Choosing a Video SDK: A Head-to-Head Comparison

Video calling might look simple from the outside. You have two people, a camera each, and a connection between them.

Underneath, it's one of the harder things to get right.

Encoding video efficiently, adapting to bad networks in real time, routing traffic to keep latency low across the world, and scaling from two participants to two thousand is no easy feat. (And not all providers get it right.)

This piece walks through nine video SDKs to help you decide on the right one for your use case. We start with the architecture choices that separate them, then break down each one on its own.

What is a Video SDK?

A video SDK is a set of tools that let you add live video calling to your app, without building the underlying technology yourself.

That underlying technology includes things like: turning a camera feed into a video stream, sending that stream to other participants with low delay, adjusting video quality automatically when someone's network gets worse, and connecting users who are behind different types of firewalls or routers.

Most video SDKs give you client libraries (for iOS, Android, web, and other platforms), server-side APIs, and often some pre-built UI components. Some give you all of this out of the box. Others give you just the core building blocks and expect you to build your own interface.

Video SDKs can also extend to:

• Audio rooms. Voice-only group spaces, similar to a live podcast or a Clubhouse-style room, without any video.
• Livestreaming and broadcast. One person or a small group presenting to a large audience, rather than everyone talking to everyone.
• Voice AI agents. An AI system that joins a call as a participant, able to listen and respond in real time, rather than a person on the other end.
• Video KYC. Live video used to verify someone's identity, common in banking and finance apps.
• Virtual classrooms and events. Video calling built around a specific format, like a class with a teacher and students, or a scheduled online event.

Why Use an SDK?

AI coding tools have made it easy to build a basic video call in an afternoon. Give a tool like Claude or Codex a prompt, and you can get two devices talking to each other with surprisingly little effort. This creates a real temptation to skip the SDK and just build it yourself.

The problem is that a working demo and a production-ready video feature are not the same thing.

Eventually, you'll run into:

• A call needs to be secure. Encrypting video and audio properly, and meeting standards like HIPAA or GDPR if you're in healthcare or handling EU users, takes real security work.
• Someone says or shows something they shouldn't. Video calls need the same kind of moderation as chat or comments, i.e., a way to detect and act on harmful content, whether that's automated or a human reviewing flags.
• A user's network gets worse mid-call. Adjusting video quality automatically, in real time, so the call doesn't freeze, is a separate, hard problem from getting a call connected in the first place.
• Two devices can't reach each other directly. Firewalls and routers block a lot of direct connections. Getting around this reliably needs extra server infrastructure (TURN and STUN servers).
• You need more than two or three people on a call. Connecting a handful of people is very different from connecting hundreds, and the approach that works for a demo breaks down fast at scale.
• A browser updates. Browsers change how they handle video and audio fairly often. Someone has to keep watching for this and keep fixing it.
• Real-time AI agents need near-zero transport delay. A growing number of video and voice products now put an AI agent directly into the call. For that to feel natural rather than awkward, the underlying video and audio transport has to add almost no delay of its own. Any lag in getting audio to and from the AI model stacks on top of however long the AI itself takes to think and respond, and the two add up fast.
• Users expect AI features. Noise cancellation, background blur, and live transcription are basically standard now. Building these yourself means training your own machine learning models.

None of this is a reason AI coding tools are bad. It's a reason the easy part (getting a demo working) and the hard part (keeping video reliable for real users, indefinitely) are two very different problems.

What to Consider Before You Choose

When evaluating a video SDK, it helps to understand why one costs more, handles scale differently, or even performs better on poor network connections than another.

Consider these architectural decisions before choosing a vendor.

WebRTC vs. Proprietary Protocol

Most video SDKs are built on WebRTC, an open standard supported by every major browser. Some vendors use their own protocol instead.

