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.
We built a self-hosted video calling system for a healthcare platform client, replacing the usual “meeting link” pattern with a real phone-call flow: clinician dials, patient’s phone rings, patient answers. Highlights: Read the full WebRTC.ventures Story of Success: Self-Hosted Jitsi for Telehealth: Ringing, Answer, and Recovery Instead
Choosing to go open source over a CPaaS for your WebRTC media stack is a strategic decision about control, flexibility, and long-term ownership. For teams building real-time products, an open source WebRTC media server can offer the freedom to customize media handling, integrate deeply with your architecture,
For organizations prioritizing data privacy and zero variable cloud costs related to inference, it is entirely possible to build a voice agent using off-the-shelf open source tools. In this post, we will outline a practical Voice AI stack that avoids vendor lock-in while still supporting real-time, natural
