What is the difference between Iris and Arize?

Iris is an MCP-native agent eval tool that needs no SDK. Add the config block, restart your client, and every session lists Iris's tools on connect. Iris never intercepts: it runs when your agent calls one of its tools, when a host hook or `iris-eval ingest` hands it a trace, or when you POST one to its HTTP API. Arize AI is an enterprise ML observability platform with Phoenix (open-source) for tracing and Arize AX (cloud) for advanced monitoring with embedding drift detection. Iris focuses on z

Is Iris better than Arize for MCP agent evaluation?

For MCP-compatible agents, Iris provides protocol-native integration with zero OpenTelemetry setup and 60-second onboarding. Arize requires OpenTelemetry SDK instrumentation but offers advanced embedding drift detection, LLM-as-Judge evaluation, and enterprise-grade features like RBAC. The best choice depends on whether you need lightweight MCP-native observability or a full enterprise ML monitoring platform.

Iris vs Arize AI — MCP-Native Agent Eval vs Enterprise ML Platform

TL;DR

Iris is an MCP server your agent discovers and uses automatically — zero code changes, zero SDK imports, one SQLite file for storage. Arize AI is a comprehensive ML observability platform with Phoenix (open-source) for self-hosted tracing and evaluation, plus Arize AX (cloud) for enterprise-grade monitoring with embedding drift detection, RBAC, and advanced analytics. If you're building with MCP-compatible agents and want the simplest possible setup, Iris gets you there in 60 seconds. If you need enterprise ML observability with drift detection, broad framework instrumentation, or advanced evaluation workflows, Arize is the more comprehensive platform.

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

Feature Comparison

Side by side.

Feature	Iris	Arize AI
Integration method	MCP config (zero code)	OpenTelemetry SDK + auto-instrumentation
Self-hosting complexity	Single SQLite file	Phoenix: pip install + PostgreSQL (production)
Performance overhead	Zero (no SDK in hot path)	OpenTelemetry collector + SDK in application
Eval capabilities	20 built-in heuristic rules + 9 custom-rule types (in-process, no provider call)	LLM-as-Judge, custom evaluators, agent eval templates
Cost tracking	Per-trace USD cost	Token and cost tracking across models
MCP support	Protocol-native (IS an MCP server)	Phoenix MCP server (prompts, traces, datasets, experiments, SQL); no tool runs an eval
License	MIT (fully permissive)	Phoenix: Elastic License 2.0 (ELv2)
Embeddings & drift	Not included	Advanced embedding drift detection across NLP, CV, multi-modal
Dashboard	Real-time dark-mode UI	Full-featured dashboards, Prompt IDE, Alyx AI assistant
Framework support	Any MCP-compatible agent	20+ frameworks (OpenAI, LangGraph, CrewAI, LlamaIndex, DSPy, etc.)
Prompt management	Not included	Prompt IDE with versioning and optimization
Enterprise features	Not offered — self-hosted; no compliance certification claimed before it is held	RBAC, SOC 2, online evals, Alyx assistant
Pricing	Free and open-source	Phoenix free; AX Free $0 / Pro $50/mo / Enterprise custom
Setup time	60 seconds, one config line	Minutes to hours depending on deployment

Decision Guide

Which one fits your stack?

When to choose Iris

You're building with MCP-compatible agents (Claude Desktop, Cursor, Windsurf)
You want zero-code integration — no SDK imports, no OpenTelemetry setup
You want simple self-hosting — one binary, one SQLite file
You want fully permissive MIT licensing (not Elastic License 2.0)
You need lightweight, focused MCP agent observability without enterprise complexity

When to choose Arize AI

You need advanced embedding drift detection across NLP, CV, or multi-modal models
You need LLM-as-Judge evaluation with agent eval templates
You need enterprise compliance and RBAC today
You're using non-MCP frameworks and need broad OpenTelemetry-based instrumentation
You need a full Prompt IDE with versioning and automated optimization
You're monitoring traditional ML models alongside LLM applications

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

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

Iris vs Arize AI

Side by side.

Which one fits your stack?

When to choose Iris

When to choose Arize AI

Ready to see what your agents are doing?