Atla vs DeepEval
A side-by-side comparison of Atla and DeepEval, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Atla | DeepEval |
|---|---|---|
| Category | Eval | Eval |
| Pricing | FREEMIUM | FREEMIUM |
| License (differs) | Proprietary | Open core |
| Deployment (differs) | Cloud | Hybrid |
| Platforms (differs) | Web, API | CLI, API |
| Model support (differs) | Self-contained (on-device) | BYO key / model |
| Vendor (differs) | Atla | Confident AI |
| Capabilities |
|
|
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
DeepEval
Write LLM evals as Pytest-style assertions and run them in CI, backed by 50+ metrics across RAG, agents, and safety.
- Assertions run in your CI pipeline
- Metrics for RAG, agents, and safety
- Bring any judge model (BYO key)
- Integrates LangChain/CrewAI/OpenAI
- LLM-as-judge adds cost
- Dashboards need paid Confident AI
- Judge metrics can be noisy
When to pick which
Atla and DeepEval cover the same capabilities. They differ on license, deployment, platforms, and model support:
- License
- Proprietary · Open core
- Deployment
- Cloud · Hybrid
- Platforms
- Web, API · CLI, API
- Model support
- Self-contained (on-device) · BYO key / model