Amazon Bedrock provides flexibility in handling model outputs, supporting both immediate full responses and token-by-token streaming depending on the API method and model used. Here’s how it works:
1. Streaming vs. Full Completion:
When using Bedrock’s InvokeModelWithResponseStream API, you can receive outputs incrementally as they’re generated (token-by-token). This is useful for scenarios like chatbots or real-time interactions where showing progress improves user experience. In contrast, the standard InvokeModel API returns the entire generated output at once after processing completes. Streaming requires explicit opt-in via the withResponseStream method, while the default behavior returns a complete response.
2. Implementation Details: For streaming, the response is split into chunks sent over an HTTP stream. Each chunk contains a portion of the output text, metadata, or metrics. For example, using the AWS SDK for JavaScript, you’d attach an event handler to process incoming chunks:
const response = await bedrockRuntime.invokeModelWithResponseStream(params);
response.body.on('data', (chunk) => {
const decoded = JSON.parse(chunk.bytes.toString());
// Append decoded.output to your UI
});
Non-streaming requests return a single response object with the full completion field populated. The choice depends on latency requirements: streaming reduces perceived wait time but requires handling partial outputs.
3. Model Compatibility and Tradeoffs: Not all models in Bedrock support streaming. For example, Anthropic’s Claude and Amazon Titan support it, but others might not. Check the model’s documentation for compatibility. Streaming adds complexity—you’ll need to handle partial responses, concatenate tokens, and manage network interruptions. Full completions are simpler to implement but force users to wait for the entire generation. Use streaming for interactive use cases and full completions for batch processing or when simplicity is critical.