Hidden Costs of AI APIs: Tokenization, Caching, and Retries Can Inflate Bills
A developer building a document summarization tool projected about $40 in monthly API costs but received a bill of roughly $312. The article attributes the gap to tokenization, since technical terms can consume several tokens each, often inflating counts 30-60% above word-based estimates. It also explains that cached-input discounts apply only to byte-identical prompt prefixes, so small dynamic additions like timestamps can wipe out savings. Longer context can degrade output quality, pushing developers toward chunking and extra API calls, while rate limits add retry overhead that costs money even without a successful response.
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