Comparison

Iris vs Langfuse

MCP-native, zero-code observability vs SDK-based instrumentation. Two fundamentally different approaches to understanding your AI agents.

TL;DR

Iris is an MCP server your agent discovers and uses automatically — zero code changes, zero SDK imports, one SQLite file for storage. Langfuse is a full-featured LLM observability platform with SDK integrations for 20+ frameworks, prompt management, and enterprise compliance. If you're building with MCP-compatible agents and want the simplest possible setup, Iris gets you there in 60 seconds. If you need broad framework support, prompt versioning, or enterprise certifications today, Langfuse is the more mature choice.

Feature Comparison

Side by side.

Feature	Iris	Langfuse
Integration method	MCP config (zero code)	SDK imports + @observe decorators
Self-hosting complexity	Single SQLite file	PostgreSQL + ClickHouse + Redis + S3 + 2 containers
Performance overhead	Zero (no SDK in hot path)	0.1 – multiple seconds latency reported #6331
Eval rules	12 built-in + 8 custom types, heuristic (<1ms)	LLM-as-Judge (powerful but slow / costly)
Cost tracking	Per-trace USD cost	Token / cost per user, session, model
MCP support	Protocol-native (IS an MCP server)	MCP server for prompt management only
License	MIT (fully permissive)	MIT core + commercial enterprise modules
Independence	Independent, founder-led	Acquired by ClickHouse (Jan 2026)
Dashboard	Real-time dark-mode UI	Customizable multi-dimension dashboards
Framework support	Any MCP-compatible agent	20+ framework integrations
Prompt management	Not included	Full versioned prompt management
Enterprise features	Roadmap (v0.5)	SOC 2, ISO 27001, HIPAA, SCIM

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 decorators
You want simple self-hosting — one binary, one SQLite file
You want fully permissive MIT licensing
You value independence from corporate ownership

When to choose Langfuse

You need prompt management and versioning
You need LLM-as-Judge evaluation (semantic, not just heuristic)
You need enterprise compliance today (SOC 2, HIPAA)
You're using non-MCP frameworks and need broad SDK support
You need multi-dimension dashboard slicing (user, session, geography)

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

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