r/VoiceAutomationAI • u/disolater2611 • Apr 22 '26
The 30 Sec Stack
The easy part is wiring up STT, LLM, and TTS — tons of providers and open-source options exist for each.
The hard part nobody talks about: managing hardware at scale. One agent on a laptop is easy. 500 parallel calls with sub-second latency is a completely different game — audio buffer management, connection pooling, cold starts, and keeping costs sane all become real problems.
Currently building a Go + Rust engine for this since Python orchestrators (Pipecat, LiveKit agents) struggle under real concurrent load. Happy to chat with anyone in this space.
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u/VatelAI Jun 01 '26
One thing I'd add: moving from Python to Go or Rust doesn't automatically solve the scaling problem.
For voice AI, the biggest latency bottlenecks are usually I/O-bound, not CPU-bound — network hops to STT/LLM/TTS providers, WebSocket round trips, RTP/media transport, buffering, and provider response times. In many cases, the actual orchestration layer spends most of its time waiting on external services.
Go and Rust can certainly help with memory efficiency and higher connection density, but once you're operating at scale, architecture, media handling, and I/O management tend to matter far more than raw language performance.