What is Batch Inference?
TL;DR
An inference method that processes large volumes of data through an AI model at once. Superior in cost efficiency and throughput.
Batch Inference: Definition & Explanation
Batch Inference is an inference method that feeds large volumes of data to an AI model for processing all at once. In contrast to online inference, which processes one request at a time in real-time, batch inference handles hundreds to millions of data points simultaneously, resulting in higher GPU/CPU utilization efficiency and significantly reduced per-item costs. Major providers including OpenAI, Anthropic, and Google Cloud AI offer batch APIs, typically at a 50% discount from standard API pricing. It is ideal for tasks that do not require immediate responses, such as bulk email classification, large-scale content generation, periodic data analysis reports, and mass document summarization or translation. Key considerations for building batch inference systems include job scheduling, error handling, and retry mechanism design.