Generating one AI video is easy. Generating a useful library without losing track of cost, prompts, or failed jobs is a different problem.
I built a small asset factory for short product videos. The completed batch produced 93 vertical clips with zero failed jobs.
Write the bill before the prompts run
Every clip existed in a manifest before the first paid request.
The manifest stored the prompt, seed, filename, scene role, model, duration, resolution, and maximum cost. Each clip was four seconds at 1080×1920. Audio and on-screen text were disabled.
At the active rate, the maximum cost was $74.40. The runner refused to cross the approved $75 cap.
The script also counted only missing files. If a run stopped, it resumed the same batch instead of paying to recreate valid clips. It waited during quota pressure and kept the selected model fixed.
Generate footage, not finished ads
The prompts asked for one person, one room, one action, and one continuous shot. Each clip started and ended during movement. That made the footage easier to cut.
The prompts also banned text, logos, frozen poses, changing furniture, extra people, damaged hands, and sudden camera jumps. No prompt can remove every visual error, but clear limits improve the review pile.
The 93 clips became 12 short edits for three products. A local FFmpeg script assembled those videos with uneven shot lengths and hard cuts. That step added no generation cost.
The useful system was not the model call. It was the budget file, resumable queue, quality rules, and edit record around it.