Scaling real-time STT for high-concurrency voice agents

Scaling real-time speech-to-text for high-concurrency voice agents requires treating persistent WebSocket streams as stateful sessions rather than applying stateless REST-style autoscaling. Key practices include routing sessions with affinity, scaling workers based on active connection counts instead of CPU alone, using ping/pong heartbeats and TCP keep-alives to remove hanging sessions, enforcing gateway connection limits, and monitoring p95 and p99 latency, queue depth, frame delays, and termination reasons. The discussion recommends realistic load testing with bursty arrivals, varied call durations, and representative audio, while identifying file descriptors, GPU memory, network behavior, inference capacity, and downstream LLM/TTS demand as possible bottlenecks. It also outlines approaches for managing overload, including pre-warming or snapshot restoration, selective buffering and frame dropping, admission control, circuit breakers, exponential-backoff retries, and multi-region routing for large deployments. The source contrasts self-hosted infrastructure, which requires capacity planning and operational management, with its managed Solaria-1 service, which it claims can add capacity automatically and provide low-latency partial and final transcripts.

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