AI usage in an organization usually starts with a single application. Soon a second and a third follow: a support assistant, a document summarizer, an automation. Each one connects to models on its own.
The cost of scattered connections
- Keys spread everywhere. When a key leaks, it is hard to tell which application is affected.
- Usage becomes invisible. The security team cannot see the organization's AI usage in one place.
- Cost gets out of control. Which team spent how much becomes clear only at month end.
- Changing models gets hard. When a better model arrives, every application has to be updated separately.
What a model gateway does
A model gateway is a single layer between applications and models. Applications reach models through the gateway, not directly. On every request, the gateway:
- verifies who is making the request;
- checks permission to use the model, the budget and the request limit;
- checks input and output against your rules;
- routes the request to the right model, failing over to a backup model if it does not respond;
- logs every request.
On-premise and cloud through the same gate
One of the gateway's biggest benefits is managing on-premise and cloud models with one set of rules. On-premise is the default; the route to cloud models opens only for allowed work, and every use is logged.
Who benefits?
IT and security teams see usage in one place. Finance tracks cost by team and application. Developers work with one standard connection for every model; the model changes, the code does not.