The two AI gateway patterns in production inference

AI gateways centralize decisions that accompany every model request, including authentication, routing, usage limits, reliability handling, metering, and auditing, preventing these responsibilities from being fragmented across application code and infrastructure. The text distinguishes between access gateways, which help an application use multiple external model providers through a unified interface, and serving gateways, which enable model owners to expose their own models as secure, multi-tenant commercial APIs for customers. Serving gateways validate customer identity, select healthy and appropriate model deployments, enforce tenant isolation and rate or token limits, attribute consumption for billing, and connect customer-facing requests to underlying inference events for support and auditing. Organizations evaluating gateways should consider alignment with their customer and entitlement model, isolation during traffic spikes or degradation, inference-specific awareness, deployment location, portability, and the remaining systems needed for subscriptions, billing, and customer support. For model providers, placing serving-gateway functions close to inference can apply policy before costly execution and improve operational visibility. Baseten presents its Frontier Gateway as a managed serving-side option for Baseten-hosted models, offering branded endpoints, API-key management, usage limits and attribution, and integration with its dedicated inference infrastructure, while emphasizing that it is not intended as a universal proxy for third-party model APIs.

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