Arize Phoenix vs Galileo
A side-by-side comparison of Arize Phoenix and Galileo, two Observability tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Galileo
ObservabilityEvaluation and observability for GenAI apps and agents, with inline guardrails.
View GalileoAt a glance
| Attribute | Arize Phoenix | Galileo |
|---|---|---|
| Category | Observability | Observability |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment (differs) | Hybrid | Cloud |
| Platforms (differs) | API, Web | Web, API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Arize AI | Galileo |
The honest brief
Arize Phoenix
Spins up inside a Jupyter notebook and is sharpest at RAG debugging — finding the bad chunk that poisoned retrieval.
- Source-available, runs locally
- Strong RAG/retrieval debugging
- OpenTelemetry-based tracing
- Notebook-friendly
- Less polished than hosted SaaS evals
- Production scale leans on Arize cloud
- Setup effort for full pipelines
- Smaller than LangSmith ecosystem
Galileo
Turns offline evals into real-time production guardrails powered by its own cheap Luna eval models, not an LLM judge.
- 20+ out-of-the-box evals for RAG and agents
- Inline runtime guardrails, not just offline scoring
- Own Luna models keep eval costs low
- Model-agnostic across providers
- Pricing tiers gate the production guardrails
- Proprietary eval models, not open source
- Heavier setup than a drop-in proxy