Book Image

AWS FinOps Simplified

By : Peter Chung
Book Image

AWS FinOps Simplified

By: Peter Chung

Overview of this book

Much like how DevOps is a combination of cultural philosophies, practices, and tools that advocate a collaborative working relationship between development and IT operations, FinOps encourages the same collaboration between technology and finance team, making it key relationship to establish and maintain for any thriving business. This book will help you understand how organizations with a mature FinOps practice can decentralize cost ownership to developer teams and encourage cross-functional collaboration between business, finance, and technology, enabling speed, innovation, and business growth. You’ll focus on structuring your organization to form the right FinOps team, including a Cloud Center of Excellence, and learn how to implement practical cost savings measures with AWS tools to optimize costs in both the short as well as long term. By the end of this cloud FinOps book, you’ll be ready to implement a successful Cloud FinOps practice for your organization to get the best value from the AWS cloud for your workloads.
Table of Contents (18 chapters)
Free Chapter
2
Part 1: Managing Your AWS Inventory
7
Part 2: Optimizing Your AWS Resources
12
Part 3: Operationalizing FinOps

Setting up AWS Cost Anomaly Detection

We looked at an example use case from our friends at VidyaGames in using Cost Explorer to see anomalous usage in Chapter 3, Managing Inventory. In the example, Jeremy applied grouping and filters to past expenditure data and saw that the Amazon EC2 service and RunInstance API action were contributing significantly to costs. If a bar chart representing daily expenditure shows a sudden spike on a specific day, you can drill down to that day to see what account, service, or API action contributed to the spike. This is a legitimate, albeit manual, approach to detecting unusual expenditure activity.

AWS Cost Anomaly Detection helps to alleviate this manual approach. Cost Anomaly Detection monitors your spend patterns to detect unusual activity using ML. Using this service can help you find anomalies more quickly than doing so manually.

An undesirable effect of anomaly detection is the noise produced from false positives, which tell you that an...