Deepgram touts sub-250ms latency with end-to-end text-to-speech architecture

Deepgram has introduced an end-to-end text-to-speech (TTS) architecture that it says reduces voice latency by eliminating handoffs between speech-to-text, large language models (LLMs), and TTS stages.

The company said this design lowers latency by 50–70%, reducing pipelined system delays from 450–750 milliseconds to a consistent 200–250 milliseconds, even under concurrent load.

Traditional cascaded architectures introduce cumulative delays: 100–300ms for transcription, 200–800ms for LLM inference, and 150–400ms for speech synthesis, with orchestration and network overhead often pushing latency above the sub-300ms threshold needed for real-time interaction.

Deepgram identified four factors critical to achieving sub-300ms latency: streaming delivery and time-to-first-byte (TTFB), concurrency handling, model efficiency, and server proximity.

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It reported that practitioners typically aim for 100–250ms TTFB. WebRTC paths can deliver 60–150ms, while TCP and WebSocket loops range from 220–400ms. Low-latency stacks can perform at 130–150ms in optimal conditions, but general-purpose systems often sit in the 250–300ms range. Deepgram said its Aura system can operate below 200ms with entity-aware processing.

The company added that unified per-minute pricing and bundled services help reduce cost uncertainty. It recommended pre-launch checks covering P95 TTFB, load testing, quota planning and reliability controls, with its Voice Agent API offered as a platform for validating performance targets.

The Recap

• End-to-end TTS reduces voice latency by 50–70% overall.
• Unified models achieve 200–250 millisecond mouth-to-ear latency range.
• Validate sub-300ms P95 TTFB and load readiness before launch.