How can I handle rate limits or throughput limits in Bedrock to avoid throttling in a production system?

To handle rate limits or throughput limits in AWS Bedrock and avoid throttling in a production system, implement a combination of client-side retries, workload distribution, and proactive monitoring. Bedrock, like many cloud services, enforces limits to ensure fair resource usage, so your application must gracefully handle throttling errors (e.g., HTTP 429 responses) while optimizing request patterns to stay within allowed quotas.

First, design your client to handle retries with exponential backoff and jitter. When a request is throttled, immediately retrying it can worsen the problem. Instead, use a retry strategy that increases wait times between attempts (e.g., starting with 1 second, then 2, 4, etc.) and adds random variation (jitter) to prevent synchronized retries across clients. Most AWS SDKs, including those for Bedrock, include built-in retry mechanisms that can be configured for these scenarios. For example, in Python’s boto3, you can adjust the retry_mode and max_attempts in the client configuration. Additionally, use circuit breakers (e.g., with libraries like resilience4j) to temporarily stop sending requests if failures persist, reducing load on both your system and Bedrock.

Second, distribute requests to stay within throughput limits. If your application requires high concurrency, spread workloads across multiple Bedrock endpoints, regions, or accounts where possible. For instance, partition large batches of requests into smaller chunks processed over time. Use queuing systems like Amazon SQS to control the flow of requests—this lets you decouple producers and consumers, buffer tasks during peaks, and process them at a sustainable rate. If Bedrock allows provisioning reserved throughput (similar to Amazon SageMaker), allocate capacity for critical workloads. Monitor your usage against service quotas via CloudWatch metrics (e.g., NumberOfRequests or ThrottledRequests) and set alarms to trigger scaling actions or notify teams before hitting limits.

Finally, proactively test and adjust your strategy. Simulate throttling scenarios in pre-production environments using tools like AWS Fault Injection Simulator to validate retry logic and fallback mechanisms. If consistent throttling occurs, request a quota increase via AWS Support, providing usage data to justify the need. Continuously optimize your request patterns—for example, use batch APIs if available, cache frequent model outputs, or compress payloads to reduce per-request overhead. By combining these techniques, you can maintain reliability while efficiently using Bedrock’s capabilities.