In this article
An AI gateway sits between your app and one or more LLM APIs, providing semantic caching, rate limiting, cost tracking, load balancing across providers, fallback routing, prompt/response logging, and content safety filtering. Azure API Management, LiteLLM, and Portkey are popular options. It's a critical production pattern for any multi-model deployment.
What it means in practice
AI Gateway is not just vocabulary; it is a design handle. Use it as a reference point when comparing architecture choices, debugging implementation trade-offs, or explaining system behaviour to another engineer. It helps convert a vague technical conversation into a concrete design question with trade-offs that can be tested.
Why engineers care
- It gives teams a shared name for the behaviour, risk, or architecture choice being discussed.
- It helps separate the goal from the implementation detail, so you can compare alternatives instead of copying a tool pattern blindly.
- It creates a useful checklist for reviews: inputs, outputs, failure modes, ownership, cost, latency, and measurement.
Production watch-outs
Be careful with shallow definitions. The useful meaning usually depends on workload, failure mode, data shape, and who owns the system in production.
Related context
Useful neighbouring concepts: Inference, Model Serving, Cost Control. Related deep dives on AI Wisdom include THE AI Gateway Pattern.