Speech-to-speech (STS) models revolutionize real-time voice AI by processing voice input and generating voice output within a single system, bypassing the delays typical of traditional pipelines involving Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS). This integrated approach maintains tone, emotion, and speaker identity, providing a natural conversational experience with sub-200ms latency, which is crucial for applications like multilingual meeting translation, customer service, media localization, and in-car assistants. Providers such as Deepgram emphasize audio-native pipelines that combine ASR, language understanding, and TTS to minimize latency and improve production reliability, handling real-world audio conditions effectively. Organizations must evaluate STS platforms based on specific needs such as accuracy, scalability, compliance, and integration capabilities, ensuring that the chosen provider can handle specialized audio conditions and meet operational constraints without relying solely on laboratory benchmarks.