Are there concurrency best practices for using Bedrock, such as whether to use multiple parallel requests or queue requests to achieve better throughput?

When working with AWS Bedrock, the choice between parallel requests and queuing depends on your workload’s characteristics and Bedrock’s rate limits. For most scenarios, parallel requests can improve throughput, but you must stay within Bedrock’s service quotas to avoid throttling. Queuing is better when tasks require ordered processing, have variable execution times, or when you need to handle bursts gracefully. A hybrid approach—using parallelism within rate limits and queuing for overflow—is often optimal.

To use parallel requests effectively, structure your code to send multiple asynchronous calls using Bedrock’s SDK (like Boto3 in Python). For example, a Python script might use ThreadPoolExecutor to handle 10 concurrent requests if Bedrock allows 100 requests per second (RPS) per account. However, check AWS documentation for your specific model’s limits—some models cap concurrent invocations. If you exceed limits, Bedrock returns ThrottlingException errors, so implement retries with exponential backoff. Tools like AWS SDKs automatically handle retries, but you can customize this behavior. Parallelism works well for stateless tasks, such as processing multiple independent text prompts for summarization.

Queuing becomes necessary when you need to manage large workloads or prioritize tasks. AWS services like SQS or Amazon SNS can decouple request producers from consumers. For instance, a Lambda function triggered by an SQS queue can process Bedrock requests in batches, ensuring you stay within quotas. Queues also help if tasks have dependencies (e.g., processing step A before B) or if you need to distribute workloads across regions. For example, a video transcription service might queue user uploads, process them sequentially via Bedrock, and store results in DynamoDB. Monitoring tools like CloudWatch can track queue depth and throttling events, allowing you to adjust concurrency dynamically. Combining queues with limited parallelism (e.g., 5 concurrent Lambda invocations) balances throughput and stability.

In practice, start with parallel requests within documented limits and add queuing if you encounter throttling or uneven workloads. For example, a chatbot handling user queries might use 20 parallel threads during peak hours but route excess requests to SQS during traffic spikes. Test different configurations using Bedrock’s metrics to find the right balance. Always implement error handling for throttling and network issues, and consider cost tradeoffs—parallelism may increase compute usage, while queuing adds latency. By monitoring and adjusting based on actual usage, you can optimize Bedrock’s throughput without hitting service limits.