Optimizing cost per task and proving agent ROI on Amazon Bedrock

This AWS post argues that Amazon Bedrock spend should be judged by cost per task rather than cost per token, since tokenizer efficiency, output verbosity, agentic loop count and reasoning overhead can make a lower-priced model cost more. A worked example shows a model with 2x the list price finishing 65% cheaper per task, and the post adds caching, routing and output constraints plus a five-dimension scorecard for measuring agent ROI against human baselines.