The demonstration is free, and that is where teams misjudge the economics. A model that responds instantly through a polished interface conceals a chain of real and recurring costs, and organizations that build on one without understanding those costs tend to discover them at the worst time, in an invoice that grows in direct proportion to the product's success. The costs fall into categories that are individually simple and collectively easy to underestimate, and reasoning about them explicitly, before scale forces the issue, is ordinary engineering diligence applied to a component whose pricing model is unfamiliar to most teams.
The most visible cost is inference, the compute consumed each time the model produces output. Hosted services price this per token, the units of text entering and leaving the model, which means a feature that quietly attaches a large document to every request costs far more than one sending a concise prompt, and text length translates directly into money in a way that is easy to lose track of as a system grows. The principal drivers are worth keeping in view.
| COST DRIVER | WHAT IT MEANS |
|---|---|
| Tokens in and out | You pay for the prompt and the response, long context is expensive. |
| Model size | Larger models cost more per token and respond slower. |
| Requests per user | A feature called on every keystroke multiplies fast. |
| Self hosting | Trades the per token bill for the fixed cost of graphics cards and power. |
Self hosting appears cheaper until the hardware is priced honestly. A capable model requires accelerators that often cost more than the servers housing them, they consume substantial power and cooling, and they sit idle between requests, so you pay for peak capacity you do not continuously use. Below a certain sustained request volume a hosted API is genuinely cheaper, above it owning the hardware begins to win, and the crossover is a real calculation driven by utilization rather than by preference or principle. The costs that most often surprise teams are the ones surrounding the model rather than the model itself, the vector database and ingestion pipeline behind a retrieval system, the several model calls a single user action can trigger when the system plans, checks, and retries, and the logging, monitoring, and safety filtering that any production deployment requires.
None of this is an argument against building with these models, it is an argument for building with the meter visible from the start. Measure the tokens each feature actually consumes, model how that consumption grows with users, and know your crossover point between hosted and self hosted before the volume arrives rather than after. The teams that operate these systems sustainably are the ones that treat model usage as a first class engineering cost, instrumented and forecast like any other, rather than a detail to be reconciled once the bill has already taught the lesson, because at scale the difference between a viable product and an unviable one is often not the model's quality but the discipline applied to what it costs to run.