Streaming LLM Responses: How It Works and Best Practices
Streaming LLM responses explained: how token streaming works, SSE vs WebSocket, and best practices for latency, UX, and cost in your app.
Streaming LLM responses means showing tokens as they are generated instead of waiting for the full answer. It makes AI apps feel fast — first token in under a second versus several seconds — and is table stakes for chat UX.
How streaming works
- Client sends a normal completion request with stream=true.
- Server begins generating tokens immediately.
- Tokens are pushed over Server-Sent Events (SSE) as they are produced.
- Client appends tokens to the UI and stops at the end event.
SSE vs WebSocket
- SSE: one-way server push over HTTP — perfect for text streaming, simpler, works with standard HTTP tools.
- WebSocket: bidirectional — needed for interactive agents that also take client input mid-generation.
Best practices
- Show partial output and a cursor; don't wait for completion.
- Handle cancellation: a stop button should abort the upstream request.
- Buffer and flush: batch tiny deltas to reduce UI churn.
- Time out and retry: streams can drop mid-generation.
- Track tokens streamed for cost analytics even without buffering the whole reply.
Streaming through a proxy or gateway
If you route through a gateway, make sure it passes SSE through without buffering the whole response. Buffering a stream in the proxy destroys the latency benefit.
FAQ
Why is streaming important for LLM apps?+
It drops perceived latency to the first token and lets users read as output generates, which dramatically improves chat UX.
What is Server-Sent Events?+
A standard HTTP-based format for one-way server push. LLM providers use SSE to stream tokens to clients.
Does streaming change API cost?+
No — you are billed for the same tokens whether streamed or not. Streaming only changes how you receive them.
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