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Comparison · Testing

Iris vs Promptfoo

MCP-Native Agent Eval vs Config-Driven CLI Eval and Red-Teaming.

TL;DR

Iris is an MCP server your agent discovers and uses on connect — one config block, no SDK, one SQLite file, every rule's precision and recall published at iris-eval.com/proof. Promptfoo — from its own pages, read 2026-09-21: Promptfoo is an open-source CLI and library for evaluating and red-teaming LLM apps. Evals run on your machine and talk directly with the LLM, with results in a local web viewer. (source)

Iris grades what an agent did with its tools — the trace, the answer, the cost — not whether an MCP server honours its own contract; a server test harness answers that question, and Iris runs beside it. For the method, see the agent eval guide.

Feature comparison

Side by side.

Twelve features, the same twelve on every comparison. Every Promptfoo cell links the page it was read from and the date. The highlighted cells are Iris's own call on which side is stronger for a team running MCP agents — 3 to Iris, 3 to Promptfoo — not a measurement.

FeatureIrisPromptfoo
Integration methodOne block in the MCP config, no code — the agent discovers Iris and its tools on connectOpen-source CLI and Node library driven by declarative YAML test cases; also runs in CI/CD.Promptfoo's page · read 2026-09-21
Self-hostingOne process, one SQLite file; Docker image with a health checkRuns locally; a basic Docker image hosts a server that stores evals (SQLite); not recommended for production.Promptfoo's page · read 2026-09-21
Where it runsNothing in the agent's process — Iris is a separate server the agent callsNo in-app instrumentation; evals run on your machine and call the LLM directly. The Enterprise MCP Proxy is in-path.Promptfoo's page · read 2026-09-21
Evaluation21 built-in deterministic rules and 9 custom-rule types, in-process; 6 judge templates on a key you supply; every rule's precision and recall publishedDeterministic assertions plus model-graded (LLM rubric) assertions; custom JavaScript and Python assertions.Promptfoo's page · read 2026-09-21
Cost trackingPer-trace USD cost and tokens; a cost spike judged against the agent's own historyA cost assertion checks provider-reported cost against a threshold; requires the provider to return cost information.Promptfoo's page · read 2026-09-21
MCP supportProtocol-native — Iris is an MCP server with 12 tools; OTLP traces inMCP provider tests MCP servers directly; ships an MCP server exposing its eval tools; Enterprise MCP Proxy product.Promptfoo's page · read 2026-09-21
LicenseMIT, the whole packageOpen-source core is MIT licensed; Promptfoo Enterprise (hosted SaaS) and Enterprise On-Prem are commercial.Promptfoo's page · read 2026-09-21
OwnershipIndependent and founder-ledAnnounced 2026-03-09 that it agreed to be acquired by OpenAI; its about page states Promptfoo is now part of OpenAI.Promptfoo's page · read 2026-09-21
DashboardA local dashboard on its own port: traces, moments, regressions, five viewsLocal web viewer opens eval results in the browser; results can be shared to promptfoo.app or a self-hosted server.Promptfoo's page · read 2026-09-21
Framework supportAny MCP client (1 verified, 9 claimed — see /clients); OTLP/HTTP from anything elseProviders for OpenAI, Anthropic, Azure, Google, HuggingFace, local models, HTTP, custom JS/Python scripts, MCP, n8n.Promptfoo's page · read 2026-09-21
Prompt managementNot includedPrompts inline or in Git-tracked files; external stores (Langfuse, Portkey, Helicone) referenced by version or label.Promptfoo's page · read 2026-09-21
Enterprise and complianceSelf-hosted. Nothing leaves your machine unless you set IRIS_OTEL_ENDPOINT, which exports traces to the collector you name, or enable the LLM judge with your own key. No compliance certification is claimed before it is heldEnterprise tier lists SSO, granular permission profiles, RBAC and teams; site shows SOC2 and ISO 27001 badges.Promptfoo's page · read 2026-09-21

Decision guide

Which one fits your stack?

When to choose Iris

  • You are building with MCP-compatible agents and want the integration to be one config block
  • You want the evaluation to be deterministic and local — no model call, nothing leaving the machine
  • You want to read what each rule is worth before you trust it: every rule's precision and recall is published
  • You want self-hosting to be one process and one file
  • You want a fully permissive MIT license on the whole package

When to choose Promptfoo

  • You need red-teaming probes and vulnerability scanning of your LLM app alongside evals. source
  • You need declarative YAML test cases that run as a CLI step in CI/CD and compare models side-by-side. source
  • You need evals that run entirely on your machine and talk directly to the LLM, with no server. source
  • You need a proxy that governs which MCP servers your organization's applications and users may reach. source
  • You need an on-premises enterprise deployment with a dedicated runner behind your firewall. source

Sources

Every Promptfoo statement on this page was read from one of these pages on the date shown. The file behind this page is website/src/lib/compare/promptfoo.json.

Last verified: 2026-09-21. This comparison is based on publicly available documentation and may not reflect recent changes to Promptfoo. We aim to keep this page accurate and fair.

See something outdated or incorrect? Report an inaccuracy — we review and update within 48 hours.

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