As AI applications grow, they often depend on multiple models, providers, tools, and policy rules. If each feature integrates with providers directly, the product tends to collect duplicated retry logic, inconsistent logging, scattered API keys, and no reliable view of cost. Provider APIs also change: models are renamed, pricing shifts, rate-limit behavior varies, and some capabilities are not portable across vendors.

The AI gateway pattern adds a central layer between your application and model providers. Instead of calling providers directly from each feature, the application routes model requests through a gateway that owns routing, retries, fallbacks, logging, budget controls, access control, and policy enforcement.

In this chapter, we build the pattern from first principles, then compare custom gateways, open-source proxies, and managed gateway services.