Inference mode is the way an AI model is served to users or applications. In practice, it describes whether requests are handled as streaming output, where tokens arrive incrementally, or as non-streaming output, where the full response is returned at once. Cloud platforms may also use the term to separate online serving from batch-style processing.
In cyber security, inference mode matters because it changes how traffic looks, how costs accumulate, and how abuse is detected. Streaming requests can create long-lived connections that need different monitoring and rate limits than short, bursty calls. Non-streaming use may be easier to log and audit, but sudden spikes can still signal misconfigured automation, credential abuse, or an application loop. Clear inference-mode metadata helps defenders tie spend and activity to approved workloads, build better alerts, and investigate suspicious usage without relying on brittle text parsing.