By Sagar Shankaran, Founder of CallSphere
SSE quietly became the default for LLM token streaming in 2026. Here is when to use it, when to reach for WebSocket, and what AI SDKs actually do under the hood.
Key takeaways
If your AI app is "user types prompt, server streams tokens," use SSE. If it is "user and server have an ongoing conversation with audio, tools, and interruption," use WebSocket. The middle ground is smaller than it looks.
flowchart LR
Twilio["Twilio Media Streams"] -- "WS · μlaw 8kHz" --> Bridge["FastAPI Bridge :8084"]
Bridge -- "PCM16 24kHz" --> OAI["OpenAI Realtime"]
OAI --> Bridge
Bridge --> Twilio
Bridge --> Logs[(structured logs · OTel)]Because LLM token streaming follows a request-then-stream pattern, which is exactly what SSE was designed for. The user POSTs a prompt; the server responds with text/event-stream and pushes tokens as they generate. The connection closes when generation finishes. There is no ongoing bidirectional state.
Under the hood, OpenAI's SDK, Anthropic's SDK, and the Vercel AI SDK all use SSE. It works through every corporate proxy because it is plain HTTP. It survives firewalls. It auto-reconnects. It does not require any special infrastructure beyond an HTTP server. For 95% of LLM-powered features, SSE is correct.
The decision is mostly about whether the client also needs to send things mid-response:
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| Need | SSE | WebSocket |
|---|---|---|
| Server streams tokens to client | Yes | Yes |
| Client interrupts mid-response | Hard | Native |
| Bidirectional audio | No | Yes |
| Ongoing presence/typing state | Possible | Native |
| Auto-reconnect built into protocol | Yes | DIY |
| Works through every proxy | Yes | Mostly |
| Browser API simplicity | Higher | Higher footgun count |
The cost of WebSocket is operational. The cost of SSE is "I cannot send back to the server on the same connection." For a chatbot that streams answers, SSE wins. For a voice agent with real-time interruption, WebSocket is the only realistic answer.
CallSphere uses both, deliberately:
/api/chat/stream. The client POSTs a message, the server streams the AI response. Reconnection is handled by EventSource. Simple, debuggable.This pattern keeps the chat surface dirt simple while reserving WebSocket complexity only for paths that genuinely need it.
export async function POST(req: Request) {
const { messages } = await req.json();
const stream = new ReadableStream({
async start(controller) {
const enc = new TextEncoder();
const completion = await openai.chat.completions.create({
model: "gpt-4o-mini",
messages,
stream: true,
});
for await (const chunk of completion) {
const tok = chunk.choices[0]?.delta?.content ?? "";
controller.enqueue(enc.encode(`data: ${JSON.stringify({ tok })}\n\n`));
}
controller.enqueue(enc.encode("event: done\ndata: {}\n\n"));
controller.close();
},
});
return new Response(stream, {
headers: { "Content-Type": "text/event-stream", "Cache-Control": "no-cache" },
});
}
Cache-Control: no-cache and X-Accel-Buffering: no (for nginx) so intermediaries do not buffer the stream.Does SSE work in mobile WebViews? Yes — every modern WebView ships EventSource. Older Android WebViews need a polyfill.
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Can SSE handle voice? No, not practically. SSE is text-only and one-direction. Voice means WebSocket or WebRTC.
Does HTTP/3 change anything? SSE benefits significantly — multiplexed streams over QUIC are faster and more robust. Use HTTP/3 if your edge supports it.
Does the Vercel AI SDK use SSE? Yes. useChat() hits an SSE endpoint by default.
What about Server Components streaming in React? That uses a custom RSC protocol, but it is layered on the same SSE-like chunked transport.
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Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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