AWS Machine Learning Blog· Majd Arbash·· 3 d ago
Optimizing cost per task and proving agent ROI on Amazon Bedrock
Optimizing cost per task and proving agent ROI on Amazon Bedrock
AI summary
Comparing per-token prices hides the real cost of running a model on Amazon Bedrock. This post shows how to calculate the true cost per task across four hidden cost drivers, reduce it with caching, routing, and output constraints, and prove agent ROI with human baselines, holdout experiments, and a five-dimension scorecard.
Selection record
Threshold 60Official, first-handFirst 34Second 38
Not admittedSum of both 72 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：Amazon Bedrock智能体成本与ROI优化教程
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com