a single workflow.

The platform is particularly compelling for teams working with RAG systems and multi-step AI agents because it provides specialized evaluations and tools for investigating the causes of poor AI behavior. Its OpenTelemetry/OpenInference support also makes it easier to incorporate Galileo into existing observability architectures.

Luna provides another notable advantage by targeting the cost and latency challenges associated with continuously evaluating production AI systems. Enterprise deployment options, RBAC, SSO, and on-premises support make Galileo suitable for organizations with more demanding security and governance requirements.

The main drawback is its specialized focus. Galileo is not a general-purpose AI assistant, coding tool, chatbot builder, or content-generation platform. Organizations that simply need basic application monitoring may also find its evaluation and agent-reliability capabilities more extensive than necessary.

For AI engineering teams building production-grade GenAI applications and agents, Galileo provides a comprehensive reliability layer spanning testing, evaluation, observability, debugging, and runtime protection.