- 谁
- AWS
- 引文
This post shows you how to use these capabilities to answer three questions that every FinOps and AI Platform leader asks as AI tool adoption scales: What is the real per-user cost?
发生了什么
AWS 作者介绍了如何跟踪通过 Amazon Bedrock 路由推理请求的 AI 编码和效率工具的用户级成本。该指南介绍了如何为 CUR 2.0 配置 IAM Identity Center 会话身份和标签，随后说明 Athena 查询和仪表板选项。指南还比较了多种支出控制方式，包括 AWS Budgets、结合 Lambda 的 CloudWatch 警报、DynamoDB 配额和 LLM 网关。
接下来读什么
查看这件事的完整进展AWS Details Cost Attribution and Spending Controls for AI Tools on Amazon Bedrock这件事到目前为止
AWS explains how to attribute Amazon Bedrock inference costs to individual users of AI coding and productivity tools, then compare ways to control their usage. The guide configures IAM Identity Center session identities and tags for CUR 2.0 reporting, with Athena queries and dashboard options for analyzing per-user costs. It also compares AWS Budgets, CloudWatch alarms with Lambda, DynamoDB quotas, and LLM gateways as spending controls. AWS’s earlier walkthrough covers tracking Bedrock costs by IAM user or role with cost allocation tags, Cost Explorer or CUR 2.0, and AWS Budgets actions that attach a Deny policy. Budgets refresh up to three times a day, so spending can exceed a threshold before an action runs. Newly applied cost allocation tags can take up to 24 hours to appear and another 24 hours after activation to populate billing data. A Deny policy on a shared role blocks everyone using that role.