Cutting AI generation costs by a third without touching quality

Generative video is priced per second, and the difference between providers is large enough that the naive approach — pick one model, use it for everything — quietly costs multiples of what the work requires.

We were paying $0.84 for a five-second clip. We now pay $0.56 for the same clip at the same quality. The change was not a cheaper model.

The default that costs money

The model generated synchronized audio by default. We were producing clips for a pipeline that replaces the audio track downstream anyway — so we paid a premium, every render, for a soundtrack that was discarded seconds later.

Turning it off cut a third off the bill and changed nothing about the output we actually used.

Why a registry beats a hardcoded default

The obvious fix is to pass the flag. The durable fix is to stop hardcoding provider quirks in application code, because every provider names things differently and prices them differently. We moved model metadata into a registry — which parameter controls audio, whether it affects price, what the per-second cost is — so job code asks for what it wants and the registry resolves how to ask for it.

That structure also makes an unknown model a loud error rather than a silent wrong default, which matters more than it sounds. The expensive failures in cost engineering are the quiet ones.

The habit worth stealing

Read the pricing page for every parameter, not just the model name. Then ask, for each thing you're paying for, whether it reaches the final output. Ours didn't, for months, because nobody had checked what the default was doing.

Inference cost is the operating expense that scales exactly with your success. It deserves the same scrutiny as any other line item, and it usually gets none.