How to Build a Real-Time AI Endpoint with Server-Sent Events (SSE)

Real-time responses are important in many AI apps. Whether it’s a chatbot or a live transcription tool, you often don’t want to wait for the full res...

Real-time responses are important in many AI apps. Whether it’s a chatbot or a live transcription tool, you often don’t want to wait for the full response—you want it to stream in as it’s being generated.

One simple way to do that is by using Server-Sent Events (SSE). Unlike WebSockets, SSE is designed for one-way communication—from server to browser—and it's easier to implement.

Let’s go through what you need to do to set up a real-time AI stream using SSE.

What You'll Need

A backend server (can be in Node.js, Python, Go, etc.)
A way to send partial AI responses (e.g. as the model generates text)
A frontend that can display incoming messages in real time

Step-by-Step: Building a Real-Time SSE Endpoint

Step 1: Add a new endpoint in your backend

This is a URL path (like /stream) that your frontend will connect to. It needs to:

Stay open (don’t close the connection after one message)
Send data in small chunks (like one message or token at a time)
Use the correct headers so the browser knows it’s a stream

The goal: this endpoint continuously sends messages over time—like "Thinking...", "Processing...", and then the final AI output.

Step 2: Make sure the browser knows it's a stream

Browsers don’t treat every response as a stream. You have to:

Set the right response headers (e.g. content type for streaming)
Avoid buffering or caching (so messages arrive instantly)

Different programming languages have different ways to handle this, but the idea is the same: treat the connection as live and continuous, not a one-time reply.

Step 3: Handle it on the frontend

In the frontend, instead of using fetch() (which waits for a complete response), use a browser API called EventSource.

It allows the frontend to:

Open a connection to your /stream endpoint
Receive each message as it's sent
Display messages one by one in the UI

You don’t need a lot of setup here—just make sure you're listening for incoming data and updating the interface as messages arrive.

What You Get in the End

Once your setup works:

Your frontend connects to /stream
Your backend sends partial messages over time
Your app looks more responsive, like ChatGPT or Copilot streaming tokens

You don’t need fancy frameworks or real-time sockets. SSE is simple, works over HTTP, and is supported by most browsers.

Isolation vs. Shared Architecture in AI Development

When working with AI code—especially with tools like v0 or any prompt-based generators—there’s a strong temptation to "make it reusable."

You might think:

“Let’s create shared handlers, schemas, and a generic response module so we can use this across multiple endpoints.”

Here’s the problem: you're not building an API product. You're building one endpoint that streams a reply. And if you're moving fast, shared architecture is a trap.

You end up with files you can’t trace
Refactoring becomes the main task instead of building
-A small change breaks three other things

Instead, build small, isolated pieces that work on their own. Especially when using AI codegen—copy/paste is safer than premature abstraction.

This article breaks it down with a real-world example. A developer tried building a shared system for a simple SSE response. It backfired, added complexity, and wasted hours. Isolation would have solved everything.

Start small. If you find yourself copy-pasting the same thing five times later, then refactor. Not before.

Final Thoughts

Building real-time AI responses doesn’t have to be complicated. With Server-Sent Events, you can create fast, simple, and effective streaming endpoints without reaching for heavyweight tools.

Just remember: Keep it isolated. Keep it working. Refactor later.

That mindset—especially when working with AI tools—will save you time, bugs, and headaches.

Community

How to Build a Real-Time AI Endpoint with Server-Sent Events (SSE)

What You'll Need

Step-by-Step: Building a Real-Time SSE Endpoint

Step 1: Add a new endpoint in your backend

Step 2: Make sure the browser knows it's a stream

Step 3: Handle it on the frontend

What You Get in the End

Isolation vs. Shared Architecture in AI Development

Final Thoughts

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