What is real-time inference?
Real-time inference is producing a prediction at the moment it is requested, fast enough to be used in the same transaction. A request arrives over an API, the model runs, and the answer returns while the user or the calling system waits. The acceptable wait is set by the use case: tens of milliseconds for a fraud check at the point of sale, a second or two for a chat assistant’s first token, a few seconds for a document classification in an upload form.
Serving in real time means the model must always be loaded and ready, which requires an inference server, enough hardware for peak load instead of average load, and monitoring of latency as well as accuracy. Time to first token and tokens per second are the usual measures for language models.
Why real-time inference matters
Real-time inference lets a model take part in a process while it is happening. A warehouse scanner app that suggests the correct bin for a mis-slotted item needs the answer before the worker walks away. An agent assist tool that drafts a reply for a customer service representative needs it while the customer is still on the chat. Batch inference cannot serve either case.
The cost is availability. A real-time endpoint is production infrastructure with uptime targets, capacity planning, and failover, and it is the component that fails visibly when traffic spikes. Running it on the company’s own hardware, or at the edge for latency-sensitive plant floor applications, keeps the data local and the latency predictable, but the capacity has to be sized and paid for in advance.