Token Streaming
Token streaming delivers model output incrementally as it's generated — via SSE or websockets — so users see text immediately instead of waiting.
Token streaming sends a model's response as it's generated — token by token over Server-Sent Events or websockets — so the consumer renders output immediately rather than waiting for completion.
It exists because inference is sequential: the model produces one token at a time, and a long answer takes real seconds. Streaming doesn't make generation faster — it makes waiting obsolete by shifting the felt metric from total time to time-to-first-token, which is why every chat product streams and why TTFT is a first-class latency number alongside tokens-per-second.
Engineering-wise, the happy path is easy (providers ship SSE out of the box; scaffolding the endpoint is rote) and the edges are where care goes: structured output arrives in fragments (buffer or parse incrementally), tool calls stream as deltas, mid-stream errors leave partial responses to handle, and UI rendering wants throttling so token-rate doesn't thrash the DOM. In agent systems streaming compounds — each step's output streams into visibility, which is how long-running agents stay legible instead of silent.
Frequently asked questions
- Why stream tokens instead of waiting for the full response?
- Perceived latency. Generation takes as long as it takes, but streaming moves the user's wait from 'full response time' to 'time-to-first-token' — often 10x shorter. For chat and agents, watching text arrive is the difference between feeling instant and feeling broken; nothing about the total time changed, only when value starts arriving.
- What's tricky about consuming a token stream?
- Structure. Plain prose streams trivially; JSON and tool calls arrive as fragments that only parse when complete — so consumers buffer structured segments, parse incrementally, or use provider events that delimit them. Error handling changes too: a stream can fail mid-response, so handle partial output and aborts deliberately.
Related
- Token (LLM)A token is the unit LLMs read and write — a word fragment of roughly 3–4 characters in English. Models are priced, limited, and measured in tokens, not words.
- InferenceInference is running a trained model to produce output — for LLMs, generating tokens one at a time. Its cost and latency define the economics of AI products.
- Add a Streaming LLM EndpointScaffold a token-streaming LLM endpoint — server-side streaming plus the client handler — so responses render incrementally instead of after a long wait.
- Context WindowThe context window is the maximum text — measured in tokens — an LLM can consider at once: prompt, conversation, documents, and its own output combined.