• WebRTC: Wide compatibility, less vendor lock-in, since no single company owns the standard
• Proprietary protocol: Can sometimes perform better, since the vendor controls every part of it, but ties you more closely to that one vendor

How the SDK Connects Multiple People on a Call

Two people on a call can usually connect directly. More than that, and direct connections get expensive fast:

Past a certain size, direct connections between every device stop being realistic, and the SDK needs a server in the middle instead.

There are two common approaches:

• SFU (Selective Forwarding Unit): A server receives one stream per person and forwards it to everyone else. This is the more common approach today, since it uses less server processing
• MCU (Multipoint Control Unit): A server combines everyone's video into a single stream before sending it out. It's less common now, but simpler for some use cases.

Keep in mind, there's a tradeoff. True end-to-end encryption, where not even the vendor's own servers can see the call content, is difficult to achieve in an SFU architecture. Getting real end-to-end encryption on top of an SFU requires encrypting the media at the frame level before it ever reaches the server, so the SFU only ever handles encrypted data it can route but not read. Not every vendor implements this, so if end-to-end encryption is a requirement, you should ask directly rather than assuming an SFU-based SDK provides it by default.

How the SDK Handles Bad Networks

Not every user has a strong, stable connection. Two things determine what happens when a connection gets shaky:

• Adaptive bitrate: The SDK checks the network in real time and lowers video quality automatically, instead of letting the call freeze or drop.
• Codec support: This is the method a device uses to shrink a video down so it can be sent over the internet, then rebuild it on the other end so it can be watched. Not every device supports every codec.

Where the Servers Are

Video has to travel from one person to another, and physical distance adds real delay.

SDKs with servers spread across many regions can route traffic through the closest one, keeping delay low worldwide. On the other hand, SDKs operating from only a few locations will show worse call quality for users far from those locations, even if everything else about the SDK is solid.

How Recording and Transcription Work

Recording a call has to happen on a server, not just on someone's device, since you can't rely on a user's device to reliably store and process an entire call.

Some SDKs include recording and transcription as a built-in feature, while others treat it as a separate paid product or don't offer it at all.

Head-to-Head: 9 Video SDKs

Before we break down each SDK, let's consider how each stacks up against the others.

Stream Video: Best for apps that need scale & reliability

Stream Video is built on WebRTC with an SFU architecture, running across Stream's own global edge network. Its main architectural differentiator is Dynascale, a system that continuously adjusts resolution, codec, frame rate, and bandwidth for each participant individually, in real time, rather than applying one setting to an entire call.

Stream also offers Vision Agents, a separate open-source SDK for building real-time voice and video AI applications on top of the same video infrastructure.

Architecture in detail:

• Under Dynascale, a participant whose video is shown as a small thumbnail gets automatically downscaled to save bandwidth, and scales back up the moment that participant becomes the active speaker or is shown larger on screen.
• Stream has demonstrated scaling a single WebRTC-based livestream to 100,000 concurrent participants while maintaining stable frame rates and low latency.

• Video is one of several Stream products (alongside Chat, Activity Feeds, and AI Moderation) that share the same underlying user model and permissions system, so adding video requires comparatively little additional integration work. Note: AI moderation covers live video and audio, so you can flag inappropriate behavior in real time.
• Stream includes built-in support for real-time transcription and meeting summaries as part of the core video product rather than as a separate add-on.

Where the limitations are:

• Stream Video is a fully managed product with no self-hosted or open-source option. Teams that need to run their own infrastructure directly, whether for strict data residency, regulatory requirements, or wanting to avoid per-participant-minute pricing entirely at very high volume, would need to look at an open-source, self-hostable option like LiveKit instead.
• Pricing is quote-based above the self-serve tiers, so a team comparing per-participant-minute costs across this list cannot slot Stream in without talking to sales.

Twilio Video: Best for teams already using Twilio for SMS or WhatsApp

Twilio Video is built on WebRTC using a standard SFU architecture. Each participant's device publishes its video and audio once to Twilio's media server, which clones that stream and forwards it to every other participant in the room.

This is why a participant's upload bandwidth and battery use stay roughly the same, no matter how many people are in the call. The device only ever sends one copy of its own stream, regardless of room size.

