1. Check Network and Service Health
Start by verifying your network connectivity and the AWS Bedrock service status. If your application is running in a different AWS region than the Bedrock endpoint you’re calling, network latency can cause delays. Use tools like curl or AWS CloudWatch metrics to measure the round-trip time for requests. Check the AWS Health Dashboard for regional service outages or throttling incidents. For example, if you’re invoking us-east-1 endpoints from an application in ap-southeast-1, consider colocating resources or using a Content Delivery Network (CDN) to reduce latency. Ensure your application isn’t blocked by firewalls or security groups restricting outbound traffic to Bedrock endpoints.
2. Optimize Model Parameters and Usage
Review the model configuration and input parameters. Larger models (e.g., Anthropic Claude v2 vs. Claude Instant) or higher max_tokens values can significantly increase response times. Simplify prompts or reduce output length where possible. For example, setting temperature=0 (deterministic output) or lowering top_p can reduce processing time. If you’re using streaming responses, ensure your code handles partial outputs efficiently instead of waiting for the entire response. Additionally, implement retries with exponential backoff in your code to handle transient throttling (HTTP 429 errors). For batch workloads, spread requests evenly over time to avoid hitting rate limits.
3. Profile and Debug Application Code
Instrument your code to isolate where delays occur. Use logging to capture timestamps for each step: request serialization, network transmission, model inference, and response parsing. For example, if serializing a large payload with JSON takes 500ms, consider optimizing data structures or compressing inputs. If the issue is server-side, enable Bedrock CloudWatch metrics to track ModelLatency and InvocationLatency for specific models. For Python applications, tools like cProfile can help identify bottlenecks in your code. If timeouts persist, test with smaller payloads or simpler models to rule out infrastructure issues. Finally, contact AWS Support with specific request IDs, timestamps, and code snippets to investigate backend throttling or model-specific limitations.