Model providers change capabilities, pricing, and availability. A multi-model AI gateway is an architectural layer between your application and model vendors. It can provide a shared API, centralized authentication, model selection, usage visibility, and a place to implement fallbacks.
What it should own
The gateway is a good place for concerns repeated across AI features: credentials, request logging, usage accounting, quota management, model aliases, routing policy, and timeouts. Your product should retain business logic, user authorization, domain validation, and the definition of a correct answer.
Reliability is a policy, not a promise
No individual model or network route is perfectly available. Define the failure path before the incident: which errors may retry, how long to wait, which fallback is acceptable for a task, and when to return a clear user-facing message. Do not silently replace a high-stakes route with a weaker one unless the quality requirement allows it.
primary model → retry transient failures → compatible fallback for low-risk work → queue or clear error for high-risk workUse explicit aliases
Instead of scattering provider model IDs across code, create aliases such as fast-extract, balanced-chat, and deep-reasoning. Map each alias to a tested route in configuration. The team can upgrade or replace a route after evaluation without changing application code.
Observability makes routing intelligent
For every call, record the alias and resolved model, token usage, latency, status, retry count, and a quality signal. This tells you where a route needs to change and prevents confusing a prompt or tool regression with a model regression.