So, you’ve seen the magic of conversational AI in action, like the voice feature in ChatGPT, and you're ready to build something similar for your own product. That’s a fantastic goal. But as you start digging into the technical side, you’ll quickly hit a major fork in the road: should you connect to a Realtime API using WebSockets, or is it better to build out a proper architecture with WebRTC?
This isn’t just a technical detail to gloss over. The choice you make here will define how well your app performs, how secure it is, and how much it costs to run. This guide is here to clear up the confusion in the Realtime API vs WebRTC debate. We’ll walk through the differences, the good, the bad, and where each one shines, so you can pick the right path for your project.
The foundational technologies explained
Before we pit them against each other, let's get a quick handle on what these two technologies actually are. They might sound like they do the same thing, but they work in very different ways.
What is a Realtime API?
A Realtime API is a broad term for any setup that lets a client (like a web browser) and a server have a live, two-way conversation. When we talk about voice AI, this almost always means using WebSockets. The protocol behind WebSockets is called TCP (Transmission Control Protocol), and it’s a stickler for the rules. It makes sure every single piece of data gets to its destination, in the right order, no exceptions.
Take OpenAI's Realtime API as an example. It's incredibly capable, but the "real-time" part can be a bit tricky. The API often fires back the AI's audio in big, fast bursts. This means your application is suddenly left holding the bag, responsible for catching all that audio, buffering it, and playing it back smoothly without any weird pauses or glitches for the user.
What is WebRTC?
WebRTC stands for Web Real-Time Communication. It’s an open-source project designed for one job: enabling super-fast, low-latency audio, video, and data communication right inside a web browser. If you’ve used Google Meet or hopped on a Discord voice chat, you’ve used WebRTC.
Unlike WebSockets, WebRTC’s main protocol is UDP (User Datagram Protocol). UDP values speed over absolute perfection. Think of it like a normal conversation, if you miss one word, you don’t stop everything and ask the person to start their sentence over, you just keep going. This is perfect for voice, where a tiny, unheard blip is way better than a long, awkward silence while your app waits for a lost packet of data to be sent again.
Even though people often call it peer-to-peer, WebRTC still needs a "signaling" server to act as a matchmaker, helping your browser and the AI backend find each other to start the call. This makes the initial handshake a bit more complex than a simple WebSocket connection.
Key differences in performance and reliability
The biggest point of contention in the Realtime API vs WebRTC showdown comes down to how they deal with a live conversation on the messy, unpredictable public internet.
Latency and packet loss: TCP vs UDP
Let’s circle back to the TCP vs. UDP difference, because it’s the heart of the matter.
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WebSockets (TCP) are like sending a carefully written letter. Every word has to be received in the exact order it was written. If one page gets lost in the mail, the whole process stops until a replacement arrives. This is great for loading a webpage or sending a file, but it’s a recipe for disaster in a voice call. It’s the source of that frustrating lag and choppiness that makes a conversation feel unnatural.
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WebRTC (UDP) is like a phone call. If the line crackles for a second and you miss a word, you both just keep talking without breaking the flow. This ability to brush off minor packet loss is why WebRTC feels so much more responsive and immediate, especially if your user is on a spotty Wi-Fi or mobile connection.
Client-side complexity
One of the most underestimated headaches of using a Realtime API directly is the sheer amount of work it dumps onto your application. Your client-side code suddenly has to become an expert in:
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Audio Engineering: Juggling incoming chunks of audio to make sure playback is smooth and uninterrupted.
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Live Transcription: If you're showing what the AI is saying, you have to sync the text perfectly with the audio as it plays.
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Interruption Handling: What if the user starts talking over the AI? Your app has to catch that, stop the AI’s audio, and tell the API exactly when the user cut in so the AI knows what was actually heard.
This adds a ton of complex code to your app. A WebRTC-based architecture avoids this mess by moving that work to a backend server. Your app’s only job is to handle a clean audio stream, making it lighter, faster, and way easier to manage across web and mobile.
Network resilience
WebRTC was built for the internet's chaos. It has tools baked in to adjust to changing network conditions, smooth out "jitter" (when data packets arrive out of order), and correct errors. It's designed to survive bad internet weather. WebSockets, on the other hand, aren't nearly as tough. A flaky connection can quickly turn a good user experience into a laggy, frustrating one.
Architecture and security considerations
Beyond just performance, how you structure your app has huge consequences for security and your control over the user experience.
Direct client-to-API vs. mediated architecture
There are really two ways to build your voice AI app:
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The Direct Route: The user's browser connects straight to the AI provider's Realtime API. This is easy to get up and running for a quick test.
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The Mediated Route: The user's browser uses WebRTC to connect to your backend server. Your server then talks to the AI provider on the user's behalf. This is more work to set up but is the professional standard.
Security implications
The direct route has a massive, deal-breaking security flaw: you have to put your secret API key into the client-side code. That's like leaving your house key under the doormat. Anyone with a little technical skill can find it, steal it, and start making API calls on your dime, potentially running up a huge bill.
