Effortless Scaling: LiveKit for Thousands of Concurrent Users

What do you do if your LiveKit app jumps from a couple of dozen users to thousands of connections at once? In this scenario, some servers may become overloaded and crash or run extremely slowly if there is no adequate load balancing.

So what is the fix?

By understanding how LiveKit Scales, your infrastructure will be ready for explosive user growth! Proper distribution of nodes and your server will ultimately reduce operational expenses and downtime. The result? Your users will all be overjoyed to have a flawless experience.

In this guide, we will break down exactly how to scale your LiveKit deployment to support massive audiences.

Understanding the LiveKit SFU Scaling Model

LiveKit’s design is centered around the Selective Forwarding Unit (SFU). For scaling, you need to know how this works. Traditional media servers typically decode the streams, mix them, and then re-encode them before sending them to users. This results in high latency and high CPU load.

SFU is different. It acts as a smart router rather than a media mixer. When a user streams the video, the SFU grabs the data packet and then forwards it directly to other viewers according to the user’s network conditions and subscriptions. As there is no media transcoding involved in SFU, it maintains low latency and significantly reduces the server’s CPU load.

According to LiveKit documentation, the SFU is horizontally scalable: you can run it on one or 100 nodes with identical configuration. The nodes route peer-to-peer via Redis, so clients that join a room are guaranteed to connect to the same node. Running LiveKit as a single node requires no external dependencies, but Redis is required for distributed, multi-node configurations. LiveKit has several powerful features, such as:

• Dynacast: server-side dynamic publishing of simulcast.
• Scalable Video Coding: SVC layers based on subscriber demand.
• AdaptiveStream: client-side dynamic subscription based on rendered video size.

Using approaches such as Simulcast and Scalable Video Coding (SVC), the platform guarantees that people on bad connections receive lower-resolution streams and the best-quality video for those with good connections. This flexible layering is an absolute prerequisite for scaling, since it prevents network congestion from degrading the experience for average participants.

Important note: LiveKit supports WebRTC Insertable Streams, through which E2EE can be implemented. The data is encrypted on the sender's device and decrypted only on the receiver's device. Since the SFU merely relays the encrypted packets and does not need to decode them, E2EE incurs almost zero performance overhead on the server side, making the server highly scalable.

Horizontal Scaling and Room Distribution Strategies

When the number of users exceeds the capability of your single server, you need to scale out your infrastructure. LiveKit has a distributed mesh network architecture for horizontal scaling. In this design, a single point does not become a central bottleneck. Rather, a collective of LiveKit nodes is responsible for the playing field.

LiveKit uses Redis to control the state in the distributed environment. Redis holds the real-time state of rooms, participants, and available nodes. The system queries Redis to determine which node hosts that particular room when a new user wants to join a session.

Successful room allocation relies on the following strategies:

1. Geographical proximity: Having nodes in multiple regions brings your media server closer to end users, reducing physical data travel and latency.

2. Node capacity awareness: The routing layer considers a node's load (CPU and bandwidth usage) before assigning new participants, so that a server is not overwhelmed.

3. Mesh routing: For larger rooms, LiveKit can shard participants across multiple nodes and route streams between those servers in the background, so users get a seamless experience.

Load Balancing Across LiveKit Nodes

When a client initiates a connection, they first hit an API endpoint over HTTPS to join a room. The load balancer then forwards this initial signaling request to one of the LiveKit nodes. After accepting the request, the node will provide the client with specific IPs and ports to send the actual media.

To more evenly balance the load across thousands of users, try the following:

• Use DNS routing to direct the user to the local (closest) regional data center.
• Use L4 load balancers (such as HAProxy and AWS NLB) that efficiently manage the routing of UDP packets without the need to inspect the payload.
• Make sure you are distributing your signaling (WebSocket) and media (UDP) traffic correctly. The vast majority of your bandwidth will be media traffic, but keep in mind that signaling uses TCP/WebSocket, and media uses UDP (with TCP/TLS fallback via tcp_port for restrictive networks).

Autoscaling Patterns for Cloud-Native Setups

Consider the following autoscaling best practices:

• Metric selection: CPU utilization is the primary scaling signal (target ~60–70%); you should monitor memory, but it is rarely the binding constraint for an SFU.
• Scaling velocity: Scale up rapidly when facing a sudden surge of users, but scale down more slowly to avoid ending calls prematurely.
• Node pools: With your cloud provider (e.g., AWS EKS or Google GKE), leverage auto-provisioning node pools that dynamically provision virtual machines to your cluster when your current pod count exceeds your hardware capacity.

Managing Large Rooms vs. Many Small Rooms

The approaches to scaling differ by application type. A single stream with 5,000 viewers who are just watching demands a very different architecture from that of 1,000 independent rooms, each with 5 active users.

Many Small Rooms

A virtual healthcare consultation or private language tutoring is an example of an application with many small rooms. So this is relatively easy for you to scale. The load balancer evenly distributes the rooms across all nodes in the pool. If a node goes down, only users in the specific rooms it was hosting are affected, and they can quickly reconnect to a different server.

Large Broadcast Rooms

Solutions such as interactive webinars or large-scale live streams have a different bottleneck. For very large broadcasts, a single node's egress bandwidth becomes the bottleneck long before the CPU does. To address this, LiveKit employs distributed rooms. The main broadcaster connects to a core node, and the media stream is forwarded internally to secondary nodes. The viewers connect to these secondary nodes to consume the stream. This will require you to carefully set up your mesh network so the internal server-to-server traffic doesn’t become a bottleneck.

Cost Implications of Large-Scale Real-Time Systems

Running a voice or video service for thousands of users every day has real financial consequences. You have to walk a fine line between the serviceability requirements and running costs.

Bandwidth Costs

In a cloud computing environment, outbound data transfer is typically the most costly component. Cloud providers Amazon Web Services (AWS) and Google Cloud charge for egress traffic by the gigabyte. When you have a platform with thousands of users, such fees can run to the tens of thousands of dollars per month. To work around this, use the adaptive bitrate presets to decrease quality (and therefore bandwidth) for users on mobile devices or small displays.

Self-Hosted vs. Managed Cloud

You can deploy LiveKit in two ways. Hosting the open-source software yourself on bare-metal servers or cloud infrastructure allows you to maintain full control and avoid dependence on any vendors. Many bare-metal providers also offer unmetered bandwidth, which, at scale, can significantly reduce operational costs.

Or you can use a managed service such as LiveKit Cloud that hides the complexity of Kubernetes clusters, Redis databases, and global node distribution. While your per-minute costs may be higher than those of self-hosting on bare metal, you are saving a tremendous amount on DevOps salaries and infrastructure maintenance time.

Scalable LiveKit Architecture Design with Clover Dynamics

By using a decentralized SFU model, managing your deployments with Kubernetes, and knowing your resource limitations intimately, you'll be able to build communications tools that are as performant as the largest enterprise platforms on the planet.

However, if you need help with this task, contact Clover Dynamics for professional LiveKit integration and consulting services. We offer tailored solutions that leverage LiveKit’s WebRTC infrastructure to create innovative real-time video and audio platforms; expert consulting and implementation services to align infrastructure with user demands; cross-platform integration for web and mobile applications, ensuring consistent real-time communication; security-focused solutions with end-to-end encryption and adherence to standards like HIPAA and GDPR, ensuring data protection for sensitive applications, and much more.

From the high-level architectural design to seamless media server production, we will ensure your systems are designed and implemented to meet real-time performance and scalability requirements.