Architecture in detail:

• Twilio's docs list up to 50 concurrent participants in the standard "Group Rooms" mode.
• For smaller calls, Twilio also offers a lighter peer-to-peer mode ("Go Rooms") capped at 3 video participants (plus up to 10 audio-only), which skips the SFU server entirely and connects devices directly. It's lower cost, but it doesn't scale past a handful of people.
• Twilio supports simulcast, meaning a device sends its stream at several quality levels at once, so the SFU can forward a lower-quality version to participants on weak connections without asking the sender to re-encode.
• Because the SFU has to decrypt and re-encrypt media in order to route it, Twilio Video isn't end-to-end encrypted. Twilio's own servers can technically access the raw media stream in transit, which matters if you're evaluating this for a use case with strict data-privacy requirements.

Where the limitations are:

• There's no pre-built call interface included. You're building your own UI on top of the raw API, which is more frontend work than UI-kit-first competitors like Daily or 100ms require.
• Recording isn't a single feature. You enable "track recordings," then separately need the Compositions service to merge those individual recordings into one watchable file. It's two things to configure instead of one.
• Twilio has publicly wavered on this product's priority. The company announced it was sunsetting Video as a standalone product, then reversed that decision, which is a reasonable thing to ask about if long-term product commitment matters for your use case.

Agora: Best for apps needing massive-scale live broadcast alongside calling

Agora built its own transport network, called SD-RTN, rather than routing everything over standard WebRTC. It's a private overlay network layered on top of the public internet, which Agora uses to route media between participants instead of relying purely on browser-native WebRTC paths.

This is a meaningfully different choice from most of the other vendors here, since Agora controls more of the path media takes from end to end.

Architecture in detail:

• Agora's SFU architecture supports scaling from a 1:1 call up to millions of viewers in its Interactive Live Streaming mode, using a role model where most participants join as "audience" and specific users get promoted to "co-host" when they need to publish audio or video.
• Agora reports its network carries 80 billion or more minutes of voice and video monthly, and operates in over 200 countries and regions.
• An Extensions Marketplace lets teams add AI noise suppression, AR facial effects, and 3D spatial audio without building that processing themselves.
• Agora holds ISO 27001, ISO 27701, SOC 2, and supports GDPR, CCPA, and HIPAA compliance requirements.

Where the limitations are:

• Because Agora's core transport is proprietary rather than open WebRTC, teams take on more dependency on Agora specifically. Migrating off Agora later means replacing more of the underlying transport than it would with a WebRTC-native vendor.
• Agora ships fewer pre-built UI components than UI-kit-first competitors like Daily or 100ms, so teams that want a ready-made call interface rather than building their own should expect more frontend work.

Daily: Best for teams wanting a fast, well-documented WebRTC integration

Daily is built on standard WebRTC with an SFU architecture, and much of its public documentation focuses on making that architecture legible to developers rather than hiding it behind a black box.

Media servers run across 10 geographic regions and 30 availability zones, which Daily says gets first-hop network latency down to 13 milliseconds or less for a large share of the world's population.

Architecture in detail:

• Daily supports adaptive simulcast, sending video at multiple quality layers so the SFU can forward a lower layer to participants on weaker connections without needing to re-encode anything.
• Small calls can run in a serverless, peer-to-peer mode with no SFU involved at all, and Daily lets developers control the exact point at which a call switches over to SFU mode as participants join, using a configurable switchover threshold.
• Daily's Interactive Live Streams support up to 100,000 active participants in a single real-time session, according to Daily's own published specifications.
• Daily has built a separate framework (used for products like Daily Bots and Pipecat) specifically for connecting AI voice and vision agents into live sessions as participants.

