LiveKit is an open-source WebRTC platform and managed cloud service for building realtime audio, video, data, and AI-agent applications. Its core is a Selective Forwarding Unit (SFU) that routes media between participants; the wider stack adds signaling, connectivity, SDKs, authentication, agent orchestration, telephony, and managed operations. That makes it more than a browser WebRTC wrapper, but it does not remove the need to choose between running the infrastructure yourself and paying for LiveKit Cloud.
What is LiveKit?
LiveKit is a toolkit for building realtime applications in which people, devices, or AI agents exchange audio, video, and data. It combines an open-source server with client and server SDKs, developer tooling, an AI-agent framework, and an optional managed cloud platform.
The central component is LiveKit Server, a distributed WebRTC SFU. A client publishes audio or video tracks to a LiveKit room, and the SFU forwards selected tracks to other participants who subscribe to them. LiveKit also handles signaling and connectivity tasks that an application would otherwise need to build around WebRTC.
LiveKit Server is open source under the Apache 2.0 license. The project is written in Go and uses Pion WebRTC. LiveKit’s documentation describes it as a server that “orchestrates realtime communication between end users and agents.”
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Is LiveKit an SFU, or a complete WebRTC stack?
The SFU is the core
An SFU receives media from participants and forwards it to the appropriate subscribers without mixing every stream into one. LiveKit’s server supports features including selective subscription, simulcast, moderation APIs, webhooks, end-to-end encryption, and data tracks for low-latency data such as telemetry or teleoperation. It also provides UDP, TCP, and TURN connectivity options.
The rest of the stack fills in application infrastructure
Calling LiveKit an end-to-end stack means it covers much more than the SFU itself. The project includes signaling and connectivity, authentication using JWTs, APIs, client and server SDKs, and tooling for running and managing applications. LiveKit Cloud extends that foundation with hosted infrastructure and services for agents, telephony, inference, analytics, recordings, transcripts, traces, and log drains.
It is not a replacement for every part of an application. Developers still build their product’s user experience, business logic, integrations, and policies. LiveKit supplies the realtime communication foundation and related infrastructure.
How does LiveKit handle WebRTC traffic?
Direct peer-to-peer versus an SFU
With direct peer-to-peer WebRTC, each participant may need to upload a separate copy of their media to several other participants. In an SFU design, a participant generally sends one upstream copy to the server, which forwards it to subscribed participants. This can simplify the upload burden on clients as rooms grow, while shifting forwarding work to the server.
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The actual bandwidth and capacity effects depend on room topology, codecs, simulcast settings, and which tracks participants subscribe to. There is no single bandwidth multiplier that applies to every application.
Track selection and adaptation
LiveKit routes tracks based on subscriptions and can adjust parameters such as resolution or bitrate in response to subscriber bandwidth. Simulcast can make multiple quality layers available for the same source, allowing the system to forward a layer suited to a subscriber’s conditions. Adaptive behavior helps manage changing network quality; it cannot guarantee that every participant will have a smooth connection on a poor or unstable network.
What can you build with LiveKit?
Realtime audio, video, and data applications
The platform is suitable for applications such as calls, meetings, interactive broadcasts, and realtime device communication. Data tracks can carry low-latency application data, including telemetry and teleoperation signals. The open-source project lists SDKs for browser, Swift, Android, Flutter, React Native, Rust, Node.js, Python, Unity, ESP32, and C++, as well as server APIs and UI components.
Realtime AI agents
LiveKit Agents lets Python or Node.js programs join rooms as full participants. An agent can receive a user’s audio or other input, respond in realtime, and exchange media or data with people in the room. The framework includes speech-to-text, language-model, and text-to-speech pipelines; turn detection; interruption handling; tool use; multimodal input and output; provider plugins; and multi-agent handoffs.
For production deployments, the Agents framework also provides agent-server orchestration and load balancing, supports Kubernetes, and can work with telephony. The framework supplies the realtime participant and orchestration layer; the behavior and capabilities of an agent still depend on the model, tools, and application logic chosen by its developer.
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Should you self-host LiveKit or use LiveKit Cloud?
