LiveKit is an open-source WebRTC infrastructure project built around a Selective Forwarding Unit media server, client and server SDKs, and an Agents framework purpose-built for real-time AI voice and video applications. It has become one of the most commonly adopted infrastructure choices specifically for teams building AI voice agents, due largely to that Agents framework and an active open-source community.
This guide covers what LiveKit actually is, whether it is genuinely open source and what that means for cost, how its architecture works, how it compares to other WebRTC infrastructure options, and how to decide between self-hosting and a managed partner.
What Is LiveKit?
LiveKit is an open-source WebRTC infrastructure project providing a Selective Forwarding Unit media server, client and server SDKs for building real-time video and audio applications, and an Agents framework specifically designed for building AI voice and video agents.
LiveKit is a WebRTC infrastructure project built around three core components: a Selective Forwarding Unit, or SFU, that handles media routing for real-time video and audio calls, client and server SDKs that developers use to build applications on top of that infrastructure, and an Agents framework specifically designed for building AI-driven voice and video agents.
The project is widely used across real-time applications generally, video conferencing, live streaming, and interactive audio experiences, but it has gained particular traction specifically within AI voice agent development, where the Agents framework provides purpose-built tooling for connecting speech-to-text, a language model, and text-to-speech into a coherent, real-time conversational pipeline.
Is LiveKit Really Open Source? What's Free vs What's Paid
LiveKit's core server is genuinely open source and self-hostable without a licensing fee, typically under an Apache 2.0 license. LiveKit Cloud is a separate, paid managed hosting offering from the company behind the project, an open-core business model common among infrastructure projects of this kind.
Is livekit open source is worth answering precisely rather than with a simple yes or no, since the project follows what is commonly called an open-core model.
The core LiveKit server is genuinely open source, typically licensed under Apache 2.0, meaning the source code is publicly available and the software can be self-hosted without paying a licensing fee to use it.
LiveKit Cloud is a separate, paid offering, the managed hosting service the company behind the project operates commercially. Choosing LiveKit Cloud means paying for the operational convenience of not managing the infrastructure yourself, not for access to the underlying software itself, which remains open source regardless.
This distinction matters directly for cost planning. A team with the infrastructure operations capacity to self-host incurs no licensing cost for the software itself, only the actual infrastructure and operational cost of running it. A team choosing LiveKit Cloud pays specifically for that managed convenience.
How LiveKit's Architecture Works
LiveKit's architecture centers on a Selective Forwarding Unit that receives each participant's media stream once and forwards the appropriate streams to other participants, avoiding the inefficiency of a full mesh topology where every participant connects directly to every other participant.
LiveKit's core architectural choice is the Selective Forwarding Unit model, which addresses a specific scaling limitation inherent to simpler WebRTC topologies.
Full mesh topology, where every participant connects directly to every other participant, scales poorly as participant count grows, since each additional participant requires every existing participant to establish an additional direct connection.
The SFU model, which LiveKit implements, has each participant send their media stream once to the server, which then selectively forwards the appropriate streams to the other participants who need them, removing the connection count problem full mesh topology creates.
Client SDKs handle the application-facing integration work, connecting to the LiveKit server and managing the local media capture and playback a specific application needs. Server SDKs provide backend control over room management, participant permissions, and recording, giving a developer programmatic control over the call infrastructure beyond what the client alone exposes.
Self-Hosting LiveKit vs Using LiveKit Cloud
Self-hosting LiveKit offers full infrastructure control, including SFU scaling and geographic placement decisions, at the cost of owning the operational responsibility directly, while LiveKit Cloud trades that control for reduced operational burden, similar to the broader self-hosted versus managed infrastructure tradeoff common across WebRTC deployments.
This tradeoff mirrors the same self-hosted versus managed decision covered in our guide to WebRTC TURN servers, applied here to the SFU layer specifically.
Self-hosting gives a team full control over SFU configuration, geographic placement, and scaling decisions, at the cost of owning capacity planning, monitoring, and ongoing maintenance directly, requiring genuine infrastructure operations capacity on the team.
LiveKit Cloud removes that operational burden, offering managed hosting without requiring the team to deploy and scale SFU infrastructure themselves, at the cost of less direct control over the underlying deployment and an ongoing service cost tied to usage.
