Latency is the time delay between when a caller finishes speaking and when the AI agent begins its response. Low latency creates natural conversation flow; high latency creates awkward pauses that make interactions feel robotic.
What contributes to latency?
Total latency includes speech recognition processing, network transit to AI services, language model inference time, and text-to-speech generation. Each component adds delay. Optimization requires attention to all parts of the pipeline, from audio streaming to model selection to infrastructure placement.
Why does latency matter?
Human conversations have natural rhythm. Delays of more than 300-500 milliseconds feel unnatural and create the perception of a slow, unintelligent system. Callers may think they were not understood and repeat themselves, further disrupting the flow. Low latency is essential for voice AI that feels conversational rather than mechanical.
Latency in practice
A voice AI platform measures end-to-end latency at 1.2 seconds, causing caller complaints. Analysis reveals 400ms for ASR, 600ms for LLM inference, and 200ms for TTS. Switching to streaming responses, optimizing model selection, and deploying infrastructure closer to users reduces total latency to 350ms, transforming the caller experience.