The AI gateway: one control point for cost, security and quality
Direct provider calls scattered across services make AI spend impossible to govern. A gateway gives you one place to enforce budgets, policy and model choice.
Most AI architectures start the same way: an API key in an environment variable, a provider SDK in one service, then in five. Within months there is no single answer to what the company spends on AI, or on what.
What a gateway actually does
- Attribution: every call carries a team, product and user context, so cost lands where the value is created
- Budgets and limits: hard caps and alerts per team, not one shared credit card
- Policy: redaction, prompt and response logging, blocked data categories, retention rules
- Routing: cheap models for cheap tasks, premium models where quality is measurable
- Portability: providers change prices and deprecate models; a gateway makes that a config change
The cost lever most teams miss
Model choice usually beats prompt tuning. Routing routine classification, extraction and summarisation to a smaller model, while reserving frontier models for genuinely hard requests, commonly cuts AI spend by half at unchanged output quality. You can only do this credibly if you measure quality per route.
Caching is the second lever
A meaningful share of production traffic is repeated or near-repeated. Semantic caching at the gateway removes that spend entirely and improves latency at the same time.
Do it before the second use case
Retrofitting a gateway across a dozen integrated services is a migration project. Introducing it when you have one or two is an afternoon. The cost of waiting is not the tooling — it is the rework.