When using AWS Bedrock for generative AI workloads, concurrency best practices depend on your specific workload characteristics and Bedrock’s service quotas. Generally, a combination of parallel requests and queuing can optimize throughput while avoiding throttling. Bedrock enforces rate limits (requests per second and tokens per minute), so parallel requests can help maximize throughput within those limits. However, exceeding quotas will trigger throttling, so queuing (e.g., using AWS services like SQS or Step Functions) is necessary to manage bursts or sustained high loads. The optimal approach balances parallelism with careful monitoring of Bedrock’s quotas and retries with backoff strategies.
For workloads requiring low latency, such as real-time applications, parallel requests are preferable. For example, if your application processes user queries that demand immediate responses, you can use asynchronous SDK calls (e.g., Python’s asyncio or JavaScript Promise.all) to send multiple requests concurrently. However, you must first test Bedrock’s rate limits for your chosen model and adjust the concurrency level accordingly. For instance, if a model allows 100 requests per second, you might use a thread pool or semaphore to cap parallel requests at 90 to avoid throttling. Tools like the AWS SDK’s built-in retry mechanisms or libraries like aiobotocore (for Python asyncio) can help manage transient errors. For batch workloads (e.g., processing large datasets), queuing systems like SQS or Lambda with reserved concurrency can smooth out traffic spikes and retry failed tasks without overwhelming Bedrock’s API.
Implementation-wise, start by benchmarking Bedrock’s quotas for your model using gradual load increases. Use exponential backoff with jitter for retries to avoid thundering herds. For parallel requests, leverage language-specific concurrency primitives—like Java’s CompletableFuture, Go’s goroutines, or .NET’s Task.WhenAll—while respecting quotas. If quotas are strict, use a queue to serialize requests, paired with a worker pool to process them at a sustainable rate. Tools like AWS Lambda Destinations or EventBridge can help route failed requests to dead-letter queues for analysis. Finally, monitor CloudWatch metrics like ThrottledRequests and SuccessfulRequests to fine-tune your strategy. For example, a video transcription service might use parallel requests for real-time subtitles but queue overnight batch jobs to stay within token-per-minute limits.