A practical guide to infrastructure choices, workload accounting, and cost per accepted result—with an illustrative budget you can adapt.
Matching input and output base rates do not make two AI models equally expensive. Compare context tiers, caching, effort, retries, and cost per accepted result.
Reasoning effort changes the cost, latency, and behavior of supported models. Test settings per task and keep validation and human review where needed.
Prompt caching can reduce repeated input charges. Check eligible prefixes, writes, expiration, and measured reuse before estimating what it will save.
Token counts depend on the model and the actual request. Learn to estimate input, caching, reasoning, and output costs without treating words as a precise measure.
Measure more than the license: review, correction, usage, infrastructure, maintenance and risk. Compare total cost with accepted outcomes on similar tasks.