Optimizing cost per task and proving agent ROI on Amazon Bedrock | Amazon Web Services
Part 1 of this series built the operating model that controls Amazon Bedrock spend: attribution with AWS Identity and Access Management (IAM) principal tags and Cost and Usage Report 2.0, observability with model invocation logging, enforcement at three tiers, and a review cadence. That layer answers who spent it, and you can stop them. It can’t tell you whether the spend was well spent.
This post answers two questions that Part 1 deliberately left open. First: Why is this workflow expensive? The answer requires thinking in cost per task, not cost per token. Second: Is the agent better than the humans were? The answer requires baselines, controlled experiments, and a scorecard tied to decisions.
In this post, you learn a framework for calculating true model cost (including four hidden cost drivers that the pricing page doesn’t show), techniques for reducing cost per task through caching, routing, and output constraints, and...
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