AI Gateways Explained
An AI gateway centralizes model access, routing, authentication, logging, and fallback behavior in front of one or more providers.
Prerequisites
Overview
Calling a model provider’s API directly from application code works until you need to switch providers, add a fallback, track cost per team, or enforce rate limits centrally. An AI gateway sits in front of one or more providers and handles that centrally instead of scattering it across every call site.
Where It Fits
Application
AI Gateway
Routing, auth, logging, fallback.
Provider A
Provider B
Key Points
- Unified interface
- A gateway typically exposes one API shape (often OpenAI-compatible) regardless of which underlying provider actually serves the request.
- Fallback and routing
- If one provider is slow, rate-limited, or down, the gateway can route to an alternative without the application noticing.
- Centralized logging and cost tracking
- Because every model call passes through one place, the gateway is a natural point to record cost, latency, and usage per team or feature.
Interview Question
What problem does an AI gateway solve that a direct API integration with one provider doesn’t?
It centralizes concerns that otherwise get duplicated at every call site — provider fallback, rate limiting, authentication, and cost/usage logging. Without a gateway, switching providers or adding a fallback path means touching every place in the codebase that calls the model directly.
Explain It in 30 Seconds
An AI gateway sits between an application and one or more model providers, centralizing routing, fallback, authentication, and usage logging so the application doesn’t need to handle those concerns at every call site.
Real-World Stack
Technologies commonly used to implement this in production.