AI Gateway

An AI gateway is a central technical layer between internal applications and one or more AI providers.

Acting as a proxy or API layer, it routes requests, manages access, logs usage and applies defined rules before data is sent to an external service.

What an AI gateway does

A gateway can centrally manage access keys, log requests and responses, filter sensitive data according to defined rules, and provide visibility into usage and cost across teams.

Depending on the product, it may also route requests between models or providers based on availability, suitability or cost.

Why gateways make governance practical

Direct integrations across several teams often lead to different credentials, filtering rules and logs. This makes it difficult to trace which data is sent to which service.

A gateway can enforce guardrails such as data classification and access control centrally. Shared logging also makes it easier to provide technical evidence about the use of external services.

Where the limits are

A gateway makes usage and inference costs easier to manage, but does not automatically improve model quality.

As a central component, it requires a robust and, where appropriate, redundant design. It also does not replace content review: routing and logging do not establish whether generated output is correct.