Glossary · 1 minute read
What Is AI Inference?
AI inference is the process of using a trained model to make predictions or generate outputs on new inputs—what happens every time an AI answers a question or classifies data. It differs from training, which builds the model once; inference runs continuously in production, so it's the ongoing operating cost of AI. Because generative AI charges per request, inference economics decide whether an AI feature is viable at scale. You optimize inference with model selection, caching, batching, and efficiency techniques.
Inference is what happens every time an AI answers—and it's the cost that never stops. Here's what it is, why it drives economics, and how to optimize it.
What AI inference is
AI inference is using a trained model to make predictions or generate outputs on new inputs—what happens each time an AI answers a question or classifies data.
Inference vs training
| Training | Inference | |
|---|---|---|
| When | Build the model once | Run it continuously |
| Cost | Big upfront | Ongoing, scales with usage |
Training builds the model; inference runs it in production—so inference is the ongoing operating cost of AI.
Why inference economics matter
Because generative AI charges per request, inference is a recurring cost that scales with usage. At scale, inference economics decide whether an AI feature is viable—central to generative AI cost and AI SaaS unit economics.
How to optimize inference
| Technique | Effect |
|---|---|
| Model selection | Right-sized, not biggest |
| Caching | Reuse repeated results |
| Small/distilled models | Cheaper per request |
| Retrieval | Fewer tokens |
These keep per-request cost aligned with value—see how to build an AI API.
Why FISTA
FISTA Solutions builds AI with efficient inference—model selection, caching, and optimization that control cost at scale—through AI enablement, backed by 150+ projects across 12+ countries.
Making AI affordable at scale? Talk to FISTA.
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01What is AI inference?
Using a trained model to make predictions or generate outputs on new inputs—what happens every time an AI answers or classifies. It's the runtime use of a model, as opposed to training, which builds it.
02What's the difference between training and inference?
Training builds the model once by learning from data; inference uses the finished model repeatedly in production. Training is a big upfront cost; inference is the ongoing operating cost that scales with usage.
03Why does inference cost matter?
Because generative AI charges per request, so inference is a recurring cost that scales with usage. At scale, inference economics determine whether an AI feature is financially viable— an important part of AI unit economics.
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