Latency is the delay between sending a request to an AI model and getting a response back. For language models it often includes time to first token, which is how quickly the answer starts, and the speed at which the rest streams out.

Latency matters because it shapes how usable a system feels. A chatbot that pauses for ten seconds frustrates users, and a fraud check that runs too slowly can miss its window. Latency is driven by model size, prompt length, hardware, network, and any retrieval or tool calls in the pipeline, so it is a design choice as much as a constraint.

At arosplatforms we treat latency as a first class requirement. We right size models, trim context, cache results, and split heavy work into background steps so client applications stay responsive without sacrificing answer quality.