The bill being higher than expected is a slightly different problem from the bill being high. It means your mental model of what AI should cost is wrong somewhere. Closing that gap is more useful than just reducing the number, because a correct model prevents every future surprise.
Estimates of AI cost run low for consistent, structural reasons. Here are the ones that account for most of the gap.
You estimated output tokens and forgot input tokens
The most common estimation error is counting only the tokens the model generates. You pay for input tokens too, and in many applications input dwarfs output. A summarization task that returns two hundred tokens might send four thousand tokens of document to summarize. If your estimate was based on response length, it missed most of the cost.
You estimated one call per task, but tasks make many calls
A single user action often triggers multiple model calls behind it. An agent that plans, acts, checks its work, and corrects can make a dozen calls for one task. If your estimate assumed one call per interaction, the real number could be several times higher before anything went wrong.
You estimated steady state, but usage grows within a session
Cost per interaction is not constant. In conversational and agentic features, later interactions in a session cost more than earlier ones because accumulated context rides along. An estimate built on the cost of the first interaction understates the average across a real session.
You estimated the happy path and ignored retries
Estimates almost always model the case where everything works on the first try. Production includes rate limits, transient failures, and validation rejections that trigger retries. A realistic estimate has to include a retry factor, and if your code retries aggressively, that factor can be large.
You estimated your workload and missed everyone else's
If you estimated the cost of the feature you built, but the same billing account serves other teams, experiments, and tools, your estimate was only ever a fraction of the total. Shared accounts without attribution make every individual estimate look low against the aggregate bill.
Building an estimate that holds
A durable estimate has five inputs, not one: input tokens per call, output tokens per call, calls per task, a retry factor, and a session growth factor. Multiply those against your expected task volume and you get a number that survives contact with production. Leave any of them out and your estimate will run low in exactly the way that produces a surprising bill.
The gap between expectation and reality is not random. It is the sum of the factors your first estimate left out. Add them back and the bill stops being higher than expected, because your expectation finally matches how the system actually consumes tokens.