Inference

Webisoft AI team Updated 2 min read

Inference is the act of running a trained AI model to produce an output, feeding it an input and getting back a prediction, answer, or generation, as opposed to training, the earlier process of building the model from data. When you send a prompt to a chat assistant, the model isn’t learning anything; it’s performing inference, applying what it already learned to your input. (Training and fine-tuning change the model; inference just uses it.)

For language models, inference means generating a response one token at a time, which is why answers stream word by word and why long outputs take longer. Inference runs on specialized hardware (GPUs and similar accelerators), usually in a provider’s cloud, though smaller models can run “on-device” or on a company’s own servers when privacy or latency demands it.

The practical vocabulary of inference: latency (how long until the answer, often split into time-to-first-token and tokens per second), throughput (how many requests a system handles at once), rate limits (a provider’s cap on your usage), and cost per token. When a provider has an outage or slowdown, it’s their inference infrastructure that’s struggling.

Why it matters at work

Inference is where AI’s operating costs live. Training happened once, at the provider’s expense; your company pays for inference every single time an employee or workflow calls the model, forever. That makes inference economics a genuine budgeting topic: a workflow that runs 50,000 documents a month is buying 50,000 inference calls, and choices like model size, prompt length, and caching change the bill directly. It also frames a common misconception, typing into a chatbot does not “train the AI on your data” by default; whether prompts are retained for future training is a separate contractual question worth checking.

A work example

An engineering lead cuts a document pipeline’s inference bill roughly in half by routing routine summaries to a smaller, cheaper model and reserving the flagship model for the contracts that legal flagged as complex.

  • Token, the unit inference is priced and measured in
  • Fine-tuning, training-side adaptation, in contrast to inference-time use

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FAQ

What is the difference between training and inference? Training is the expensive, upfront process of learning patterns from data. Inference is running the finished model to get an answer, which is what happens every time you use an AI product.

Why does inference cost matter to businesses? Every AI request consumes compute, typically billed per token. At scale, inference is the recurring cost of running AI features, so latency and per-request cost often drive model choice as much as quality does.