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Why the Bill Grew, and What to Do About It

Bills grow for boring reasons, and almost never the reason people guess. Measure before you change anything, then take the free savings before the ones that cost you quality.Find where the tokens go, fix the stable part of your prompt so it can be cached, and work the levers in order: free wins before tradeoffs.Token profiling from usage logs, prefix stability and cache invalidation, and lever order: caching and hygiene before effort, batch or model changes.

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Questions this answersQuestions this answersfaq

our AI bill doubled. where do we look first?our AI bill doubled. where do we look first?how do I profile spend?
Not at the model. Look at what is being sent, because that is usually what grew: a longer standing instruction, more connectors left switched on, or conversations that run longer before anyone starts a new one. The answer people jump to is a cheaper model, and it is almost always the wrong first move.Not at the model. Look at what is being sent, because that is usually what grew: a longer standing instruction, more connectors left switched on, or conversations that run longer before anyone starts a new one. The answer people jump to is a cheaper model, and it is almost always the wrong first move.Start from logged usage per route, split input against output. Input growth points at prefix or history: a longer system prompt, more tool definitions, longer threads, or a cache that stopped hitting. Output growth points at task shape or effort. Changing model before you know which half grew is guessing, and it forfeits cache reuse for a saving you have not measured.
is a cheaper model the answer?is a cheaper model the answer?when does a smaller model actually pay?
Sometimes, and later than you think. Try the current model doing less thinking first. A cheaper model that needs two attempts and a person to check it is not cheaper, it just moves the cost somewhere that does not show up on the invoice.Sometimes, and later than you think. Try the current model doing less thinking first. A cheaper model that needs two attempts and a person to check it is not cheaper, it just moves the cost somewhere that does not show up on the invoice.Measure the newer model at lower effort before moving tiers. Judge cost per completed task, not per request: a cheaper call that needs a retry or a human correction is more expensive than the one that worked. Splitting a workflow across two models also forfeits cache reuse between them, because a cache is scoped to a model.

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We store your name, your email, which lessons you open and which you finish, and the reading level you use. Distru can see that. Nothing is charged and nothing is sold on.Stored: name, email, which lessons you open and complete, and your level on the dial. Distru staff can see it. No payment, no third-party advertising trackers.stored: name, email, per-lesson view + completion, data-level. visible to Distru. no payment, no ad-tech, no third-party pixels.