Compare Iris
Iris is the MCP-native agent eval standard. See how it compares to other evaluation and observability platforms — feature by feature, with no vendor lock-in.
Iris vs Langfuse
ObservabilityMCP-Native Agent Eval vs SDK-Based Tracing
Iris vs LangSmith
ObservabilityMCP-Native Eval vs LangChain Ecosystem Tracing
Iris vs Helicone
ObservabilityMCP-Native Agent Eval vs API Gateway Observability
Iris vs Braintrust
EvaluationMCP-Native Eval vs Experiment-Driven Evaluation
Iris vs Arize
ObservabilityMCP-Native Eval vs Enterprise ML Observability
Iris vs DeepEval
EvaluationMCP-Native Heuristic Eval vs LLM-as-Judge Framework
Iris vs Confident AI
EvaluationMCP-Native Eval vs Cloud Evaluation Platform
Iris vs Patronus AI
SafetyMCP-Native Eval vs Enterprise AI Safety Platform
Why Iris is different
- MCP-native — Iris runs as an MCP server. No SDK, no code changes, no vendor lock-in.
- Heuristic-first — Deterministic rules run on every output. No LLM-as-judge costs or latency.
- Quality + Safety + Cost — Three dimensions scored together. Not just quality, not just safety.
- Self-hosted — Your data stays on your machine. Free forever for the open-source core.