Cut Your AI Bill Without Cutting Quality

The biggest waste in most AI stacks is sending every request to the largest model. Route by task instead: use a small, cheap model for classification, extraction, and short replies, and reserve a frontier model for reasoning, code, or final drafts. A simple router that checks input length and task type can cut spend 40-70% with no visible quality drop.

Cache aggressively. Enable prompt caching for stable system prompts and long context so you pay full price once, then a fraction on every hit. Deduplicate identical requests, and store finished outputs (summaries, tags, thumbnails) so repeat traffic never touches a model. For images and video, cache by prompt hash and resolution to avoid re-rendering the same frame.

Finally, measure per-feature cost, not just a monthly total. Log tokens, model, and latency per endpoint so you can see which feature actually burns the budget. On CinderHub we tag every chat, image, and video job with its cost, making it trivial to spot a runaway storyboard loop before the invoice arrives. Set hard token caps and fall back to cheaper models on overflow.