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 is the act of running a trained AI model to get an answer, as opposed to training, which is the earlier work of building the model. Inference is what you pay for every time a user interacts with your AI feature. A feature that is cheap to prototype can still be expensive to run at scale.
Training is learning to drive. Inference is every trip you take after passing the test. The exam happens once; the road charges you per mile.
A customer service tool processes 50,000 messages a day through an LLM for intent classification. At current token pricing, the monthly running cost exceeds the original build cost within six months.
Inference is always involved when users interact with an AI feature. The question is whether you have designed for the running cost from day one.
You cannot avoid inference if you want a live AI feature. But you can cut cost by caching frequent answers, shrinking prompts, and routing simple tasks to smaller, cheaper models.
A recurring cost per use, not a one-time build cost.
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