Question

A retail company runs a customer-facing product recommendation feature on Amazon Bedrock using Anthropic Claude models via on-demand throughput. During a flash sale, traffic to the recommendation service spikes 5x for roughly 3 hours, and users begin receiving ThrottlingException errors even though the average daily volume is well within the account's default quotas. The team wants to absorb these short, unpredictable bursts without committing to a fixed hourly cost and with minimal code changes. Which approach best meets these requirements?

Accepted Answer

Configure a cross-region inference profile so requests are automatically distributed across multiple regions, aggregating on-demand quotas to absorb the burst.

Answer

Purchase Provisioned Throughput with a 1-month commitment sized for the peak burst so capacity is always reserved.

Answer

Switch the recommendation calls to the Bedrock batch inference API to smooth out the traffic spike over several hours.

Answer

Move the workload to a smaller distilled model so each request consumes fewer tokens during peak periods.