Where the limitations are:

• Daily has been focused on AI voice agent tooling in the last couple of years, which is worth noting if your use case is pure video calling rather than an AI-agent-adjacent product, since some of their newest engineering attention sits elsewhere.
• Recording and live-streaming output (RTMP and HLS) both require server-side compositing, which adds a processing step and some latency compared to raw peer-to-peer media.
• Daily's media servers have a smaller regional footprint than Agora's SD-RTN network, which operates in over 200 countries and regions. Teams with a significant user base in less-covered areas should check Daily's specific region list against their own user distribution.

LiveKit: Best for teams wanting an open-source, self-hostable core

LiveKit is the only vendor on this list that's fully open-source (Apache 2.0 licensed) with a genuinely self-hostable SFU, alongside a managed LiveKit Cloud option for teams that don't want to run their own infrastructure.

It's also built its Agents framework directly into the core product, letting developers add Python or Node.js AI agents into a room as full real-time participants rather than bolting AI on as an afterthought.

Architecture in detail:

• LiveKit's SFU handles standard interactive video calling, and its Agents framework adds a structured pipeline for speech-to-text, language model processing, and text-to-speech, so an AI agent can join a call and interact with human participants in real time.
• LiveKit Cloud runs on a distributed mesh of media servers rather than a single region, so a call is not pinned to one data center. LiveKit does not publish a concurrent-session ceiling for Cloud beyond its plan tiers.
• Because LiveKit is open source, teams can inspect, modify, or fork the SFU itself rather than working entirely through an opaque API.
• LiveKit supports direct SIP telephony integration, so an agent or participant can be reached by an ordinary phone call rather than only through an app.

Where the limitations are:

• Self-hosting LiveKit means taking on real operational work: running your own SFU cluster, TURN servers, and monitoring, which is a genuine engineering commitment rather than a one-line integration.
• Teams that want a fully managed, zero-ops experience end up on LiveKit Cloud anyway, at which point some of the "open source, self-host it yourself" appeal becomes optional rather than the default path.

100ms: Best for teams wanting one SDK that covers both calls and live streaming

100ms combines WebRTC-based video calling and HLS-based live streaming in a single SDK, rather than treating those as two separate products that a team has to integrate independently.

It also abstracts a lot of the manual bitrate and resolution tuning that developers would otherwise have to write themselves, through a feature it calls "auto-tune."

Architecture in detail:

• 100ms's auto-tune system dynamically adjusts bitrate and resolution per participant based on real-time network conditions, so developers don't have to hand-write that logic themselves.
• Roles (such as host, speaker, or viewer) are configured from a dashboard rather than in code, and each role determines what a participant can publish or subscribe to in a given room.
• A single 100ms room can scale up to 1,000 hosts for interactive calls, or up to 10,000 attendees when used in live-streaming mode.
• 100ms supports post-call transcription and AI-generated summaries as part of its core feature set, alongside more standard capabilities like recording and RTMP output.

Where the limitations are:

• 100ms runs on cloud infrastructure rather than operating its own dedicated global media network the way Agora or Stream do, which is worth asking about directly if consistent global latency at scale is a top priority.
• 100ms has repositioned itself around healthcare conversational AI, describing itself as "previously known for powering real-time video." The video SDK repos are still actively maintained, but ask directly about roadmap commitment before building on it.

Vonage Video: Best for teams needing flexible session sizes without picking a resolution tier

Vonage Video API is one of the longer-running products in this comparison, and it shows in how deliberately it's engineered around flexible session sizes rather than fixed pricing or participant tiers. It supports two distinct connection modes depending on session size and feature needs.

Architecture in detail:

• Routed mode sends media through Vonage's servers, which is required for recording, archiving, and the AI-powered Audio Connector and Media Processor tools. Relayed mode attempts a direct peer-to-peer connection instead, cutting latency but giving up every server-side feature.
• Vonage's Adaptive Media Routing evaluates each publisher's subscriber count individually. If a publisher only has one subscriber and no advanced features are active, that specific stream can be relayed peer-to-peer even within an otherwise routed session.
• Vonage documents up to 15,000 total participants in broadcast-style use, with a soft ceiling of 25 simultaneous active publishers before per-stream costs increase, since more concurrent publishers strain every viewer's device.