A mediated architecture completely solves this. Your secret API keys stay locked away on your secure backend. The user's browser only gets a temporary token to join the WebRTC session. For any real-world application, this is a must-have.
Building and maintaining this kind of secure, mediated infrastructure is a serious engineering project. This is where platforms like eesel AI are a huge help. They provide the pre-built, optimized infrastructure that deals with all the messy parts of real-time communication, security, and AI integration, so you can focus on building your app's features instead of reinventing the plumbing.
When to use which approach
So, after all that, which one should you choose? It really comes down to what you’re building.
| Use Case | Direct Realtime API (WebSocket) | Mediated WebRTC Architecture |
|---|---|---|
| Hobby Project / Internal PoC | Good fit. It's simple enough to get an idea off the ground quickly. | Overkill. The setup is too complex for a simple test. |
| Production Application | Not recommended. It's a recipe for performance issues and major security risks. | Best practice. This is how you ensure reliability, security, and a great user experience. |
| App Needing Server-Side Control | Very limited. You can't easily manage sessions, control costs, or add your own logic. | Required. This is essential for adding business logic, VAD, and tracking usage. |
| Multi-Participant Conferencing | Not suitable. WebSockets aren't designed for group calls. | The standard. WebRTC is the technology that powers modern group calls. |
The hidden cost factor
It's easy to forget that APIs from providers like OpenAI are expensive, and they often charge for every second of audio you send, even silence. Every time a user pauses to think, you're still paying.
A mediated architecture gives you a secret weapon against this cost: Voice Activity Detection (VAD). You can run VAD on your server to figure out when the user is actually talking and only send that audio to the AI. This one trick can slash your API costs.
For companies that want to launch a production-ready voice agent without the engineering headaches, a managed solution is usually the smartest financial move. eesel AI not only gives you the strong WebRTC infrastructure but also connects directly with helpdesks like Zendesk and knowledge sources like Confluence, turning a complex engineering problem into a simple setup process.
Understanding the cost models
As you start to budget, you need to know about the three main ways costs can stack up when building a voice AI app.
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Raw API Costs: If you use a Realtime API directly, you pay a usage-based fee, usually per minute of audio. This can be almost impossible to predict. A busy month could leave you with a shockingly high bill, making it tough to plan your finances.
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DIY Infrastructure Costs: Building your own mediated WebRTC setup isn't free. You have to pay for servers on AWS or Azure, budget for ongoing maintenance, and, most importantly, cover the salaries of the engineers needed to build and run it. These hidden costs can easily add up to more than the raw API fees.
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Managed Platform Pricing: The third path is to use a managed platform that bundles all the infrastructure and API access into one predictable subscription. This approach gets rid of surprise API bills and the heavy cost of maintaining your own system.
Unlike the wild swings of usage-based billing or the hidden costs of a DIY project, platforms like eesel AI offer transparent, predictable pricing. With plans based on a clear number of monthly interactions and no per-resolution fees, you can grow your AI support without dreading the end-of-month bill. This lets you budget with confidence and focus on your return on investment.
Making the right choice for your voice AI application
The takeaway here is pretty clear: for any serious, user-facing application, a mediated architecture using WebRTC is the better choice for performance, security, and growth. A direct connection to a Realtime API is really only for quick-and-dirty prototypes or internal tools where those things don't matter as much.
At the end of the day, your choice is between building all this complex infrastructure yourself or using a platform that has already solved these hard problems for you.
Go live in minutes, not months, with eesel AI
Why spend months wrestling with complex infrastructure when you can have all the benefits of a powerful, secure WebRTC architecture right out of the box? You can skip the build phase and go live in minutes. eesel AI is a fully-managed platform that hooks into your existing tools, learns from your knowledge bases, and lets you deploy smart voice and text AI agents with just a few clicks. You can even simulate how it will perform with your own historical data to roll it out with total confidence.
Ready to see how easy building a production-grade AI agent can be? Start your free trial today.
Frequently asked questions
The core difference lies in their underlying protocols. Realtime APIs often use WebSockets (TCP), which prioritizes guaranteed data delivery, while WebRTC uses UDP, which prioritizes speed and tolerates minor packet loss, making it ideal for real-time voice.
WebRTC generally provides a smoother, lower-latency experience due to its UDP protocol, which handles lost data packets better than TCP-based Realtime APIs. This avoids the noticeable lag and choppiness often associated with TCP for live voice.
Yes, a direct Realtime API connection can expose your API keys on the client side, posing a major security risk. A mediated WebRTC architecture, where your backend handles API communication, keeps keys secure and is essential for production.
A direct Realtime API is generally suitable only for quick hobby projects or internal proofs-of-concept where security and performance are less critical. For any production-grade application, a mediated WebRTC architecture is the recommended approach.
A direct Realtime API pushes significant audio engineering and synchronization responsibilities onto the client. WebRTC, especially with a mediated architecture, offloads much of this complexity to the backend, simplifying client-side code.
Absolutely. Direct Realtime API usage can lead to unpredictable, high costs as you pay for all audio, including silence. A mediated WebRTC setup allows for Voice Activity Detection (VAD) on your server, which can drastically reduce API costs by only sending active speech.
