The WebRTC.ventures Blog.

AWS autoscaling works well for stateless applications. And for the stateless components of VoIP and real-time systems like APIs and routing backends, AWS Auto Scaling groups (ASGs) and Amazon Elastic Container Service (ECS) do exactly what they're designed to do. But stateful VoIP infrastructure components like FreeSWITCH and RTPEngine maintain active SIP sessions and media streams tightly bound to specific instances. Because each instance holds live call state, scaling events must be carefully coordinated. Otherwise,

For the past several years, AV1 has been generating real excitement in the WebRTC and real-time communications space. Better compression, no licensing fees, and backing from the biggest names in tech made it look like the codec the industry had been waiting for. Then, on March 23, 2026, Dolby Laboratories filed patent infringement suits against Snap Inc., targeting Snap's use of HEVC and AV1 to encode and transcode videos in its Snapchat application. For developers

For years, developers building real-time video and audio applications on the web have worked under a quiet constraint: whatever protocol you chose, it had to work in Safari.  Well, that’s not 100% true because many WebRTC apps used to have disclaimers like “works best in Chrome”. When Safari finally fully supported WebRTC, it was a happy day for WebRTC developers. We could stop asking users “what browser are you using?” when responding to bug reports.

Your users report poor call quality, a dropped call, or a connection that never got established. But what actually happened? In this episode of WebRTC Live, we’ll break down what commonly fails in production WebRTC apps, how experienced teams debug live incidents, and how to build the visibility that keeps you ahead of problems. Guest host Alberto Gonzalez, CTO of WebRTC.ventures, sits down with Justin Williams, Senior WebRTC Engineer at WebRTC.ventures. Justin brings hands-on experience building real-time

Have you ever noticed a very subtle and easy-to-miss security setting in WhatsApp called “Protect IP address in calls”? Some people scroll past it without a second thought. Others may enable it without fully understanding why it exists. The real question is: Why would WhatsApp even give users the option? If calls are already “end-to-end” encrypted, what exactly still needs protection? VoIP (Voice over Internet Protocol) and WebRTC (Web Real-time Communication) calls are often considered

AI-driven QA testing is reshaping how teams validate real-time applications. Doing it well requires intentional processes, shared knowledge, and a collaborative culture that allows teams to use AI responsibly and consistently. Our WebRTC.ventures QA team has approached this with a clear mindset: the real value of AI in software testing comes from building operational systems that let the entire team benefit, while maintaining strong testing discipline. We've built internal practices that make AI a core

The “no-code” revolution has made AI voicebots significantly more accessible. Non-technical teams can now launch voicebots quickly with platforms like Vapi or Bland AI and start automating customer interactions without a dedicated engineering team. In practice, running a good voicebot requires serious operational strategy. Long-term success depends on factors that aren’t always visible upfront:  how the system handles context how deeply it integrates with your existing tools how it performs under real-time constraints how costs

WebRTC.ventures CTO Alberto González recently joined the Software Defined Talk podcast to share insights on building voice, video, and streaming applications for enterprise use. In the conversation, he explains how WebRTC powers the real-time experiences behind many of today’s most important communication products, and why companies across healthcare, education, customer support, legal, and other regulated industries rely on custom solutions to meet their business and compliance needs. For teams exploring enterprise communication platforms, the episode

Early Voice AI deployments were built on a straightforward pattern: Speech To Text, LLM, Text To Speech. That pipeline was enough to produce compelling prototypes for customer support, sales automation, and meeting summaries. The pattern holds well until it meets a regulated environment. Telecom platforms, telehealth systems, emergency response workflows, and financial infrastructure impose requirements that the basic pipeline was never designed to satisfy.  This post outlines a production voice AI architecture for regulated environments:

Improving quality in WebRTC applications is an ongoing task, it doesn’t stop when you deploy your application. To support maintenance, debugging, and continuous improvement, observability needs to be baked in from the beginning. On March 25, 2026, our guest was Balázs Kreith, a Senior Software Engineer at Riverside.fm, lead developer of the open source ObserveRTC project, and a veteran of WebRTC teams at Whereby and callstats.io. Balázs talks about Quality of Service, Quality of Experience, and share common pitfalls

