Does Amazon Bedrock support asynchronous requests or batch processing, and if so, how can it be utilized?

Amazon Bedrock does not natively support asynchronous requests or batch processing for its core model invocation APIs. The primary method to interact with foundation models in Bedrock is through synchronous API calls like InvokeModel, which processes a single input and returns a result immediately. However, you can implement asynchronous workflows or batch processing by combining Bedrock with other AWS services, such as AWS Lambda, Step Functions, or Amazon SQS. This approach allows developers to decouple request handling, scale processing, and manage large workloads efficiently.

To achieve asynchronous behavior, you can use AWS Lambda to invoke Bedrock models in response to events (e.g., from Amazon S3 or SQS). For example, a Lambda function could process a queue of requests stored in SQS, send each to Bedrock’s InvokeModel API, and store results in a database like DynamoDB. This avoids blocking your application while waiting for responses. Alternatively, Step Functions can orchestrate multi-step workflows involving Bedrock, such as chaining model invocations with data transformations or error handling. For instance, you might use Step Functions to retry failed requests or aggregate results from parallel Bedrock calls. These patterns let you handle long-running tasks without tying up resources.

For batch processing, you’ll need to manage parallelization and input/output handling manually. A common approach is to split a large dataset into smaller chunks (e.g., using AWS Glue or a custom script), process each chunk via Bedrock’s API, and combine results. For example, a Python script could read a CSV file, iterate over rows, and send groups of 10 rows at a time to Bedrock using concurrent threads or processes. You’d need to handle rate limits (Bedrock’s default is 10 transactions per second per account) and errors, possibly using exponential backoff. Results could be written to S3 or a database. While this requires more setup than a built-in batch API, it provides flexibility to optimize costs and performance based on specific use cases.