What is AI Inference?
AI inference is when a trained model produces an output in production.
Technical detail
Training teaches a model patterns from data; inference is the live usage step. During inference, the model classifies, extracts, summarizes, or generates content based on new inputs. Inference quality depends on prompt design, context quality, and the right model for the task. Cost and speed are shaped by token volume, latency targets, and caching strategy.
Why it matters
- Most ongoing AI cost comes from inference, not one-time setup.
- Inference latency directly affects user experience and conversion.
- Smart model selection can improve reliability and reduce spend.
- Clear measurement helps teams avoid overbuilding.
Example
A support workflow classifies incoming tickets and drafts first responses. Simple tickets use a smaller model for fast turnaround, while complex cases route to a stronger model and a human review step.
How Retailbridge relates
Retailbridge emphasizes practical inference choices per workflow. Teams can route simple tasks to lighter models and reserve higher-cost models for harder decisions. Weekly snapshots make cost, speed, and quality tradeoffs visible.