Over the last few years, Voice AI agents have moved quickly from experimentation into production. Early adoption centered on customer support, basic IVR modernization, sales automation, meeting summaries, and general-purpose voice assistants. These early use cases were low-stakes enough to tolerate imperfection. That is changing. Real-time Voice AI agents are now being deployed in regulated and mission-critical environments such as telecom platforms, telehealth systems, emergency response workflows, and financial infrastructure. In those settings, the hard

Testing an AI voice agent is nothing like testing a standard application. You're validating a live, real-time pipeline where WebRTC audio streaming, speech-to-text, LLM reasoning, and text-to-speech synthesis work together within milliseconds, every time a user speaks. Traditional QA processes and frameworks weren't built for this. They were not designed for systems where latency is a feature, where a 400ms spike in STT processing changes the entire feel of a conversation, or where multi-user dynamics

Recently, I read an article on LinkedIn that captured something many experienced developers have been feeling: software development is changing rapidly in the age of generative AI, but not always in ways we fully understand. One quote especially resonated with me:  “An MIT professor called AI ‘a brand new credit card that lets us accumulate technical debt in ways we were never able to before.’ That credit card now writes 41% of the code.” Whether

When a telehealth platform starts to grow, its video infrastructure either grows with it or becomes the thing that holds it back. That's exactly the crossroads Sessions Health reached. Their HIPAA-compliant practice management platform for mental health professionals had outgrown its self-hosted Jitsi setup, hitting reliability issues at around 500 concurrent sessions. With adoption accelerating, they needed their telehealth video infrastructure to handle 10× that load without compromising call quality or patient privacy. WebRTC.ventures conducted

Critical environments like emergency response, industrial IoT, and public safety are systems of systems: communications, data, and operational technology are tightly coupled, and failures propagate fast. VoIP is core operational infrastructure. It’s a dependency that other critical operations assume will work under stress, during incidents, and across organizational boundaries. When calls drop or degrade, teams need systems that fail predictably and expose clear diagnostic signals across the stack. While SaaS and CPaaS platforms excel for

Autoscaling is often treated as the gold standard for cloud efficiency. With a few lines of configuration, you can tune your infrastructure to match traffic in real-time, saving money while keeping your app solid under load. But with Real-Time Communication (RTC) apps, the rules change. Whether you’re using WebRTC, SIP-based VoIP, or any other real-time media stack, traditional autoscaling can flip your system into the “Upside Down.” Instead of efficiency, you get dropped calls, ghost

TURN servers remain one of the most common points of confusion in WebRTC applications. While STUN and TURN servers both play critical roles in establishing WebRTC peer connections, teams new to WebRTC development often struggle to understand their differences and implementation details. Even experienced developers may overlook key considerations when deploying and managing TURN servers. That’s why we’ve assembled a panel of experts to cover everything you need to know about TURN servers in WebRTC.

When something breaks in a real-time application, users know immediately. There's no grace period, no hiding behind a loading spinner. Voice, video, chat, and live data platforms demand a higher standard of maintenance and support than traditional web applications. The model you choose to deliver that support has lasting consequences for uptime, scalability, and customer trust. For CTOs, engineering leaders, product managers, and operations teams evaluating hourly support vs a Managed Service Provider (MSP), this

Monitoring call quality in a WebRTC application is harder than it looks. You need consistent telemetry, enough context to interpret what you're seeing, and dashboards that are actually useful when something goes wrong in production. This post covers how we integrated Peermetrics into an Amazon IVS Real-Time Streaming application to get reliable, per-session call quality monitoring, including the specific wrapper approach that makes it work given how IVS exposes WebRTC connections. This is a follow-up

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 conversations over WebRTC. At WebRTC.ventures, we have seen clients ask for self-hosted and on-premise Voice AI architectures that they can control, audit, and tune for their own performance