All New: Evaluations for RAG & Chain applications

Build and Evaluate Generative AI Apps Faster

Welcome to Galileo: Your Complete Solution for Generative AI Evaluation, Experimentation, and Observability

Meet Galileo

An end-to-end platform for GenAI evaluation, experimentation, and observability


Accelerate GenAI System Evaluations

Stop experimenting in notebooks and spreadsheets. Instead leverage powerful metrics and build GenAI Systems that just work.

  • Collaboratively build and test models, RAG systems, Prompts and more
  • Evaluate inputs and output using powerful metrics
  • Store, track and compare experiments over time
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Monitor AI System Outputs in Real-Time

Rather than reacting when its too late, proactively detect hallucinations in production and instantly drive effective root-cause analysis.

  • Monitor cost, latency, hallucinations, and more
  • Define governance and security guardrails
  • Get proactive alerts and notifications
Learn more


Real-Time Request and Response Interception

Proactively protect your users from harmful responses, while also protecting your AI from malicious users.

  • Flexible API to build, enforce and edit guardrail logic
  • Easily apply guardrails to your GenAI applications
  • Built-in actionability upon rule breakage

Evaluation Powered by Research-Backed Metrics

Boost productivity with proprietary guardrail metrics developed by Galileo Research, or easily use your own custom metrics.

Learn more about our Guardrail Metrics

Designed for

The AI landscape changes quickly. Galileo is designed to easily connect with your stack.

Designed for Data Scientists

Get started in minutes

Use Galileo wherever you work

pip install

Built for the Enterprise

Keep your data and your customers safe. Galileo has been designed to help organizations of any size deploy safe and trustworthy AI applications.



Deployed in your private cloud so your data never leaves



Adhere to compliance frameworks, risk mitigation checks and company policies



SOC2 Compliant. HIPAA coming soon!

What people are saying

Leaders and data scientists love Galileo

“The reality for my team is that we often get more performance from fixing data than we do from tuning models. Galileo provides a wonderfully useful toolset for quickly and painlessly cleaning our training data.”

Gabor Angeli

Gabor Angeli

Head of conversational AI, Square

"Today's ML teams are mostly fixing issues with the ML data. This consumes most of their time, is slow, manual, hard and lacks dedicated tooling. Galileo finally provides that tooling."

Anthony Goldbloom

Anthony Goldbloom

CEO, Kaggle

There is a strong need for an evaluation toolchain across prompting, fine-tuning and production monitoring to proactively mitigate hallucinations. Galileo's LLM Studio offers exactly that toolchain. Highly recommend it to all LLM builders!

Waseem Alshikh

Waseem Alshikh

Co-founder and CTO

“Galileo solves the most time consuming and error prone aspect of any iterative ML model development – finding the data blindspots before they blow up in production. Galileo provides immediate visibility and never before seen intelligent insights that flips a labor intensive duplicative process to a low effort methodical workflow for ML practitioners”

Archi Mitra

Archi Mitra

Director of ML, Buzzfeed

“Today, 80% of ML teams' time is spent on data. Most teams lack the right tooling for handling, debugging, and programming large scale datasets, especially for unstructured data. The tooling from Galileo are very promising for solving the problem for ML teams. You have to try it out for yourself!”

Will Lu

Will Lu

Senior Engineering Manager, Google AI

"The best way to improve your ML model's accuracy is almost always to fix problems in your training data, but traditionally that's been a slow, manual, and frustrating process. With Galileo's tools you can find and fix dataset problems quickly and easily, and I highly recommend them to any ML professional."

Pete Warden

Pete Warden

Co-creator of Tensorflow

"Galileo's an insightful tool that helps understand how unstructured data contributes to the performance of a model. This helps ML engineers massively speed up their training iterations in their journey of improving model performance, in a guided and interactive way."

Eric Chen

Eric Chen

Senior Eng Manager, Uber AI (Co-creator of Michelangelo)

Ready to get started?

  • Detect model hallucinations
    Detect model hallucinations
  • Find the best prompt
    Find the best prompt
  • Inspect data errors while fine-tuning
    Inspect data errors while fine-tuning
  • Works with popular tools and models
    Works with popular tools and models

Working with Natural Language Processing?

Read about Galileo’s NLP Studio

Natural Language Processing

Natural Language Processing

Learn more