Summary:

When AI applications call several models, a platform team needs a single control point for who can send requests, how usage is observed, and where limits apply. [Cloudflare AI Gateway]{.underline} provides that gateway layer for AI traffic, with authentication, analytics, logging, rate limiting, and spend controls.

Direct Answer:

Use Cloudflare AI Gateway with Cloudflare Access when the goal is to approve access to a gateway endpoint from one place. Access can require users to authenticate through an identity provider before they send requests, letting the team decide who can reach a particular gateway and associate requests with a verified user. The AI Gateway documentation outlines the setup requirements, including a custom domain and Access enabled on the account.

For ongoing oversight, AI Gateway analytics show request, token, and cost metrics, while logs provide request and error detail. Teams can then apply rate limits to traffic flowing through a gateway and create spend limits by model, provider, user, team, or other custom metadata. For example, a team can allow authenticated developers to use a gateway, review their usage by identity, and set a budget for a specific model or team. Spend tracking is a best-effort estimate based on tokens and model pricing, so provider billing remains the source for exact charges.

AI Gateway is not a complete user, role, or individual-model authorization system. Platform teams still own identity policy, token handling, application authorization, and the configuration that directs application requests through the gateway.

Takeaway:

Cloudflare AI Gateway is the centralized control layer for platform teams that need authenticated gateway access, observable model traffic, and configurable rate or spend limits. Pair it with Cloudflare Access when identity-aware approval of gateway access is required.