To monitor and measure the performance of Amazon Bedrock requests, you can use AWS CloudWatch metrics, custom logging, and tracing tools. Amazon Bedrock integrates with CloudWatch to provide built-in metrics such as request latency, error rates, and invocation counts. You can also instrument your code to log custom metrics like token usage and analyze logs for deeper insights.
First, leverage CloudWatch’s built-in metrics for Bedrock. Metrics like InvocationLatency (response time in milliseconds) and NumberOfRequests track basic performance. For error rates, use BedrockServiceErrors to capture HTTP 4xx/5xx errors. These metrics are automatically published to CloudWatch, where you can create dashboards to visualize trends or set alarms for thresholds like high latency or error spikes. For example, an alarm triggering when InvocationLatency exceeds 5000 ms could signal performance degradation. CloudWatch also lets you filter metrics by Bedrock model (e.g., Claude or Titan) to isolate issues.
Second, track token usage and custom metrics. Bedrock’s API responses include metadata like inputTokens and outputTokens for some models. Extract these values in your code and log them as custom CloudWatch metrics using the AWS SDK. For instance, after each Bedrock API call, record the tokens used with PutMetricData. This helps correlate token counts with costs, as pricing is often token-based. If token data isn’t directly available, approximate it by counting input/output characters and dividing by average token length (e.g., 4 characters per token for English).
Finally, implement structured logging and tracing. Use AWS X-Ray to trace Bedrock requests end-to-end, identifying bottlenecks in multi-step workflows. Log detailed request/response data (e.g., timestamps, model IDs) to Amazon CloudWatch Logs or S3, and analyze logs with Athena or OpenSearch for patterns like frequent timeouts. For auditing, enable AWS CloudTrail to log Bedrock API calls. Combine these tools to create a monitoring pipeline: CloudWatch for real-time metrics, X-Ray for tracing, and logs for post-hoc analysis. This approach balances simplicity with granularity, letting you optimize costs and reliability.