The two AI gateway patterns in production inference

AI gateways centralize decisions that accompany model requests, including authentication, routing, rate limits, resilience, usage attribution, and auditing, reducing the fragmentation that occurs when these functions are spread across application code and operational systems. The text distinguishes between access gateways, which help applications use multiple external model providers through a unified interface, and serving gateways, which help model owners expose their own models as secure, multi-tenant customer APIs. Serving gateways are designed to connect customer identities and commercial policies with inference infrastructure, enforcing tenant isolation, quotas, capacity protections, metering, and traceability across each request. Gateway evaluation should therefore focus on alignment with a company’s customer model, billing needs, inference awareness, deployment location, portability, and remaining operational tooling rather than generic claims of routing or observability. For organizations commercializing their own models, placing gateway controls near inference can allow policies to be applied before expensive execution and improve visibility into deployment health and consumption. Baseten presents its Frontier Gateway as a managed serving-side option for models hosted on its Dedicated Inference platform, offering branded endpoints, API-key management, usage limits, consumption tracking, and inference-integrated operations, while noting that it is not intended as a universal provider-routing proxy.

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