Deployment & economics
Inference cost is the line item that surprises teams after launch. A feature has to be priced and designed around its running cost, or a popular one can quietly become a loss.
In plain terms
Inference cost is the ongoing expense of running an AI model each time it is used, driven mainly by how much text goes in and out.
Inference cost is what you pay each time the system answers. It is separate from the cost to design and build the feature. Vendors usually charge by how much text goes in and comes out. Images, long documents, and chatty answers cost more than a short classification.
Price the busy month, not the pilot. A feature that saves a support minute can still lose money if each chat costs more than that minute was worth. Cap the length of an answer, cache repeated questions, and pick a smaller model for the easy jobs. Put an alert on the bill before the finance review finds it.
Think of it this way: Inference cost is like an electricity bill that charges per device per hour. The more your AI feature is used, the higher the bill. A popular feature with an inefficient prompt design can be a loss at scale.
Picture a document feature priced at a flat fee per user. If each user sends long files on every visit, the running bill can pass the price you charge. That gap shows up after people start using it, not in the demo.
A subscription app offers unlimited AI summaries. Power users paste whole books. The flat subscription no longer covers those users. The team caps summary length, charges a higher tier for heavy use, and routes simple summaries to a cheaper model. The feature stays. The loss-making pattern stops.
Model inference cost into every feature design discussion. Map typical input and output sizes, pick the smallest model that meets quality requirements, and size the running cost before pricing the product. Never design an AI feature without a cost model. The cost of that omission is usually discovered after launch, when changing the architecture is expensive and the pricing is already set. An ongoing operating cost that scales with usage.
RaftLabs prices the running cost before the build, so a feature people like does not become a loss. You get a number for a busy month, not only a demo. The related work on our side is AI cost calculator.
This sits with the other deployment & economics terms on the glossary. What you pay to run AI, and the choices that change the bill. Worth reading next: API, Open vs Closed Models, and Latency.
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