What is the difference between Iris and Confident AI?

Iris is a self-hosted MCP server that evaluates agent output inline in production using deterministic rules. Confident AI is a managed cloud platform built on top of DeepEval that provides LLM-as-Judge evaluation, team dashboards, regression testing, and dataset management. Iris is free and open-source. Confident AI is a commercial SaaS platform.

Should I use Iris or Confident AI for my AI project?

Use Iris if you want self-hosted, zero-code eval running on every agent output in production with no vendor dependency. Use Confident AI if you need a managed platform with team collaboration, regression testing across experiments, and LLM-as-Judge evaluation with a visual dashboard for non-technical stakeholders.

Comparison

Iris vs Confident AI

Self-hosted inline eval vs managed cloud testing platform. Own your data or outsource the infrastructure.

TL;DR

Iris is a self-hosted MCP server that evaluates every agent output inline in production — your data stays on your machine, no API keys needed. Confident AI is the commercial cloud platform built on top of DeepEval, offering LLM-as-Judge evaluation, team dashboards, regression testing, and dataset management. If you want zero-vendor-dependency eval that runs on every output, Iris. If you need a managed platform with team collaboration and semantic evaluation, Confident AI.

For background on agent evaluation methodology, see our agent eval guide.

Feature Comparison

Side by side.

Feature	Iris	Confident AI
Deployment	Self-hosted (your infrastructure)	Managed cloud (SaaS)
Integration method	MCP config (zero code)	Python SDK + API key
Eval approach	Deterministic heuristic rules (<1ms)	LLM-as-Judge metrics (semantic)
When eval runs	Inline, every output in production	Batch experiments and CI/CD
Data ownership	100% local (SQLite, your machine)	Cloud-hosted (vendor manages)
Team collaboration	Single-user (team features on roadmap)	Multi-user dashboards, shared experiments
Regression testing	Not included	Built-in experiment comparison
Dataset management	Not included	Synthetic data generation, golden datasets
Cost tracking	Per-trace USD cost, aggregate visibility	Not a primary feature
MCP support	Protocol-native (IS an MCP server)	Not MCP-aware
Pricing	Free and open-source (MIT)	Free tier + paid plans
PII detection	Built-in (SSN, credit card, phone, email)	Via custom metrics or toxicity checks

Decision Guide

Which one fits your team?

When to choose Iris

You want to own your eval data — no cloud dependency, no vendor lock-in
You're building with MCP-compatible agents and want zero-code setup
You want inline production eval, not just pre-deployment testing
You need cost tracking and PII detection out of the box
You want fully open-source with no usage limits

When to choose Confident AI

You need team collaboration with shared dashboards and experiments
You want LLM-as-Judge evaluation for nuanced semantic scoring
You need regression testing to compare model versions
You want synthetic dataset generation for comprehensive test coverage
You prefer a managed platform without infrastructure overhead

Last verified: March 2026. This comparison is based on publicly available documentation and may not reflect recent changes to Confident AI. We aim to keep this page accurate and fair.

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