Galileo

Galileo provides AI teams with end‑to‑end evaluation, observability, and real‑time guardrails to build and monitor trustworthy LLM‑based apps and agents.

Description

Galileo offers an evaluation intelligence platform that enables teams to test, monitor, and secure generative AI and agentic systems throughout development and production. It delivers agent‑specific metrics (e.g. tool selection quality, action completion), customizable human review workflows, multi‑span tracing, dashboards, and integrations with common tooling. AI engineering and ops teams across industries such as edtech, fintech, telecom, and enterprise communications use Galileo to validate prompts, identify failures, monitor at scale, and enforce guardrails in real time.

Customers

• Cisco
• Magid
• Satisfi Labs
• Clearwater Analytics

What Problem Does Galileo Solve?

Complex AI and agent workflows often fail unpredictably in production—leading to hallucinations, wrong tool calls, and inconsistent behavior—which slows innovation and creates compliance or user trust issues. Galileo fixes this by embedding evaluation and monitoring into every stage, surfacing failure patterns, and enabling proactive intervention and real‑time response.

Pros

• Agent-Centric Evaluation: Provides detailed insights into agent behavior, tool use, and reasoning, enhancing debugging and iteration speed.
• Production-Grade Observability: Enables real-time tracking of LLM agents with latency, success, and failure metrics across workflows.
• Broad LLM Compatibility: Works across foundation models and frameworks, offering flexibility for enterprises building agentic systems.

Cons

• Evaluation Complexity: Setting up end‑to‑end evaluation pipelines, defining custom metrics, and designing structured prompts requires expertise in MLOps and AI QA.
• Narrow Focus Beyond GenAI: Platform is optimized for generative AI and agent reliability use cases, offering limited support for non‑LLM models or broader ML contexts.
• Limited Real-Time Capabilities: Galileo primarily focuses on evaluation during model development or fine-tuning stages, with less emphasis on live production monitoring, making it less suitable for use cases needing real-time feedback or in-flight model corrections.

Investors

• Databricks Ventures
• ServiceNow Ventures
• Citi Ventures
• Scale Venture Partners
• Battery Ventures

Last updated: October 1, 2025

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