Online Inference
An inference mode in which an AI model is embedded within a business pipeline to return predictions in real time for each individual request, as opposed to offline batch processing. This mode places strict requirements on inference latency.
Timeline (last 90 days)
Online inference requires low latency and high concurrency, and vendors must reserve GPU resources to handle burst traffic, resulting in a higher unit cost; batch inference allows tasks to be queued and executed during off-peak periods, resulting in a lower unit price.
International card network standards typically require end-to-end decision latency for fraud detection to be below 100–200 milliseconds.
All Facts (2)
Online inference requires low latency and high concurrency, and vendors must reserve GPU resources to handle burst traffic, resulting in a higher unit cost; batch inference allows tasks to be queued and executed during off-peak periods, resulting in a lower unit price.
50%UnverifiedInternational card network standards typically require end-to-end decision latency for fraud detection to be below 100–200 milliseconds.
50%