Streaming LLM Responses with SSE: A Practical Guide

## Why streaming matters

A chat product feels much faster when the first words appear immediately. Server-Sent Events let the browser receive a long model answer in small chunks instead of waiting for the whole completion. With ClaudeN AI you can use the familiar OpenAI streaming pattern across Claude, GPT and Gemini with one API key and one base URL.

## Python streaming example

``python
from openai import OpenAI

client = OpenAI(api_key="YOUR_CLAUDEN_KEY", base_url="https://clauden.ai/v1")

resp = client.chat.completions.create(
model="claude-3-5-sonnet",
messages=[{"role": "user", "content": "Explain this in plain English."}],
stream=True,
)

for chunk in resp:
print(chunk.choices[0].delta.content or "", end="", flush=True)
``

## Production tips

Keep the API key on your backend, proxy the stream to the browser, use a longer timeout for streaming routes, and record token usage after the final chunk. Streaming improves perceived latency without changing your model choice. Sign up for ClaudeN AI and use the $5 free credit to test real streaming workloads.