# Streaming OpenAI API Responses: How It Works, in Python, Django & FastAPI

*Originally published on the [Djangix blog](https://djangix.com/blog/streaming-openai-api-responses/). This is a condensed version — the full article, with complete code, is linked at the end.*

A non-streamed chat answer leaves the user staring at a spinner for the whole generation. Streaming changes the feel, not the total time: the first tokens arrive in under a second and the answer appears to type itself.

## What is actually sent

The transport is Server-Sent Events — one HTTP response that stays open and delivers small messages. Each message carries a JSON chunk, and the useful text lives in a tiny delta field. Most chunks are fragments, not whole words, so the client simply concatenates them until a final done marker arrives.

## Going through your own backend

Browsers should not call the provider directly, because that would expose your API key. So your server receives the chunks and re-emits them — and that middle step is where many implementations break.

- **Wrap each token as JSON.** Raw token text can contain newlines, which break the event framing. Encoding the token inside a JSON object avoids mangled multi-line answers.
- **Read with fetch, not EventSource, for POST chats.** The built-in event API cannot send a JSON body or an authorization header, so read the response stream manually and keep a buffer for events split across network chunks.
- **Ask for usage explicitly.** A stream option is needed if you want a final usage chunk for billing or budgeting.

## Production pitfalls worth planning for

- **Buffering proxies:** Reverse proxies and CDNs may hold the response and deliver it all at once, silently defeating streaming. Disable buffering for that route and test through the real production path.
- **Errors after the stream starts:** Once the first chunk is sent, you cannot change the status code. Send an error as an event instead and render it in the UI.
- **Disconnected users and saving:** Stop consuming when the client goes away, and save the assembled answer once at the end rather than writing to the database per token.
- **Idle timeouts:** Periodic keepalive comments can keep a long generation from being cut off.

## When not to stream

Short answers gain little, machine-consumed structured output usually needs to be complete before it can be parsed, and background jobs have no one watching. Streaming is a user-experience feature — use it where a person is waiting.

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**Read the full article on Djangix:** [Streaming OpenAI API Responses: How It Works, in Python, Django & FastAPI](https://djangix.com/blog/streaming-openai-api-responses/) — with the complete Python, Django and FastAPI code.