Where the limitations are:

• Vonage's AI capabilities are built around exporting audio out to external services (via its Audio Connector) for transcription, translation, or captioning, rather than an integrated framework for building AI agents that join a session as participants, the way LiveKit or Daily provide. Teams wanting an AI participant in the call itself, not just processing on the side, would need to build that orchestration layer themselves.
• Pushing past 25 active publishers is technically possible (up to 55), but it demands real client-side performance planning, since every additional publisher adds decode load to every viewer's device.

Zoom Video SDK: Best for teams wanting Zoom's infrastructure without the Zoom meeting interface

Zoom Video SDK gives developers access to the same underlying global infrastructure that runs Zoom Meetings and Webinars, but without any of Zoom's own branded UI.

This is a distinct product from Zoom's separate Meeting SDK, which embeds Zoom's actual meeting interface into a third-party app, and from the Zoom Apps SDK, which builds apps that run inside Zoom itself. Teams evaluating "Zoom" for a video integration should be clear on which of these three they actually need.

Architecture in detail:

• Per Zoom's SDK documentation, rendering limits vary by platform: up to 49 simultaneous videos on native platforms, 25 on desktop browsers like Chrome and Edge, and only 4 on mobile browsers.
• Zoom Video SDK provides raw access to audio, video, and screen-share data on all platforms, which developers can feed into custom AI pipelines for transcription, analysis, or other processing.
• Realtime Media Streams (RTMS), a newer capability, exposes live session media directly to external services, supporting use cases like real-time clinical documentation or CRM updates during a call.
• Recording, live transcription, and translation are all available as part of the platform, alongside livestreaming output to services like YouTube and Facebook Live.

Where the limitations are:

• The browser rendering caps (25 videos on desktop, 4 on mobile) meaningfully constrain any use case built around a large gallery view, specifically on the web, compared to native mobile or desktop apps.
• Some Android WebView environments lack support for SharedArrayBuffer, which disables certain features, including background noise suppression and higher resolution video, in those specific contexts.
• The Video SDK shares infrastructure with Zoom's consumer product but sits behind three similarly named developer products (Video SDK, Meeting SDK, Apps SDK), so confirm in writing which one your contract and support cover.

Zegocloud: Best for teams wanting a wide feature set (chat, effects, AI agents) bundled with video

Zegocloud runs its own proprietary global network of more than 500 edge nodes across over 200 countries, and builds a notably broad product catalog around that core video and voice infrastructure, including in-app chat, AI-driven visual effects, and full AI avatar products.

Architecture in detail:

• Zegocloud's own published specs support up to 10,000 video participants in a single call, and up to 10,000 users in group voice chat.
• A "Digital Human" product layers an AI-generated avatar, with lip-sync and gestures, directly on top of Zegocloud's voice and video infrastructure.
• Zegocloud's Conversational AI Agent product is designed to combine chat, voice, and video into a single AI-driven interaction, rather than treating those as separate integrations.
• The platform includes an AI Noise Suppression and voice-enhancement layer alongside more standard video effects like background segmentation and beautification filters.

Where the limitations are:

• Zegocloud publishes less architectural detail (specifics on SFU versus MCU behavior, codec support, or network recovery behavior) than several competitors on this list, which makes it harder to evaluate exactly how it behaves on a bad connection without testing directly.
• The breadth of the product catalog, spanning chat, effects, digital humans, and conversational AI, means teams that only need straightforward video calling will need to actively scope down which parts of the platform they actually use.

Where This Leaves You

These nine video SDKs are built around different problems.

Some prioritize massive broadcast scale, some prioritize a fast, prebuilt integration, some are built specifically for AI voice agents, and a couple hand you the whole stack open source so you can run it yourself.

Next up, test these out yourself. The "Best for" line under each name is meant to get you to the right two or three worth testing directly, and then we recommend exploring the free tiers where available.

And, as always, happy building.