What an AI gateway is
An AI gateway is a control point that sits between your applications and the models they call, whether those models are hosted by a third party or run in your own environment. Every prompt going out and every response coming back passes through it. That single chokepoint is where enterprise governance becomes real: instead of trusting each application to behave, you enforce policy once, centrally, at the moment of the call. The gateway is to AI traffic what an API gateway is to service traffic, with one difference that matters. It understands the content of the request, not just its route, so it can redact, classify, and block based on what is actually being sent to the model.
What it does on every call
Four jobs run at the gateway. It intercepts, so no AI request reaches a model without passing the control layer. It redacts, stripping or masking sensitive data such as customer records, secrets, and regulated personal data before the prompt leaves your boundary. It enforces, checking each request against policy: which users and applications may call which models, which data classes are forbidden, which actions require approval, and failing closed when a request breaks the rules. And it observes, writing an immutable log of every interaction so you can answer what happened, who triggered it, and what the model returned. These run in real time, in line with the request, not as a report produced afterward.
Why a gateway beats per-app guardrails
Building guardrails inside each application means every team reimplements governance, and your security posture is only as strong as the weakest one. New tools appear faster than reviews can keep up, and shadow AI fills the gap. A gateway inverts that. Policy lives in one place, applies to every application by default, and updates everywhere at once when a rule changes. A new app inherits governance the day it is connected. For a regulated enterprise, that is the difference between a control you can evidence to an auditor and a patchwork you have to defend tool by tool.
What to look for when evaluating one
Ask whether it acts at runtime or only documents after the fact. A gateway that enforces at the inference call prevents bad outcomes; a registry that records risk afterward leaves the exposure live. Confirm it is model agnostic, so you are not locked to one vendor and can route across hosted and self-hosted models under one policy. Check that redaction happens before data leaves your boundary, not after. Confirm it fails closed, so a policy gap blocks the request rather than letting it through. And confirm the audit log is complete and tamper evident, because that record is what turns AI usage from a liability into something you can stand behind.
Frequently asked questions
What is an AI gateway?
A runtime control point between your applications and the AI models they call. It intercepts every prompt and response to redact sensitive data, enforce access and usage policy, and log every interaction for audit.
How is an AI gateway different from an API gateway?
An API gateway routes and rate-limits traffic by endpoint. An AI gateway understands the content of each request, so it can redact regulated data, classify intent, and enforce policy based on what is being sent to the model.
Why do enterprises need an AI gateway?
It centralizes governance at one control point so policy applies to every AI application by default, including new ones, instead of relying on each team to build its own guardrails. That is what makes AI usage auditable and safe to scale.