Atla vs Ragas
A side-by-side comparison of Atla and Ragas, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Atla | Ragas |
|---|---|---|
| Category | Eval | Eval |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Proprietary | Open source |
| Deployment (differs) | Cloud | — |
| Platforms (differs) | Web, API | CLI, API |
| Model support (differs) | Self-contained (on-device) | BYO key / model |
| Vendor (differs) | Atla | Exploding Gradients |
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
Ragas
Popularized reference-free RAG metrics — faithfulness, context precision — scored by an LLM judge, so you evaluate without gold answers.
- Faithfulness & relevancy metrics
- Knowledge-graph synthetic test sets
- LLM-as-judge scoring
- Integrates LangChain, LlamaIndex, CI
- LLM-judge scores add cost/variance
- Python library, no hosted UI
- Focused on RAG, narrower scope