As AI-powered meeting bots—from transcription assistants to real-time coaching tools—become essential in remote work and hybrid collaboration environments, the need for live audio-video access has surged.
These intelligent bots rely heavily on streaming protocols to ingest and process data from platforms like Zoom, Microsoft Teams, and Google Meet.
However, the choice of streaming protocol can significantly impact the bot’s responsiveness and performance.
Two dominant options for real-time streaming are WebRTC and RTMP, each with its strengths and trade-offs. The wrong choice could lead to delayed responses, compatibility issues, or inefficient scaling.
In this guide, we’ll break down the differences between WebRTC vs RTMP for live meeting bot applications.
Whether your bot provides real-time transcription, instant sentiment feedback, or archives entire meetings for compliance, you’ll learn how to select the right protocol.
Additionally, we’ll demonstrate how MeetStream.ai streamlines the process by providing built-in support for both, allowing developers to create powerful, adaptable bots.
What is WebRTC and How It Works for Live Streaming
WebRTC (Web Real-Time Communication) is an open-source project that enables real-time peer-to-peer media communication directly in browsers and mobile applications.
It supports audio, video, and data channels, and it’s the backbone of many live communication platforms.
Why It’s Ideal for Real-Time Applications:
- Ultra-low latency: Often under 300ms, WebRTC enables bots to perform live transcription, instant keyword detection, or real-time sentiment analysis with minimal delay.
- Browser-native support: Works in Chrome, Firefox, Safari, and Edge without any plugins, making it seamless for web-based bots.
- Secure by design: Uses DTLS and SRTP to encrypt streams, supporting compliance and data security needs.
- Built for interactivity: Perfect for use cases like sales coaching, language learning bots, or interactive Q&A assistants.
WebRTC Architecture Considerations:
- Peer-to-peer complexity: While suitable for 1:1 or small group interactions, scaling WebRTC requires SFUs (Selective Forwarding Units) or MCUs (Multipoint Control Units).
- Challenging NAT traversal: Developers must deal with ICE negotiation, STUN/TURN servers, and connection reliability in varied network environments.
- Infrastructure needs: Scaling for large meetings requires robust signaling and media relay infrastructure.
Despite these complexities, WebRTC remains the protocol of choice for bots requiring real-time responsiveness, interactive feedback, and browser-based deployment.
What is RTMP and Why It’s Still Used
RTMP (Real-Time Messaging Protocol), originally developed by Adobe for Flash video, is still widely used today—especially for live broadcasts, stream archiving, and server-based streaming workflows.
Strengths of RTMP in Bot Development:
- Stable, server-centric streaming: RTMP streams are pushed to a centralized server and then pulled by multiple consumers—ideal for 1-to-many distribution.
- Easy to scale: Compatible with CDN infrastructure, cloud storage, and streaming servers like NGINX-RTMP, Wowza, or Media Server Frameworks.
- Widely supported tools: Integrates with tools like OBS Studio, FFmpeg, and streaming platforms like YouTube Live, Facebook Live, and Twitch.
RTMP Limitations:
- Higher latency: Typically 2–5 seconds, which makes it unsuitable for real-time interactivity.
- Not browser-native: Modern browsers have deprecated Flash, requiring transcoding or a JavaScript player like HLS.js or Dash.js to play RTMP-based streams.
- More suitable for recording & analytics: Best used in scenarios where real-time processing is not essential.
Despite being legacy tech, RTMP remains relevant for recording meetings, broadcasting, or delayed AI analysis, especially when scalability and interoperability matter more than latency.
How MeetStream Supports Live Streaming via Both WebRTC and RTMP
MeetStream.ai removes the complexity of choosing a single protocol by offering native support for both WebRTC and RTMP.
Whether you need real-time bot interaction or post-call analytics, MeetStream allows developers to build bots tailored to specific streaming needs—or both.
With platform-agnostic integrations and developer-first design, MeetStream empowers bots to operate across Zoom, Teams, and Google Meet without writing protocol-specific logic.
Developer-Centric APIs for Full Streaming Control
MeetStream offers flexible APIs and streaming SDKs that allow bots to:
- Initiate or switch between WebRTC and RTMP modes dynamically.
- Adapt behavior based on meeting state (e.g., coaching mode vs archival mode).
- Stream to multiple destinations—browser clients, analytics backends, cloud recorders—simultaneously.
This flexibility enables developers to build bots that are context-aware, resource-efficient, and highly scalable—without being locked into one protocol.
Real-Time Intelligence with WebRTC
When latency and responsiveness matter most, WebRTC is your best bet. Bots built with MeetStream’s WebRTC integration can:
- Tap into live audio-video with sub-second latency.
- Perform real-time transcription, emotion detection, or automated summarization during the meeting.
- Deliver live prompts or coaching cues in time-sensitive scenarios.
With MeetStream handling ICE negotiation and network traversal, developers can focus on writing Python or Node.js logic to consume, process, and act on live media streams—without worrying about the complexity of WebRTC under the hood.
Scalable Archiving and Analytics with RTMP
For use cases like meeting recording, compliance logging, or video-based post-processing, RTMP is a better fit. MeetStream allows bots to:
- Push RTMP streams to cloud storage, media servers, or compliance dashboards.
- Forward video to third-party services or machine learning pipelines for delayed analysis.
- Offload media encoding and routing to MeetStream’s infrastructure for better scalability.
This makes RTMP ideal for bots that:
- Don’t need low-latency feedback.
- Focus on archival, post-event transcription, or third-party integrations.
Which Protocol Should You Choose for Your Meeting Bot?
Here’s a quick breakdown of WebRTC vs RTMP based on your bot’s purpose:
| Feature | WebRTC | RTMP |
| Latency | ~100–500 ms | 2–5 seconds |
| Ideal Use Cases | Real-time transcription, live coaching, event triggers | Recording, compliance, analytics |
| Browser Compatibility | Native support | Not natively supported |
| Scaling | Needs SFU/MCU for large groups | Easy 1-to-many broadcasting |
| Developer Effort | Higher due to NAT and ICE | Lower, simpler to set up |
| Audio/Video Quality | Adaptive bitrate, low-latency | High quality but higher delay |
If your bot needs instant feedback, live voice processing, or interactive coaching, choose WebRTC.
If your goal is streaming to servers, cloud storage, or video archiving, go with RTMP.
Hybrid Streaming Architecture: Best of Both Worlds
Many modern bots combine both protocols to get the advantages of real-time feedback and post-meeting analysis.
With MeetStream’s hybrid support, you can:
- Use WebRTC for real-time features like live alerts or speaker coaching.
- Simultaneously stream via RTMP for video archiving, compliance, or integration with third-party systems.
- Dynamically switch protocols depending on the meeting state—e.g., WebRTC during the call, RTMP after the call ends.
Example Use Case:
A sales coaching bot uses WebRTC to monitor tone and suggest responses in real-time. After the call, it switches to RTMP to send the full recording to the CRM system and perform in-depth voice analytics.
This dual-mode architecture ensures that your AI bot remains both agile and comprehensive, without the need to re-architect your infrastructure.
Conclusion
When comparing WebRTC vs RTMP, the decision often comes down to your bot’s primary function.
- WebRTC is best for real-time, low-latency, browser-friendly interactivity.
- RTMP is reliable for scalable distribution, recording, or analytics where some latency is acceptable.
But with MeetStream.ai, you don’t have to lock into just one. Our platform uses socket-based streaming under the hood, giving developers the flexibility to combine the strengths of both protocols in a unified framework.
Build intelligent bots that process speech in real time, store media for long-term use, and scale effortlessly—all on a single platform.
