When WebRTC was first introduced by Google over a decade ago, it came with the promise of simplicity. “Just drop in a little JavaScript and you’ve got video chat in the browser with no downloads necessary!” While that vision helped kickstart a wave of innovation in real-time
Large Language Models (LLMs) have dominated conversations about AI integration in WebRTC, particularly when it comes to voice-based features like transcription, summarization, and intent detection. But there’s an emerging layer that many outside of research circles are missing: Vision Language Models (VLMs). Unlike LLMs, which work with
Real-time video communication applications face unique scalability challenges that can make or break the user experience. When thousands of users simultaneously join virtual classrooms, video conferences or other streaming video experiences, traditional autoscaling approaches often fall short. The key to managing predictable traffic spikes in WebRTC applications
Since Zoom adopted WebRTC, we’ve been closely monitoring their developer platform evolution. Zoom’s WebRTC-powered Video SDK is a powerful addition to the CPaaS landscape, offering rapid integration, robust performance, and a wide array of features for custom video solutions. At their 2025 Developer Summit, Zoom unveiled significant
