An AI feature can ship fast and then surprise everyone with its bill. On AWS, inference cost is mostly an architecture decision — and a few habits keep it from running away.
Not every request needs the biggest model
Route by difficulty. A small, cheap model handles the common, easy cases; escalate to a larger one only when needed. Most traffic is simpler than the worst case you designed for, so most traffic should cost less.
Cache and batch relentlessly
Identical or near-identical prompts should not be paid for twice — cache results where it is safe. Where latency allows, batch requests to use hardware efficiently. These two levers often cut cost more than any model swap.
Make spend observable
Tag inference workloads and watch cost per feature, not just total. A dashboard that shows spend next to usage turns a scary surprise into a number you manage. You cannot optimize what you cannot see.
The goal is not the cheapest possible setup — it is a bill that stays proportional to the value the feature delivers.