Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption

As organizations scale their generative AI workloads on Amazon Bedrock, operational visibility into inference performance and resource consumption becomes critical. Teams running latency-sensitive applications must understand how quickly models begin generating responses. Teams managing high-throughput workloads must understand how their requests consume quota so they can avoid unexpected throttling. Until now, gaining this visibility required custom client-side instrumentation or reactive troubleshooting after issues occurred.

Today, we’re announcing two new Amazon CloudWatch metrics for Amazon Bedrock, TimeToFirstToken and EstimatedTPMQuotaUsage. These metrics give you server-side visibility into streaming latency and quota consumption. These metrics are automatically emitted for every successful inference request at no additional cost, with no API changes or opt-in required. They are available now in the AWS/Bedrock CloudWatch namespace.

In this post, we cover the following:

• Why visibility into time-to-first-token latency and quota consumption matters for production AI workloads
• How the new TimeToFirstToken and EstimatedTPMQuotaUsage metrics work ...