LLM features are cheap to prototype and surprisingly expensive to run at scale. A demo that costs pennies becomes a five-figure monthly bill once real users arrive, because every request pays per token and it's easy to send far more tokens than you need. The good news: most AI bills are bloated, and a handful of tactics reliably cut them without users noticing any drop in quality.
The most expensive mistake is using your biggest, smartest model for everything. Most work in a product doesn't need it. Route by difficulty:
Implement a model router: a cheap first pass decides how hard the task is, and only the hard cases escalate to the premium model. This single change often cuts spend dramatically because the long tail of easy requests stops paying frontier prices.
Many requests are repeats or near-repeats. Don't pay twice:
You pay for every token in and out, so waste is literal money:
For work that isn't real-time — nightly summaries, bulk classification — use provider batch endpoints, which are often significantly cheaper than synchronous calls. For interactive features, stream tokens to the user so responses feel instant even when total latency is unchanged; perceived speed lets you use a cheaper model without users complaining. You can't cut what you can't see. Log token counts, model, and cost per request, tagged by feature. Almost always, one or two endpoints drive most of the spend — usually an over-large context or an over-powered model on a high-traffic path. Fix those first; ignore the rest.
Set budgets and alerts so a runaway loop or a viral spike doesn't produce a shocking invoice. And build a thin abstraction over the model call so you can change providers or models as prices move — the market shifts fast, and yesterday's cheapest option rarely stays cheapest.
Done together, these tactics routinely cut LLM costs by more than half with no visible quality loss. If you want your AI features fast and affordable at scale, talk to us.
Originally published on the Doktouri Agency blog. We build web, mobile, SaaS, and AI products — let's talk.