Comparison
Streaming (LLM Responses) vs Time to First Token
Streaming (LLM Responses) and Time to First Token are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Streaming (LLM Responses)
Streaming (LLM Responses) comes up when the question is fundamentally about inference.
A ChatGPT-style web app: SSE stream rendering tokens as they arrive, TTFT ~0.6s vs full-wait of ~5s.
When you would reach for Time to First Token
Time to First Token comes up when the question is fundamentally about inference.
Claude with a 50K-token cached prefix: TTFT drops from ~6s to under 1s on subsequent calls reusing the same prefix.
Frequently asked
What is the difference between Streaming (LLM Responses) and Time to First Token?
Streaming (LLM Responses): Streaming returns tokens to the client as they're generated rather than holding the full response until completion. Implemented over Server-Sent Events (SSE) or WebSocket; what makes chat UIs feel fast. Time to First Token: Time to first token (TTFT) is how long it takes from sending a request until the first response token arrives. The user-perceived latency metric for streaming chat.
When should I use Streaming (LLM Responses) vs Time to First Token?
Streaming (LLM Responses) is the right concept when you are focused on inference. Time to First Token applies when you are focused on inference.
Are Streaming (LLM Responses) and Time to First Token the same thing?
No. Streaming (LLM Responses) is inference; Time to First Token is inference. They are related but address different parts of the AI stack.