Throughput measures how much work an AI system can handle over time, for example tokens per second or requests per minute. Where latency is about the speed of a single response, throughput is about total capacity across many concurrent users.

It matters because production systems serve many people at once. A model can feel fast for one user yet fall over under real load if throughput is too low. Throughput depends on hardware, batching, model size, and how the serving stack is configured, and it directly affects both cost and reliability at scale.

At arosplatforms we plan for throughput from day one by load testing against realistic traffic, tuning batching and concurrency, and choosing serving setups that scale. This keeps client systems stable during peaks without overpaying for idle capacity.