DeepEval vs Iris
A side-by-side comparison of DeepEval and Iris, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | Iris |
|---|---|---|
| Category | Eval | Eval |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | CLI, API | API |
| Model support | BYO key / model | BYO key / model |
| Vendor (differs) | Confident AI | Iris |
| 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
Iris
MCP-native: every output through the protocol is scored automatically with no SDK or instrumentation, rather than wiring evals into your code.
- No SDK or instrumentation to add
- Free self-host, free cloud tier
- Trace logging and LLM-as-judge scoring
- PII, injection, and cost checks
- Newer, niche MCP-focused tool
- Best fit for MCP-based agents
- Smaller ecosystem than SDK evals
When to pick which
Iris leans on LLM observability as a headline capability; DeepEval treats it as secondary.
- LLM observability (primary capability)
They also differ on:
- Platforms
- CLI, API · API
Their capability lists differ in recorded depth — compare the full lists above before deciding.