AI inference: complete definition in AI for SMEs

AI inference is the stage where a language model "reasons" over an input (your prompt, your document, your image) to produce an output (response, classification, summary). It is the moment the model does its work: turning raw data into usable results. Training creates the model; inference uses it.

What it changes for an SME

In SMEs, inference is invisible but determines everything:

• Cost: every API call (every response, every auto-classification) is an inference billed in tokens;
• Latency: the time between your question and the answer depends on inference performance (model, infrastructure, network);
• Quality: the same model can produce different results depending on temperature, context and data chunking.

The choices that matter

Cloud inference (OpenAI API, fast, no server) or local inference (vLLM, fixed cost, data stays secure)? The answer depends on volume, data sensitivity and budget. In fractional AI leadership, we arbitrate per use case: an internal chatbot can run locally, while a one-off data extraction uses the API.