Foundational Concepts
Inference
The process of a trained AI model actually running and producing outputs, as opposed to being trained.
Definition
There are two phases in the life of an AI model: training and inference. Training is where the model learns from data, which is expensive and time-consuming. Inference is when the trained model is actually used, processing a user's request and generating a response. Every time you send a message to an AI chatbot, the model is running inference. The cost and speed of inference is what determines how practical a model is to deploy at scale.
Related Terms
Training Data
The text, documents, or other content an AI learns from during its development.
Parameters
The internal numerical values a model adjusts during training, more parameters generally means more capable.
Model Serving
The infrastructure that makes a trained model available to receive and respond to requests.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026
Knowledge Base·Foundational Concepts·Inference