Comparison
Inference vs Streaming (LLM Responses)
Inference and Streaming (LLM Responses) are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Inference
Inference comes up when the question is fundamentally about inference.
A ChatGPT response: one inference call per turn.
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.
Frequently asked
What is the difference between Inference and Streaming (LLM Responses)?
Inference: Inference is what happens when you actually run a trained model on new input. For LLMs that means generating tokens one at a time, with sampling and a KV cache. 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.
When should I use Inference vs Streaming (LLM Responses)?
Inference is the right concept when you are focused on inference. Streaming (LLM Responses) applies when you are focused on inference.
Are Inference and Streaming (LLM Responses) the same thing?
No. Inference is inference; Streaming (LLM Responses) is inference. They are related but address different parts of the AI stack.