Galileo AI
AI evaluation platform for hallucination detection and quality
About
Galileo provides AI evaluation tools that detect hallucinations, measure response quality, and monitor LLM application performance. It offers real-time guardrails and automated quality metrics for RAG and agent systems.
Details
Galileo is an AI observability and evaluation engineering platform designed to help organizations detect, diagnose, and prevent failures in large language model (LLM) applications. The platform enables teams to capture ground truth data from synthetic, development, and live production sources, including subject matter expert annotations, to create a living dataset that continuously grounds AI systems. It offers over 20 out-of-the-box evaluators for retrieval-augmented generation (RAG), agent, safety, and security use cases, and allows users to build custom evaluators to encode domain expertise. A key differentiator is the ability to distill expensive LLM-as-judge evaluators into compact, low-latency, low-cost Luna models that can monitor 100% of production traffic at significantly reduced cost. The platform provides real-time guardrails that transition offline evaluations into production safeguards. Its insights engine analyzes agent behavior to identify failure modes, surface hidden patterns, and prescribe fixes, enabling rapid debugging and faster deployment cycles. Galileo ingests signals from models, prompts, functions, context, datasets, traces, and MCP servers, and supports a range of evaluation types including hallucination detection, response quality measurement, and performance monitoring for LLM applications. The platform is positioned as an enterprise-grade solution trusted by enterprises and developers alike, with a freemium pricing model that offers a free tier for getting started. Galileo is primarily focused on text-based LLM applications, including RAG systems and agent architectures. The platform appears to be a SaaS offering, with documentation, pricing, and resources available on its website for further exploration.
Key Features
- Hallucination index
- RAG quality metrics
- Real-time guardrails
- Automated evaluation
- Custom scorers
- Production monitoring
Use Cases
- Detecting hallucinations in LLM-generated responses
- Monitoring and evaluating RAG (retrieval-augmented generation) system performance
- Assessing and improving agent behavior in AI agent systems
- Enforcing safety and security guardrails in production AI applications
- Debugging and optimizing LLM applications through failure mode analysis
- Building and maintaining custom evaluation metrics for domain-specific AI systems
Pros & Cons
Pros
- Offers a comprehensive evaluation platform with over 20 built-in evaluators covering RAG, agents, safety, and security
- Enables cost-effective production monitoring by distilling expensive evaluators into compact Luna models
- Provides real-time guardrails that can prevent failures before they impact users
- Includes an insights engine that analyzes behavior and prescribes actionable fixes for faster debugging
- Supports ground truth data capture from multiple sources, including expert annotations, for continuous improvement
- Freemium pricing model allows users to start for free and evaluate the platform before committing
Cons
- Free tier likely has usage limits or feature restrictions that should be verified on the pricing page
- Platform appears focused on text-based LLM applications and may not support image, video, or audio inputs/outputs
- Effectiveness of evaluations and guardrails depends on the quality of ground truth data and custom evaluators
- Requires integration with existing LLM applications and infrastructure, which may involve setup effort
- Pricing for premium features or higher usage tiers is not specified and should be checked on the website
Frequently Asked Questions
What types of evaluations does Galileo support?
Based on available information, Galileo offers over 20 out-of-the-box evaluators for RAG, agents, safety, and security, as well as the ability to build custom evaluators. Specific evaluation types should be verified on the platform's documentation.
Can Galileo be used for real-time monitoring?
Yes, the platform appears to provide real-time guardrails that transition offline evaluations into production safeguards, allowing for monitoring of live traffic. The exact latency and scalability should be confirmed with the product team.
Is there a free tier available?
Galileo appears to offer a freemium pricing model with a free tier for getting started. The specific limits of the free tier, such as usage caps or feature restrictions, should be checked on the pricing page.
How does Galileo reduce evaluation costs?
The platform claims to distill expensive LLM-as-judge evaluators into compact Luna models that run at lower latency and cost, enabling monitoring of 100% of traffic at 96% lower cost. This should be verified with actual usage scenarios.
Does Galileo support custom evaluation metrics?
Yes, Galileo allows users to build custom evaluators to encode domain expertise, in addition to its built-in evaluators. The process for creating custom metrics is likely detailed in the platform's documentation.
What data sources can Galileo ingest?
Based on the website, Galileo ingests signals from models, prompts, functions, context, datasets, traces, and MCP servers. It also supports ground truth data capture from synthetic, development, and live production data, including subject matter expert annotations.