Many AI features are affordable in a demo but not in production. I set up the architecture so cost per user stays predictable, even as usage grows.
In most AI setups, the savings sit in the same three places.
A large model for a task a small model could also handle. The price difference per call can be significant.
The same question or the same document being reprocessed every time, without caching.
Models that could run on-device or on your own hardware, but are still billed per call.
I start with what you're paying now and for what. Then I only change what demonstrably pays off.
What does each AI feature cost per call and per user, and where's the most volume?
A small model where it can, a large model where it must.
Repeated questions served from cache, heavy work batched at moments when it's cheaper.
Where volume is high or privacy matters, the model runs locally or on your own hardware.
Bring your current setup and monthly cost to the quickscan. I'll show you where the biggest savings are.