You might encounter rate limit or throttling errors with AWS Bedrock because the service enforces limits on the number of API requests allowed per second (TPS) or per minute to ensure fair resource allocation and stability. These limits vary based on the model you’re using (e.g., Claude, Titan), your AWS account type, and the region. For example, a sudden spike in requests, exceeding your account’s TPS quota, or misconfigured client-side retries can trigger these errors. Throttling often occurs when your application sends requests faster than the allocated rate, even if temporarily, or if multiple clients share the same account-level quota.
To prevent throttling, first review Bedrock’s service quotas in the AWS Console or documentation to understand your model-specific limits. If your workload requires higher throughput, request a quota increase via AWS Support. Structure your application to respect these limits by implementing client-side rate limiting, such as using a token bucket or leaky bucket algorithm to pace outgoing requests. For example, if your model allows 100 TPS, ensure your code doesn’t exceed this rate. Additionally, use the AWS SDK’s built-in retry mechanisms with exponential backoff and jitter. These automatically retry throttled requests (HTTP 429 errors) after progressively longer delays, reducing the likelihood of repeated failures. Caching frequent or repetitive requests (e.g., common prompts) can also reduce the total number of API calls.
If throttling occurs despite precautions, design your system to handle it gracefully. Log and monitor throttling events using AWS CloudWatch metrics to identify patterns and adjust your rate limits or architecture. For batch workloads, consider distributing requests across multiple AWS accounts or regions (if supported) to leverage separate quotas. Use dead-letter queues (DLQs) in message-driven architectures to reprocess failed requests after a cooldown period. For example, if using AWS Lambda with Amazon SQS, configure a DLQ to capture messages that fail due to throttling and retry them later. Finally, test your application under expected load to validate your throttling-handling logic and ensure resilience during peak usage.