Category: *Post Types

If you manage Enterprise Support for a large AWS organization, you’ve likely been asked which accounts a single Support line item comes from and how it was calculated. These are the questions the AWS Billing API can now answer programmatically. This post shows you how to break down your monthly AWS Enterprise Support charge by account using three AWS Billing API operations.

AWS introduced the FinOps Agent (currently in public preview) at FinOps X 2026. FinOps automation is still relatively new, and given the sensitivity around cloud and AI cost management, it’s understandable if you’re hesitant to go all-in on autonomous FinOps right away. In this post, we’ll share our mental model for FinOps automation, the four tiers of automation you can follow, and the four built-in trust levers you can rely on to stay in control as automation scales.

If every dollar invested in artificial intelligence (AI) generated a two-dollar return, rising costs would signal positive ROI rather than inefficiency. However, establishing a relationship between AI spend and business value can be complex, leaving you without clear metrics on when to scale or recalibrate initiatives. To calculate ROI (refer to Figure 1), you start by allocating your costs, then you align them to the business outcome they will support. This gives you a Cost per Outcome, the building block for ROI. Once you can measure what each outcome costs, calculating ROI is simply a matter of comparing that cost against the value it delivers. This post outlines a practical methodology for aligning AI investments with business impact. We detail how to classify use cases, attribute costs, and identify the initiatives that drive measurable value.

Implementing a FinOps weekly checkpoint is a proactive approach to catch spend trends sooner and generates more meaningful insights for your monthly variance analysis. Gathering variance context throughout the month leads to a clearer, more impactful analysis. This weekly checkpoint involves regularly reviewing cost data, identifying anomalies or trends, and engaging with technical teams to understand cost change drivers.

As you scale Generative AI usage with Amazon Bedrock, a common question emerges: “Which team, application, or user is driving the Bedrock spend?” Until now, answering that question required manual reconciliation correlating AWS CloudTrail logs with billing data to map API calls back to specific identities. That approach is time-consuming, error-prone, and difficult to maintain at scale. AWS has announced AWS Identity and Access Management (IAM) Principal-Based […]

We’re excited to announce AI-powered cost analysis in AWS Cost Explorer, powered by Amazon Q Developer. You can now click a suggested prompt or ask a question in your own words right from the Cost Explorer. Amazon Q delivers detailed insights while Cost Explorer automatically updates its charts, tables, and report parameters to reflect the […]

With re:Invent 2025 behind us and over 60 launch announcements, here are five hidden gems that may have gone unnoticed but still have big cost impacts. While everyone was talking about the database savings plans and AI announcements, these quieter launches are already helping customers optimize their cloud spend in creative ways.

It’s Friday morning. You’re expecting an easy day when suddenly—ding—a budget alert hits your inbox. Not only have you been notified, but so has your manager and the FinOps team. Your relaxed Friday just disappeared.
Sound familiar? This scenario happens more often than it should. With Kiro CLI  or Amazon Q Developer IDE, AWS’s generative AI-powered assistant, you can prevent these panic-inducing moments while saving significant money. Here are five powerful ways to use AI to optimize your AWS infrastructure which came from a re:Invent 2025 talk: Optimize AWS Costs: Developer Tools and Techniques.

Another year has flown by. As we wrap up another exciting AWS re:Invent, I’m excited to share the latest enhancements in AWS Cloud Financial Management (CFM) space. This year’s announcements reflect our commitment to providing comprehensive solutions across the four CFM pillars: track and allocate, govern and operate, forecast and plan, and optimize and save. We’ve also made significant improvements in AI for CFM, which impacts all four CFM pillars.

Today, we’re excited to announce the extension of AWS managed monitors in AWS Cost Anomaly Detection to support linked accounts, cost allocation tags, and cost categories. Previously available only for AWS services, AWS managed monitors now enable you to track costs across all your organizational dimensions with minimal ongoing maintenance. As organizations scale from tens to hundreds of accounts and teams, you can create a single AWS managed monitor that automatically adapts as your organization grows, eliminating the need to maintain hundreds of individual customer managed monitors. After initial setup, this feature transforms cost monitoring from a time-consuming operational task into an automated process that ensures comprehensive anomaly detection coverage while enabling clear segmentation of spending alerts by cost ownership.