Enabling or disabling streaming responses in AWS Bedrock directly affects latency, resource utilization, and user experience. When streaming is enabled, responses are sent incrementally as the model generates tokens, reducing perceived latency for end users. For example, in a chat application, users see partial responses immediately, even if the full response takes the same total time to generate. However, this approach requires maintaining an open connection throughout the generation process, which increases server-side resource usage (e.g., memory and compute for handling concurrent streams). In contrast, disabling streaming forces the client to wait for the entire response before receiving any data, which simplifies server resource management but increases end-to-end latency, as the client cannot process partial results earlier.
The performance trade-offs depend on workload characteristics. Streaming benefits interactive applications (e.g., chatbots, real-time translations) where low perceived latency matters. For instance, a code-generation tool streaming lines of code incrementally lets developers review output sooner. However, streaming introduces overhead: frequent network packets and client-side logic to handle partial responses (e.g., concatenation, error recovery). Non-streaming workloads, like batch processing of documents, avoid this overhead by sending a single response payload. This reduces connection management costs and simplifies retry logic for failures, as the entire response either succeeds or fails atomically. For high-throughput batch jobs, non-streaming can improve server efficiency by freeing resources faster after each request.
A practical example illustrates this: a streaming-enabled translation API might use 20% more CPU due to prolonged connections but reduce user-observed latency by 50%. Meanwhile, a non-streaming report-generation API could handle 30% more requests per minute due to shorter-lived connections but force users to wait seconds longer for results. The choice hinges on prioritizing user experience versus infrastructure efficiency. Developers should test both modes with realistic traffic patterns, monitoring metrics like time-to-first-byte (TTFB) for streaming and requests-per-second (RPS) for non-streaming, to determine the optimal balance for their use case.