Dictionary
Inference
Running a trained model to produce outputs.
1 min readupdated 2026-07-04
/ quick answer
Inference is the act of using a model (as opposed to training it). Latency, throughput, and cost per token are the three inference metrics that matter in production. Running a trained model to produce outputs.
Running a trained model to produce outputs. Inference is the act of using a model (as opposed to training it). Latency, throughput, and cost per token are the three inference metrics that matter in production. In practice: An API call to Claude is one inference request. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Inference is the act of using a model (as opposed to training it). Latency, throughput, and cost per token are the three inference metrics that matter in production.
Example
An API call to Claude is one inference request.
/ frequently asked
What is Inference?
Inference is the act of using a model (as opposed to training it). Latency, throughput, and cost per token are the three inference metrics that matter in production.
What is an example of Inference?
An API call to Claude is one inference request.
Why does Inference matter for AI and automation?
Running a trained model to produce outputs. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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