A team with existing infrastructure operations experience and specific geographic or configuration requirements may reasonably self-host. A team prioritizing faster deployment without dedicated infrastructure operations capacity is typically better served by LiveKit Cloud or a managed implementation partner.
LiveKit vs Other WebRTC Infrastructure Options
LiveKit competes with platforms such as Agora, Dailyco, and Twilio, each offering a different tradeoff between proprietary managed infrastructure and open-source self-hosting flexibility, with LiveKit's open-source availability and Agents framework as its most distinct differentiators.
Platform | Core Model | Notable Differentiator |
|---|---|---|
LiveKit | Open-source, self-hostable, with a paid managed cloud option | Open-source availability and a purpose-built Agents framework for AI voice and video |
Agora | Proprietary managed infrastructure | Established global network with broad developer ecosystem |
Dailyco | Managed WebRTC platform | Developer-friendly API focused on quick integration |
Twilio | Programmable communication APIs, including video | Broad communication API ecosystem beyond video alone |
LiveKit's open-source availability is its clearest structural differentiator from Agora, Daily.co, and Twilio, none of which offer a comparable self-hostable option. For teams specifically building AI voice agents, LiveKit agents framework is a further differentiator none of these alternatives offer in a directly comparable form.
LiveKit for AI Voice Agents: Why It's Become a Default Choice
LiveKit has become a common default choice for AI voice agent infrastructure specifically because its Agents framework provides purpose-built tooling for connecting speech-to-text, a language model, and text-to-speech into a real-time conversational pipeline, rather than requiring a team to build that integration layer from scratch.
LiveKit's adoption for AI voice agent development specifically traces directly to its Agents framework, which provides tooling built around exactly the pipeline our guide to STT integration for AI agents describes: connecting speech-to-text, a language model, and text-to-speech into a coherent, low-latency conversational flow.
Building that integration layer from scratch on top of a generic WebRTC implementation requires solving streaming coordination, interruption handling, and latency budget management independently. LiveKit's Agents framework addresses much of this directly, which is a meaningful part of why it has become a common infrastructure choice specifically within the AI voice agent development community, reflected in the project's active open-source contributor base and community engagement around this specific use case.
What You Need to Self-Host LiveKit
Self-hosting LiveKit requires server infrastructure capable of running the SFU at the expected scale, a TURN server for NAT traversal fallback, and the operational capacity to monitor and scale that infrastructure as call volume grows.
A few infrastructure components are required before self-hosting LiveKit in production.
Server infrastructure sized for expected call volume. The SFU needs compute and bandwidth capacity matched to expected concurrent participant count, not just an initial testing configuration.
A TURN server for NAT traversal fallback. Covered in depth in our guide to WebRTC TURN servers, this remains necessary for any production WebRTC deployment, LiveKit-based or otherwise, to handle connections that cannot establish directly.
Monitoring and scaling capability. Ongoing visibility into SFU performance and the operational capacity to scale infrastructure as usage grows, rather than discovering capacity limits only once real production load arrives.
Common LiveKit Deployment Challenges
Common LiveKit self hosting challenges include underestimating SFU scaling requirements as concurrent usage grows, overlooking TURN server integration as a separate required component, and insufficient monitoring to catch capacity or performance issues before they affect real users.
Underestimating SFU scaling requirements. A deployment sized for initial testing volume can struggle once real concurrent usage grows, particularly for group calls with many simultaneous participants.
Treating TURN integration as an afterthought. TURN server deployment is a separate, required component for production reliability, not something LiveKit's SFU handles on its own, a gap covered directly in our dedicated TURN server guide.
Insufficient production monitoring. Without visibility into SFU performance and connection success rates, capacity or configuration issues often surface only after they have already affected real users, rather than being caught proactively.
Enterprise Use Cases: LiveKit Deployment by Context
LiveKit deployment priorities differ by context. AI voice agent platforms, live streaming and social applications, and telehealth platforms each weigh the Agents framework, scaling characteristics, and reliability requirements differently.
AI Voice Agent Platforms
Problem: Teams building AI voice agents need real-time coordination between speech-to-text, a language model, and text-to-speech, a pipeline that requires significant custom integration work if built from scratch on generic WebRTC infrastructure.
Solution: LiveKit's Agents framework provides purpose-built tooling for this specific pipeline, reducing the custom integration work required compared with building the coordination layer independently.
