Principle:Microsoft Semantic kernel Streaming Response
| Knowledge Sources | |
|---|---|
| Domains | AI_Orchestration, Real_Time_Processing |
| Last Updated | 2026-02-11 19:00 GMT |
Overview
Streaming Response is the principle of receiving AI-generated content incrementally, token by token, as it is produced, rather than waiting for the complete response before processing.
Description
Large language models generate text sequentially, one token at a time. In a non-streaming (batch) invocation, the entire sequence of tokens is accumulated on the server side and returned as a single response once generation is complete. This introduces perceived latency proportional to the total generation time, which can range from seconds to tens of seconds for long responses. Streaming Response addresses this by delivering each token (or small group of tokens) to the client as soon as it is generated, enabling the application to begin processing and displaying content immediately.
The streaming approach fundamentally changes the interaction model from request-response to request-stream. Instead of awaiting a single result, the caller iterates over an asynchronous stream of content chunks. Each chunk contains one or more tokens and may include metadata such as the finish reason, token usage updates, or function call fragments. The caller can display these chunks to the user in real time, creating a typewriter-like effect that significantly improves perceived responsiveness.
Streaming is particularly important for conversational interfaces, real-time dashboards, and any application where users are watching the AI compose a response. Studies in human-computer interaction show that perceived latency has a greater impact on user satisfaction than actual latency, and streaming reduces perceived latency to the time-to-first-token rather than time-to-completion. Additionally, streaming enables early termination: if the user decides they have seen enough, they can cancel the stream without waiting for the full response to be generated, saving both time and compute resources.
Usage
Use streaming whenever the AI response will be displayed to a user in real time, especially for long-form content generation, conversational chat interfaces, and interactive applications. Prefer non-streaming invocation when the entire response is needed before any processing can begin (for example, when parsing structured JSON output or when the result feeds into a subsequent computation step).
Theoretical Basis
Streaming Response implements the Observer Pattern (also known as the Publish-Subscribe pattern) applied to token generation. The AI service acts as the observable (producer) that emits tokens, and the application acts as the observer (consumer) that processes each token as it arrives.
In .NET, this pattern is formalized through IAsyncEnumerable<T>, which provides an asynchronous pull-based stream:
Producer (AI Service):
yield token_1
yield token_2
...
yield token_n
(complete)
Consumer (Application):
await foreach (var token in stream)
Process(token)
Latency analysis:
Non-streaming:
Perceived latency = T_first_token + T_generation(all tokens) + T_network
User sees nothing until complete response arrives.
Streaming:
Perceived latency = T_first_token + T_network(first chunk)
User sees content starting from the first token.
Where:
T_first_token = time for the model to generate the first token
T_generation(n) = time to generate n tokens (roughly linear in n)
T_network = network round-trip time
The IAsyncEnumerable<StreamingKernelContent> type provides backpressure naturally: the consumer controls the pace of iteration. If the consumer is slow, the producer (via HTTP chunked transfer or Server-Sent Events) will buffer accordingly. This prevents the consumer from being overwhelmed while maintaining the real-time delivery guarantee.
Cancellation semantics:
await foreach (var chunk in stream.WithCancellation(token))
if (ShouldStop(chunk))
break; // Terminates the stream, releasing server resources
Early termination via break or CancellationToken signals the AI service to stop generation, which is both a user experience improvement and a cost optimization.