Guide
AI gateways vs governance control planes
Gateways standardize model access and request guardrails. Governance control planes govern workflow decisions with approvals and exportable evidence packs.
For ML platform teams deciding between a gateway/proxy layer and a governance layer (or both) for regulated workflows.
Last updated: Dec 17, 2025 · Version v1.0 · Not legal advice.
TL;DR
The quick distinction
- Gateways govern requests: routing, provider abstraction, rate limits, request guardrails.
- Control planes govern decisions: approvals, overrides, escalation, and evidence about what happened.
- In regulated workflows you often need both: request safety + decision governance + exports.
Gateway
When a gateway is enough
- You mainly need centralized provider access, spend controls, and request-level guardrails.
- Your audit requirements do not include human approvals and decision evidence packs.
Control plane
When you need a control plane
- You must prove who approved/overrode a workflow decision and what context they saw.
- You need policy-as-code checkpoints that gate high-risk actions and record enforcement.
- You must export a verifiable evidence pack (manifest + checksums) for auditors.
References