What is
Galileo
?
Most platform initiatives fail the same way: they start too big, get funded like projects instead of products, build for too long without testing with users, and confuse renaming an infrastructure team with building a platform team. Instead, start small, learn fast and scale deliberately.
At a glance
Attribute | Details |
|---|---|
Primary use case | Evaluate and monitor AI agents and convert offline evaluations into production guardrails |
Deployment type | SaaS, Virtual Private Cloud, and on-premises |
Pricing model | Free tier available; paid pricing not verified |
API compatibility | Model Context Protocol (MCP) |
Supported integrations | NVIDIA NeMo, NVIDIA NeMo Guardrails, NVIDIA NIM |
Source type | Dual |
Cloud provider | Cloud Agnostic |
Ideal team size | Small teams to enterprise AI teams |
Required skills | GenAI application development, evaluation dataset and metric design, application integration, production monitoring |
AEP branch | Agent Infrastructure |
AEP category | Harness |
AEP subcategory | Evaluation |
AEP detail | Agent-output Evals |
Profile
Agent-output Evals is Galileo's most specific AEP level. Galileo is an AI observability and evaluation platform for building and operating generative AI applications and agents. It supports evaluation workflows from development through production monitoring, with more than 20 out-of-the-box evaluations for RAG, agents, safety, and security. Teams can also build custom evaluators and use feedback to tune metrics.
Galileo connects pre-production evaluations with production guardrails. Its platform can use evaluation scores to control agent actions, tool access, and escalation paths, and offers SaaS, Virtual Private Cloud, and on-premises deployment options.
Focus
Galileo serves developers and AI teams, from small teams to enterprise AI teams, that need to measure and improve AI-agent behavior. It helps teams find failure modes, debug agent behavior, evaluate outputs, and apply production guardrails rather than relying on separate systems for offline testing and online safety.
Background
Galileo's website states that the company is now part of Cisco. The supplied company-background research does not verify a founding date or funding history. A company-background search result describes Galileo as launching Agent Control, an open-source control plane for AI agents.
Features
Out-of-the-box and custom evaluations
Teams can evaluate RAG, agent, safety, and security behavior using more than 20 out-of-the-box evaluations, then build custom evaluators to incorporate domain expertise. Galileo says its metrics can be tuned using live feedback.
Evaluation-to-guardrail workflow
Teams can carry evaluation results into production governance, using them to control agent actions, tool access, and escalation paths. Galileo describes this workflow as bringing unit-testing and CI/CD rigor into AI development without glue code.
Production monitoring with Luna models
Teams can use distilled Luna models to monitor production traffic at lower cost. Galileo states that these models can monitor 100% of traffic at 96% lower cost.
Agent insights and debugging
Teams can analyze agent behavior to identify failure modes, uncover patterns, and surface suggested fixes. The insights engine analyzes signals across models, prompts, functions, context, datasets, and traces.
Ground-truth dataset development
Teams can build datasets from synthetic, development, and live production data and incorporate subject-matter-expert annotations. Galileo presents these datasets as a continuing source of ground truth for AI systems.
Pros and cons
Pros
Offers more than 20 out-of-the-box evaluations spanning RAG, agents, safety, and security, with the option to create custom evaluators.
Connects offline evaluations to production guardrails that can govern agent actions, tool access, and escalation paths.
Supports SaaS, Virtual Private Cloud, and on-premises deployment.
Galileo states that distilled Luna models can monitor 100% of traffic at 96% lower cost.
Cons
Building custom evaluators requires teams to encode domain expertise and design evaluation datasets and metrics.
Paid pricing is not verified in the supplied research, so costs beyond the available free tier cannot be assessed from these materials.
FAQs
Galileo FAQ
How does Galileo turn evaluations into production guardrails?
Galileo uses evaluation scores to govern production agent actions, tool access, and escalation paths, connecting pre-production evaluations with production guardrails.
What integrations does Galileo support?
Galileo lists NVIDIA NeMo, NVIDIA NeMo Guardrails, and NVIDIA NIM as supported integrations.
Is Galileo free to use?
Galileo has a free tier, but paid pricing is not verified in the supplied research.
What skills are needed to use Galileo?
Using Galileo requires GenAI application development, evaluation dataset and metric design, application integration, and production monitoring skills.
What are Galileo’s notable limitations?
Galileo’s custom evaluators require teams to encode domain expertise and design evaluation datasets and metrics; paid pricing is not verified in the supplied research.
