Galileo AI vs Phoenix: Observability, Prompts & Evals Comparison
Compare Galileo AI and Phoenix side by side. Both are tools in the Observability, Prompts & Evals category.
The short answer
Choose Galileo AI if generous free tier with 5,000 traces/month including Agent Reliability Platform.
Choose Phoenix if open-source with active development by Arize.
Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Freemium | Open Source |
| Best For | AI teams who need to measure and improve the quality of their LLM outputs | Engineering teams building agent and RAG systems who want OpenTelemetry-native observability with both self-hosted and managed options |
| Website | rungalileo.io | phoenix.arize.com |
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Pros & Cons: Galileo AI vs Phoenix
- +Generous free tier with 5,000 traces/month including Agent Reliability Platform
- +Recognized as Gartner Cool Vendor demonstrating industry validation
- +Research-backed metrics providing scientific rigor to evaluations
- +Real-time observability with automated drift detection and alerting
- +Established team of 142 employees providing solid support capacity
- −Limited public information about advanced features and capabilities
- −Pro plan pricing jumps from free to $100/month with potential gaps for small teams
- −Less community-driven compared to open-source alternatives like Langfuse
- +Open-source with active development by Arize
- +OpenTelemetry-native (no proprietary trace format lock-in)
- +Strong evaluator library out of the box
- +Both self-hosted and managed cloud options available
- +Upgrade path to full Arize enterprise platform
- −Smaller community than Langfuse for open-source-first teams
- −Cloud version's enterprise pricing is contact-sales only
- −UI and feature set tilt toward ML engineers more than application developers
When to Choose Galileo AI vs Phoenix
- Monitoring LLM output quality
- Detecting and preventing hallucinations
- Evaluating RAG pipeline accuracy
- OpenTelemetry-native LLM observability
- Hallucination detection for RAG
- Experiment tracking and golden-set evaluation
About Galileo AI
Galileo is an AI observability and evaluation platform designed to provide AI reliability for teams across the entire development lifecycle. The platform offers real-time observability that continuously evaluates systems in production, sending alerts if something goes wrong or if interactions drift from training data. Galileo provides powerful, research-backed metrics and evaluation-powered development workflows to help teams build, scale, monitor, and protect AI applications in real-time. The platform is recognized as a Gartner Cool Vendor and serves as a comprehensive solution for AI teams looking to ensure reliability and performance of their LLM applications. With the Agent Reliability Platform available as part of their free tier, Galileo makes advanced AI observability accessible to teams of all sizes. The platform emphasizes scalability, security, and premium support for enterprise customers while maintaining an approachable entry point through their generous free tier.
About Phoenix
Phoenix is the open-source observability and evaluation platform built by Arize AI for LLM and agent applications. It is OpenTelemetry-native, which means traces written through Phoenix can flow into any OTel-compatible backend in addition to Phoenix's own UI. The platform includes built-in evaluators for hallucination detection, retrieval relevance, and QA correctness, plus dataset management and prompt playground features. Phoenix can be deployed via Docker for self-hosting or used in Arize's managed cloud. The open-source core makes it attractive to teams that want to inspect and customize the observability layer, while the integration with the full Arize platform provides an upgrade path for organizations that need enterprise features like RBAC, SSO, and SLA-backed support.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
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