AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
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 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.
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.
A startup prices a document review feature at $10/month per user, then discovers after launch that each user triggers 40,000 tokens per session. At current token rates, the feature costs $12 to deliver for every $10 charged.
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.
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