A company needs to generate synthetic responses for many prompts from a large data set using an API. Responses do not need to be immediate. Which approach requires the least development effort?

Amazon Bedrock batch inference is the most efficient and least effort approach for generating many responses asynchronously. It's designed for large-scale, non-real-time processing, allowing you to submit a dataset of prompts and receive responses without managing individual API calls or complex orchestration. Real-time inference would involve sending prompts one by one and waiting for each response, which is inefficient and requires more development for managing numerous individual calls, especially when immediacy isn't required. Amazon Bedrock agents are for complex, multi-step tasks and conversational AI, not simple batch generation, and building a recursive system would be significant development effort. AWS Lambda could automate sending prompts, but it still requires custom code to manage the queue, retry logic, and storage for each individual prompt and response, making it more complex than Bedrock's native batch solution.