The Complete Guide to WebRTC Scalability

What is WebRTC Scalability?

WebRTC scalability is the ability of your system to handle more users, video sessions, or streaming load without sacrificing quality or latency. WebRTC is designed for real-time communication — and scaling real-time infrastructure requires a different mindset than scaling on-demand video like YouTube or Netflix.

For example:

• 1-on-1 video call? Works well out of the box.
• 1-to-1,000 webinar? Needs a well-architected media server.
• 100 live classrooms with 50 students each? You’ll need orchestration, auto-scaling, and monitoring. 

Scalability isn’t just about performance — it’s about planning ahead so that your system doesn’t break under pressure.

If you’re new to WebRTC or want a deeper technical dive, check out our complete guide on how WebRTC works.

Why WebRTC is hard to Scale

WebRTC was built for direct peer-to-peer communication, not for massive broadcast at scale. While that makes it lightning fast, it introduces technical challenges.

Challenges with Scaling WebRTC:

• Every connection is two-way (bi-directional), meaning bandwidth grows exponentially.
• No CDN support: Unlike HLS or MPEG-DASH, WebRTC can’t be cached at the edge.
• Each connection eats CPU: The more participants, the more processing is needed.
• High sensitivity to network quality: Packet loss, jitter, or latency can break streams

Example – In a peer-to-peer (mesh) video call with 6 participants, each user has to manage 5 separate video streams — both upload and download. That’s 30 total streams for 6 people!

Without smart routing (like SFU), things fall apart fast.

Different Architectures: Mesh, SFU, MCU

There are different core routing strategies used in WebRTC, like:

1. Mesh

• Every user connects directly to every other user.
• Simple but non-scalable.
• Maxes out quickly due to browser and bandwidth limitations.

2. SFU (Selective Forwarding Unit)

• Each user sends one stream to the server.
• The server intelligently forwards streams to other participants.
• Efficient and scalable for 1-to-many or many-to-many scenarios.

3. MCU (Multipoint Control Unit)

• The server decodes and mixes all streams, then sends one mixed stream.
• High CPU use, but ideal for recording or legacy support.

SFU is the sweet spot — scalable, cost-efficient, and the default architecture for Ant Media’s real-time workflows.

Scaling WebRTC: Vertical vs Horizontal

Vertical Scaling

• Add more resources (CPU, memory, bandwidth) to your server.
• Simple to start, but limited.
• Great for development, testing, and small production loads.

Horizontal Scaling

• Distribute traffic across multiple servers.
• Involves clustering, load balancing, and orchestration.
• Can support tens of thousands of concurrent users.

Ant Media Server supports both vertical and horizontal scaling — you can start with one node and scale up or out based on demand.

How Ant Media Server Tackles WebRTC Scalability

Ant Media Server offers built-in features to help you scale WebRTC applications from tens to tens of thousands of users.

• SFU-based architecture for efficient stream routing.
• Clustered deployments for load distribution.
• Auto-scaling on cloud platforms (AWS, Azure, GCP).
• Kubernetes and Helm chart support for containerized orchestration.

Ant Media doesn’t just give you the building blocks — it gives you ready-made tools to scale without reinventing the wheel.

Clustering in Ant Media Server

Ant Media supports clustering with both manual and cloud-based configurations. A basic cluster includes:

• Origin node: Accepts published streams. These nodes perform various tasks such as transcoding (converting streams to different formats or bitrates) and transmuxing (changing the container format of the stream).
• Edge nodes: Deliver streams to viewers. Unlike origin nodes, edge nodes do not ingest streams or perform tasks such as transcoding or transmuxing. Their sole purpose is to fetch the stream from an origin node and forward it to the viewers, ensuring efficient distribution of content.
• Load balancer: Distributes load to the appropriate edge. The load balancer acts as the entry point for both viewers and publishers. It receives user requests and intelligently directs them to an appropriate node in either the origin or edge group, based on the current load and availability of resources.
• Central Database: The database is central to the AMS cluster, storing all stream-related information. This data includes bitrates, settings, the origin node of the stream, and additional metadata necessary for stream management.

You can build WebRTC clusters either on-premises or in the cloud using Ant Media Server. For Kubernetes users, a ready-to-use Helm chart simplifies deployment.

Cluster Setup Guide – Learn how to configure, deploy, and scale your streaming infrastructure efficiently using Ant Media’s clustering architecture.

Load Balancing Strategies

Ant Media Server integrates with:

• NGINX: Fast and easy to configure.
• HAProxy: More flexible routing rules.
• Cloud Load Balancers: Use region or latency-based logic.

Each strategy supports intelligent stream distribution for high availability.

For region-based WebRTC scalability, deploy clusters in different zones and let your load balancer route users to the nearest one.

Autoscaling in Cloud Environments

Need to scale automatically based on user load or CPU usage?

Ant Media offers plug-and-play auto-scaling templates for:

These templates monitor load and dynamically add/remove nodes as needed — helping your WebRTC Scalability to scale efficiently and cost-effectively.

WebRTC Scalability Scenarios

Scaling Toolkit for WebRTC with Ant Media Server

Conclusion: Build Small, Think Big

WebRTC scalability used to be hard. With Ant Media Server, it’s now simple, affordable, and ready to go.

Whether you’re streaming a 10-person classroom or a global event with 100,000 attendees — Ant Media helps you grow without friction.

Start lean. Scale when needed. Pay only for what you use.

FAQs on WebRTC Scalability