---
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id: "ad0ce528b8050593e5f53c3c9851d916cfd1ce4e727b9d207bcc426db5171e0a"
canonical_url: "https://aidr.today/ad0ce528?lang=en"
title: "Optimizing cost per task and proving agent ROI on Amazon Bedrock"
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available_langs: ["en","vi"]
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published_at: "2026-11-12T18:43:19.000Z"
category: "Tools"
topics: ["amazon","agent","devtools"]
source_urls: ["https://aws.amazon.com/blogs/machine-learning/optimizing-cost-per-task-and-proving-agent-roi-on-amazon-bedrock/"]
summary: "A post details how to calculate the true cost per task on Amazon Bedrock by accounting for four hidden cost drivers, and how to reduce costs using caching, routing, and output constraints while proving agent ROI through human baselines, holdout experiments, and a five-dimension scorecard."
---

# Optimizing cost per task and proving agent ROI on Amazon Bedrock

> [Open the canonical story](<https://aidr.today/ad0ce528?lang=en>)

**Published:** 2026-11-12T18:43:19.000Z
**Category:** Tools
**Topics:** amazon, agent, devtools

## Summary

A post details how to calculate the true cost per task on Amazon Bedrock by accounting for four hidden cost drivers, and how to reduce costs using caching, routing, and output constraints while proving agent ROI through human baselines, holdout experiments, and a five\-dimension scorecard\.

## Sources

- [Story source](<https://aws.amazon.com/blogs/machine-learning/optimizing-cost-per-task-and-proving-agent-roi-on-amazon-bedrock/>)