Braintrust vs Iris
A side-by-side comparison of Braintrust and Iris, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Braintrust | Iris |
|---|---|---|
| Category | Eval | Eval |
| Pricing | FREEMIUM | FREEMIUM |
| License (differs) | Proprietary | Open core |
| Deployment (differs) | Cloud | Hybrid |
| Platforms (differs) | Web, API | API |
| Model support | BYO key / model | BYO key / model |
| Vendor (differs) | Braintrust | Iris |
| Capabilities (differs) |
|
|
The honest brief
Braintrust
Eval-first: prompts are versioned objects and CI scorers block a merge when quality regresses.
- Eval workflow as the primary interface
- CI scorers block merges on regression
- Dataset versioning + OTel tracing
- Generous free tier
- Closed-source SaaS
- Self-hosting needs Enterprise contract
- Overkill for tiny single-file eval needs
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; Braintrust treats it as secondary.
- LLM observability (primary capability)
They also differ on:
- License
- Proprietary · Open core
- Deployment
- Cloud · Hybrid
- Platforms
- Web, API · API
Their capability lists differ in recorded depth — compare the full lists above before deciding.