Atla vs Galileo
A side-by-side comparison of Atla and Galileo, 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 | Atla | Galileo |
|---|---|---|
| Category (differs) | Eval | Observability |
| Pricing | FREEMIUM | FREEMIUM |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms | Web, API | Web, API |
| Model support (differs) | Self-contained (on-device) | Model-agnostic |
| Vendor (differs) | Atla | Galileo |
The honest brief
Atla
Built around its own Selene LLM-judge models instead of prompting a general model, then clusters and ranks agent failures so you fix the most impactful first.
- Auto-discovers and suggests fixes
- Open-weight Selene Mini available
- Python and TypeScript SDKs
- Integrates with OpenAI and LangChain
- Y Combinator-backed team
- Younger platform, small team
- Judge-model approach is opinionated
- Free tier capped at 300 calls/month
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