DeepEval vs Evidently AI
A side-by-side comparison of DeepEval and Evidently AI, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | Evidently AI |
|---|---|---|
| Category (differs) | Eval | Observability |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | CLI, API | Web, API |
| Model support (differs) | BYO key / model | Model-agnostic |
| Vendor (differs) | Confident AI | Evidently AI |
| 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
Evidently AI
One library spanning classic ML monitoring and LLM/RAG evals — 100+ metrics from data drift to hallucination — with an optional cloud.
- Open source (Apache-2.0), self-hostable
- Covers both ML and LLM evaluation
- Built-in metrics and presets
- LLM-as-judge plus drift detection
- Optional hosted cloud with free tier
- Python-library learning curve
- Less agent-trace-centric than rivals
- Cloud features gated to paid tiers
- Reports can get heavy at scale
When to pick which
Both cover LLM evaluation and LLM observability.
Pick Evidently AI if you need Red-teaming.
- Red-teaming (secondary capability)
Evidently AI leans on LLM observability as a headline capability; DeepEval treats it as secondary.
- LLM observability (primary capability)
They also differ on:
- Platforms
- CLI, API · Web, API