DeepEval vs Ragas
A side-by-side comparison of DeepEval and Ragas, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | Ragas |
|---|---|---|
| Category | Eval | Eval |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Open core | Open source |
| Deployment (differs) | Hybrid | — |
| Platforms | CLI, API | CLI, API |
| Model support | BYO key / model | BYO key / model |
| Vendor (differs) | Confident AI | Exploding Gradients |
| Capabilities (differs) |
|
|
The honest brief
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
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
When to pick which
Both cover LLM evaluation.
Pick DeepEval if you need LLM observability.
- LLM observability (secondary capability)
They also differ on:
- Pricing
- FREEMIUM · FREE
- License
- Open core · Open source