"Modern generative AI applications often need to stream large language model (LLM) outputs to users in real-time. Instead of waiting for a complete response, streaming delivers partial results as they become available, which significantly improves the user experience for chat interfaces and long-running AI tasks. This post compares three serverless approaches to handle Amazon Bedrock LLM streaming on Amazon Web Services (AWS), which helps you choose the best fit for your application."
"AWS Lambda function URLs provide a direct HTTP(S) endpoint to invoke your Lambda function. Response streaming allows your function to send incremental chunks of data back to the caller without buffering the entire response. This approach is ideal for forwarding the Amazon Bedrock streamed output, providing a faster user experience. Streaming is supported in Node.js 18+. In Node.js, you wrap your handler with awslambda.streamifyResponse(), which provides a stream to write data to, and which sends it immediately to the HTTP response."
Real-time streaming of LLM outputs improves user experience for chat interfaces and long-running AI tasks by delivering partial results as they become available. Serverless AWS options for Amazon Bedrock streaming include Lambda function URLs with response streaming, API Gateway WebSocket APIs, and AWS AppSync GraphQL subscriptions. Lambda streaming uses Node.js 18+ and awslambda.streamifyResponse to forward tokens from InvokeModelWithResponseStream directly to the HTTP response. Architectures typically involve a client fetch to an endpoint, an SDK call to Bedrock, and incremental writes as tokens arrive. Authentication patterns include Amazon Cognito. Each approach has distinct implementation details and tradeoffs for different application needs.
Read at Amazon Web Services
Unable to calculate read time
Collection
[
|
...
]