What is it

Galileo is an AI observability and evaluation platform that helps teams assess and safeguard production LLM applications. It uses proprietary small language models called Luna-2 to deliver evaluation metrics at a fraction of the cost and latency of traditional LLM-as-a-judge approaches.

What it can do

Teams can trace AI agent workflows, evaluate RAG pipelines for groundedness, set up real-time production guardrails, and monitor 100% of LLM traffic cost-effectively. It supports multimodal evaluation including images, PDFs, and audio.

Who is it for

AI engineers, ML platform teams, and enterprises running production LLM applications who need comprehensive evaluation, debugging, and runtime protection for their AI systems.

Key Features

Luna-2 Evaluation Engine – Proprietary Small Language Model Assessors

Galileo's Luna-2 engine consists of fine-tuned Llama 3B/8B variants purpose-built for assessment tasks. It delivers sub-200ms latency even when running 10 to 20 concurrent checks, costs approximately $0.02 per 1M tokens compared to around $5.00 for GPT-4o class evaluators, and achieves 0.95 AUROC with 95% F1 accuracy across evaluation tasks.

RAG-Specific Metrics – Chunk-Level Groundedness Analysis

Built-in metrics designed specifically for Retrieval-Augmented Generation pipelines, including Context Adherence and Chunk Utilization. These metrics automatically analyze the alignment between retrieved chunks and generated answers, helping teams diagnose the root causes of RAG failures at the chunk level.

Agent Workflow Tracing – Multi-Step Execution Reconstruction

Comprehensive tracing of AI agent multi-step workflows with granular spans and token usage analysis. The platform provides three specialized views: Graph View for decision paths, Trace View for step-by-step execution, and Message View for conversational interactions.

Real-Time Guardrails – Production Runtime Protection

Enterprise-grade guardrails that intercept unsafe outputs at serve time before they reach end users. The system blocks prompt injections, PII leaks, hallucinations, and policy violations inline, providing active protection rather than passive monitoring alone.

Use Cases

RAG pipeline quality assurance

Teams building retrieval-augmented generation applications use Galileo to automatically detect context adherence failures and chunk utilization issues, ensuring retrieved information accurately supports generated answers and reducing hallucination rates.

AI agent reliability testing

Engineering teams trace multi-step agent workflows to identify reasoning errors, tool selection mistakes, and action completion failures before deploying to production, preventing costly agent malfunctions in live environments.

Production LLM monitoring

Operations teams monitor 100 percent of LLM traffic using Luna-2's cost-efficient evaluation architecture, catching hallucinations, bias, and toxicity in real time without incurring the prohibitive compute costs of traditional LLM-as-a-judge approaches.

Enterprise compliance and guardrails

Organizations in regulated industries deploy real-time guardrails to block PII leaks, prompt injections, and policy violations before content reaches users, maintaining compliance while serving AI-powered customer-facing applications.

Pricing plans

Frequently Asked Questions

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