Outcome: Development teams reach a working AI voice agent pipeline faster, leveraging tooling purpose-built for this exact coordination problem rather than solving it independently from a generic WebRTC starting point.
Live Streaming and Social Applications
Problem: Applications supporting large numbers of simultaneous viewers or participants need media routing that scales efficiently, since a full mesh topology becomes impractical well before reaching meaningful audience sizes.
Solution: LiveKit's SFU architecture handles this scaling requirement directly, routing media efficiently without requiring every participant to maintain a direct connection to every other participant.
Outcome: Applications scale to larger participant and viewer counts than a full mesh topology would practically support, without requiring the development team to build custom media routing infrastructure.
Telehealth Platforms
Problem: Telehealth platforms need reliable, scalable video infrastructure with the option for deep customization around clinical workflow requirements that a fully proprietary managed platform might not expose.
Solution: LiveKit's open-source availability allows a telehealth platform to self-host or work with an implementation partner for deep customization, rather than being constrained to a proprietary platform's fixed feature set.
Outcome: Telehealth platforms gain the infrastructure customization flexibility clinical workflow requirements often demand, without being limited to a closed, proprietary alternative's predefined configuration options.
Decision Tree: Should You Self-Host LiveKit or Use a Managed Partner?
RTC LEAGUE LiveKit Deployment Readiness Framework v1.0
A four-step framework for preparing a LiveKit deployment, covering infrastructure capacity planning, TURN integration, Agents framework evaluation for AI voice use cases, and monitoring setup, in the order they should be addressed before production launch.
Deploying LiveKit without this sequence typically produces a system that works in initial testing but encounters scaling or reliability gaps once real production usage begins. The RTC LEAGUE LiveKit Deployment Readiness Framework v1.0 orders the process correctly.
Step 1: Plan infrastructure capacity against realistic concurrent usage. Size SFU infrastructure for expected peak concurrent participants, not initial testing volume alone.
Step 2: Integrate TURN server infrastructure explicitly. Deploy and configure TURN as a required, separate component following the practices covered in our dedicated TURN server guide.
Step 3: Evaluate the Agents framework for AI voice use cases specifically. For AI voice agent deployments, assess how directly the Agents framework addresses your specific pipeline coordination needs before building custom integration work independently.
Step 4: Establish monitoring before launch, not after. Implement visibility into SFU performance and connection success rates proactively, rather than discovering capacity or configuration issues only once real users are affected.
Outcome: Following this sequence produces a LiveKit deployment validated for real production conditions, rather than one tested only against initial development-scale usage.
RTC LEAGUE's LiveKit Expertise
RTC LEAGUE builds AI voice agent infrastructure with direct, deep operational experience in LiveKit's ecosystem specifically, reflected in the team's standing within the LiveKit open-source community.
RTC LEAGUE's infrastructure work is led by a CTO holding the top ranking on the LiveKit Global Community Leaderboard, reflecting sustained, direct operational experience within LiveKit's specific ecosystem rather than general WebRTC familiarity alone. This depth of experience informs how RTC LEAGUE approaches the capacity planning, TURN integration, and Agents framework evaluation covered throughout this guide.
For teams evaluating whether to self-host LiveKit independently or work with an experienced implementation partner, this specific, demonstrated depth of LiveKit operational experience is a direct, relevant differentiator rather than a general claim of WebRTC capability.
Conclusion and Recommendation
LiveKit's core server is genuinely open source and self-hostable without a licensing fee, with LiveKit Cloud as a separate, paid managed hosting option, an open-core model that gives teams real flexibility in how they deploy it. Its SFU architecture, client and server SDKs, and Agents framework together have made it a common default choice specifically for AI voice agent infrastructure.
Self-hosting offers full control at the cost of real operational responsibility, including SFU scaling, TURN integration, and ongoing monitoring, while LiveKit Cloud or a managed implementation partner trades that control for reduced operational burden. Compared with Agora, Daily.co, and Twilio, LiveKit's open-source availability and purpose-built Agents framework remain its clearest structural differentiators.
The clearest recommendation: evaluate LiveKit based on whether your team has genuine infrastructure operations capacity for self-hosting, and whether your specific use case, particularly AI voice agent development, benefits directly from the Agents framework rather than a general-purpose video infrastructure alternative.
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