Both options use the LiveKit server and APIs, but they assign operational responsibility differently. Self-hosting gives a team more direct control over infrastructure and data handling. Cloud takes on server management and adds managed services. Compare them across the requirements that will affect your application:
| Decision area | Self-hosted LiveKit | LiveKit Cloud |
|---|---|---|
| Operations | Your team runs and upgrades the server, handles scaling, observability, TURN and networking operations, and responds to incidents. | LiveKit manages the service infrastructure and offers managed agent deployment and operational tools. |
| Deployment options | The project supports a single binary, Docker, and Kubernetes; official Docker images and Helm charts are available. | LiveKit operates the hosted service; you use the Cloud platform with the same APIs and SDKs. |
| Regions and latency | You choose infrastructure locations and are responsible for deployment and routing decisions. | LiveKit documents a global mesh SFU, nearest-edge connections, and region pinning. |
| Data residency | You control where your infrastructure and data are hosted, subject to your own architecture and obligations. | Region pinning is documented as an option for data residency; verify that the available regions and service behavior meet your requirements. |
| Scaling and reliability | Your team plans and operates capacity and availability. LiveKit documentation says self-hosted rooms support up to approximately 3,000 users per room; this is a vendor-stated figure, not an independent benchmark. | LiveKit documents no stated room-user limit and a 99.99% uptime target. These are vendor claims, not independently audited measurements. |
| Additional managed services | You choose and operate the supporting services you need. | Cloud adds services including built-in inference, native telephony, analytics, transcripts, traces, recordings, and log drains. |
| Cost model | Infrastructure and the people and systems needed to operate it are your responsibility. | Usage-based billing can include transport, agent sessions, telephony, inference, and observability; the cost depends on what you use. |
Choose self-hosting when control is the priority
Self-hosting can suit teams that need control over regions, infrastructure changes, or data handling and have the skills to run realtime systems. The trade-off is operational ownership: the team must plan capacity, manage upgrades and networking, monitor service health, and handle incidents.
Choose Cloud when you want managed operations
LiveKit Cloud is the more direct fit when you want LiveKit to manage the server infrastructure and want its integrated global routing, agent deployment, telephony, inference, and observability options. Before committing, check that its region choices and service configuration meet your latency, residency, and compliance needs.
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Make the decision using your full workload
Compare the total cost and operational fit across media transport, agent sessions, telephony, inference, and observability—not just the SFU. Also account for your expected regions, traffic patterns, reliability requirements, data handling rules, and the engineering effort required to operate a self-hosted deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much does LiveKit Cloud cost?
LiveKit Cloud uses usage-based billing, and its displayed calculator values can change. The pricing page displayed the following example per-minute rates on October 3, 2026. The plan and provider selections behind those displayed examples are not identified here, so treat them as calculator examples rather than universal or guaranteed rates.
| Displayed usage category | Example rate shown |
|---|---|
| Agent sessions | $0.0100 per minute |
| Telephony | $0.0100 per minute |
| LLM option | $0.0014 per minute |
| STT option | $0.0075 per minute |
| TTS option | $0.0090 per minute |
| Observability | $0.0100 per minute |
Those line items do not by themselves establish the total bill. LiveKit’s billing documentation says transport services—including WebRTC media, SIP trunking, stream import, and recording or export—are metered using a combination of time and data transfer. Agents deployed to LiveKit Cloud are metered by session time in one-second increments, with a ten-second minimum per session. Invoices are issued monthly.
Check the current pricing page and billing terms for your selected plan, providers, and usage before estimating a deployment. A workload with long calls, frequent agent sessions, telephony, or recordings can have a different cost profile from one that mainly uses media transport.
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What should you evaluate before building on LiveKit?
- Room shape: Estimate participant counts, publishing patterns, and track subscriptions. These determine how the SFU forwards media and what capacity your deployment needs.
- Network conditions: Consider client network variability, required connectivity methods, and whether you need TURN or TCP options.
- SDK coverage: Confirm that LiveKit’s SDKs support the client platforms and languages your product requires.
- Operational ownership: Decide who will monitor, scale, upgrade, and troubleshoot the service if you self-host.
- Regions and data handling: Confirm that your chosen deployment approach satisfies latency, residency, and compliance requirements.
- Agent dependencies: For voice or multimodal AI, account for the agent framework, model and speech providers, tool integrations, and the services that will be billed.
- Cost at expected usage: Model transport, session time, telephony, inference, and observability together, then verify current billing terms.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
