The real-time voice AI stack for agents: An architecture guide
Blog post from Deepgram
Production voice AI systems combine transport, speech recognition, turn detection, language-model reasoning, text-to-speech, and orchestration under a tight per-turn latency target of roughly one second, as delays beyond 600–800 milliseconds can reduce conversational responsiveness. Telephony paths such as SIP and PSTN may add 200–400 ms before processing, while WebRTC can reduce network overhead; streaming across STT, LLM, and TTS stages can lower time-to-first-audio by overlapping work rather than waiting sequentially. Endpointing is a major tuning trade-off because faster detection can reduce dead air but risks interrupting callers, while LLM time-to-first-token, external tool calls, and TTS time-to-first-byte each materially affect perceived speed. The discussion compares bundled platforms and configurable frameworks including Deepgram, Vapi, LiveKit, Pipecat, and Retell, emphasizing differences in streaming protocols, concurrency, compliance support, self-hosting, and operational control. It also contrasts modular cascading pipelines, which favor auditability and compliance, with lower-latency speech-to-speech systems and hybrid designs, and recommends measuring real carrier paths and audio at P95, instrumenting every stage, setting percentile-based service targets, and addressing endpointing and orchestration inefficiencies before simply replacing language models.
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