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Iris vs Promptfoo

A side-by-side comparison of Iris and Promptfoo, two Eval tools, drawn from Ignaite's continuously-verified listings.

Compared from listings verified as of

Iris

Eval

MCP-native eval and observability server for AI agents.

View Iris

Promptfoo

Eval

LLM eval CLI with rubric scoring and golden sets.

View Promptfoo

At a glance

Feature comparison of Iris and Promptfoo
AttributeIrisPromptfoo
CategoryEvalEval
Pricing (differs)FREEMIUMFREE
License (differs)Open coreOpen source
Deployment (differs)Hybrid
Platforms (differs)APICLI, macOS, Windows, Linux
Model supportBYO key / modelBYO key / model
Vendor (differs)IrisPromptfoo

The honest brief

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

Promptfoo

Define evals in plain YAML and run one goldset across models in CI — a prompt regression fails the build like any other test.

  • YAML-driven, version-controllable evals
  • Runs in CI, model-agnostic
  • Goldsets and rubric scoring
  • Also does red-teaming/security scans
  • CLI-first, less of a hosted UI
  • Teams may want managed dashboards
  • Config sprawl on large eval suites