Category: AWS Cost and Usage Report

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.

AWS Data Exports lets you select which columns to include in your AWS Cost and Usage Reports (CUR 2.0) export. But filtering down rows to specific accounts or services still required post-processing. Whether you need to share cost data with a partner, isolate a program’s spend, or meet an audit scope, teams often build downstream pipelines with AWS Lambda, AWS Glue, or Amazon Athena. AWS Data Exports supports SQL-based row filtering at the source. Each destination receives a pre-filtered dataset including only the rows that match your criteria with no post-processing step required. In this post, you’ll learn how to filter CUR 2.0 data at the source using AWS Data Exports SQL query capabilities, so you get the data you need.

AWS WAF and AWS Shield Advanced provide robust DDoS mitigation and request filtering, but estimating their combined costs can be complex. This guide helps you understand the pricing components so you can protect your web applications while keeping spend predictable.

This blog was written in partnership with Michael Peterson of CrescoNet. In this blog, you will learn how CrescoNet has employed both basic and advanced techniques to reduce their costs without compromising the performance, scalability or reliability of their critical data pipeline. The challenge for CrescoNet CrescoNet is a leading integrator of smart metering solutions […]

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 […]

A video hosting platform cut Amazon S3 costs by 70% by analyzing usage patterns and optimizing their architecture. They discovered that a tiny fraction of files were generating excessive retrieval costs in S3 Glacier Instant Retrieval and moved them to S3 Intelligent-Tiering while improving CloudFront caching. These strategic changes reduced GET requests by 90% and delivered substantial six-figure annual savings.

In Part 1 of our series on improving cost visibility and observability, “Improve Cost Visibility and Observability with AWS Cost Categories – Part 1: Fundamentals and Basic Grouping Techniques”, we explored the fundamentals of AWS Cost Categories and demonstrated how to implement basic grouping techniques using regional dimensions, multiple dimensions, and split charges to enhance cost visibility across your AWS environment. Building on these fundamentals, this second installment of our AWS Cost Categories series explores advanced techniques and automation capabilities that can further enhance your cost management strategy.

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 AWS announced Cost and Usage data exports in FOCUS 1.2 specification. You can now create exports of your AWS Cost and Usage data in the FOCUS 1.2 schema. FOCUS (FinOps Open Cost and Usage Specification), supported by the FinOps Foundation, is an open specification that standardizes Cost and Usage data to simplify cloud financial […]