Streaming uses Server-Sent Events (SSE) or WebSockets to send data in small chunks from server to client. Most AI APIs support streaming via a simple parameter: with OpenAI that is stream: true, with Anthropic stream: true or "stream" in the API call.

The server sends tokens back as a stream of data packets. The client listens to that stream and appends each packet to the display. When the model is done, the server sends an end signal.

In the browser you use the Fetch API with ReadableStream or a library that abstracts this. In a Node.js backend you use a similar approach but via a server streaming response.

The biggest advantage of streaming is perceived speed. Even if the total generation time remains the same, streaming feels faster because the user gets immediate feedback. User experience research consistently shows that a progressively loading interface is rated better than a long loading bar followed by abrupt output.

A second advantage is that users can intervene earlier. If the model is heading in the wrong direction, the user can stop the generation without waiting for the model to finish.

Streaming is not always the best approach. In the following cases it is better to wait for the full response:

• Structured output: If you need the complete JSON to validate it, streaming has little value.
• Backend processing: If you process the AI output before sending it to the user, you need the full text.
• Short responses: If the response takes less than a second, streaming adds no noticeable benefit.
• Batch jobs: In background processing there is no user waiting for output.

Streaming introduces a few technical challenges:

• Connection stability: A streaming connection that drops halfway leaves the user with an incomplete response. You need a mechanism to detect and handle this.
• Buffering: Some infrastructure components buffer data and only forward it when the connection closes. That effectively makes streaming non-streaming. Ensure your server and proxy are correctly configured (disable buffering in Nginx).
• Markdown rendering: If you stream markdown to a UI, you want the rendering to update incrementally. This requires a renderer that works incrementally.
• Error handling: An error halfway through a streamed response is harder to handle than an error in a standard request-response.

In a React application you typically use a state variable that you update with each incoming token. Libraries like ai from Vercel or the streaming helpers in the AI SDKs simplify this considerably.

Provide an indication that the model is still generating — a blinking cursor or a loading indicator — so the user knows more is coming. Also offer the ability to stop the generation.

Streaming does not change the cost: you still pay per token. Streaming can slightly increase server load because connections are kept open longer. At high concurrency this is something to consider for your infrastructure capacity.

Streaming is a relatively simple improvement that significantly enhances the user experience of AI applications. At Mach8 we implement streaming as standard in user-facing AI interfaces, because it makes the interaction with AI considerably more pleasant.

Want to build an AI application with a great user experience? View our AI agents service or get in touch.