Comparison

Iris vs Helicone

MCP-native, zero-code observability vs proxy-based cost analytics and AI gateway. Two different architectures for monitoring your AI stack.

TL;DR

Iris is an MCP server your agent discovers and uses automatically — zero code changes, zero SDK imports, one SQLite file for storage. Helicone is a proxy-based AI gateway and observability platform that excels at cost analytics, request caching, and multi-provider routing. If you're building with MCP-compatible agents and want the simplest possible setup with built-in eval, Iris gets you there in 60 seconds. If you need deep cost analytics, semantic caching, or a unified gateway across many LLM providers, Helicone is the stronger choice.

Feature Comparison

Side by side.

Feature	Iris	Helicone
Integration method	MCP config (zero code)	Proxy-based (change base URL + add header)
Self-hosting complexity	Single SQLite file	Docker container + ClickHouse + PostgreSQL
Performance overhead	Zero (no SDK in hot path)	1–5 ms proxy latency (Rust gateway)
Eval rules	12 built-in + 8 custom types, heuristic (<1ms)	Evaluators via dashboard, LLM-based scoring
Cost tracking	Per-trace USD cost	Multi-dimension cost analytics (user, model, session, geography)
MCP support	Protocol-native (IS an MCP server)	MCP server for data access only
License	MIT (fully permissive)	Apache 2.0 (permissive)
Pricing	Free + Cloud waitlist	Free (10k req/mo), Pro $20/seat/mo, Enterprise custom
Caching	Not included	Semantic caching (up to 95% cost reduction on repeated queries)
Gateway features	Observability-focused	Rate limiting, retries, fallbacks, load balancing across 100+ providers
Data retention	Unlimited (your SQLite, your storage)	1 month (free) / 3 months (Pro) / lifetime (Enterprise)
Enterprise features	Roadmap (v0.5)	SOC 2, GDPR, rate limiting, Helm charts

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 proxy rewiring, no URL changes
You want simple self-hosting — one binary, one SQLite file
You want heuristic eval rules that run locally in under 1 ms
You want unlimited data retention without tier-based limits

When to choose Helicone

You need deep cost analytics — per-user, per-model, per-session spend breakdowns
You want semantic caching to cut costs on repeated queries
You need a full AI gateway with retries, fallbacks, and rate limiting
You need enterprise compliance today (SOC 2, GDPR)
You want a unified proxy layer across 100+ LLM providers

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

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Iris vs Helicone

Side by side.

Which one fits your stack?

When to choose Iris

When to choose Helicone

Ready to see what your agents are doing?