Self-hosting a voice AI pipeline gives you control that managed APIs can’t offer: you choose the models, you decide where they run relative to each other, and you keep audio data within your own infrastructure. Getting there means managing GPU workloads, model placement, and service networking yourself.
When WebRTC quality degrades on your SFU (jitter spikes, frozen frames, choppy audio), your monitoring tells you something is wrong. It doesn’t tell you what is responsible. Is the network dropping packets? Or is your server too loaded to read them from the socket on time? The
The annual ClueCon conference founded by the creators of FreeSWITCH has always been where the real-time communications community gathers to share what’s working, what’s breaking, and what’s next. This year’s edition, August 10-14 at The Midland Hotel in Chicago, made one thing unmistakably clear: AI is no
Voice AI tooling broadly falls into two categories: managed platforms that get you a production agent fast, and custom-built frameworks that give you full ownership of your voice pipeline. Within each category, the specific tools differ in ways that are worth understanding before you commit engineering time
