Category: Compute

As many organizations are looking to achieve financial success through effective Cloud Financial Management (CFM) strategies, Infor, the global leading software provider, has been experimenting with different practices, from cost reporting to optimization. In this blog post, we will share their success and learning that helped achieve over $2 million in savings through automation and modernization initiatives.

This blog shows how you can automatically propagate account-level tags to AWS resources created by AWS Service Catalog. Service Catalog allows sharing of portfolios across AWS accounts and provides a TagOption library to manage tags on provisioned AWS resources. Resource tagging varies by account, so it is not part of the portfolio product configurations. We designed the solution to reduce the burden on users to a minimum, while also adopting cloud best practices such as infrastructure automation.

We’re excited to announce granular cost visibility for Amazon Elastic Kubernetes Service (Amazon EKS) in the AWS Cost and Usage Reports (CUR), enabling you to analyze, optimize, and chargeback cost and usage for your Kubernetes applications. With AWS Split Cost Allocation Data for Amazon EKS, customers can now allocate application costs to individual business units […]

AWS Compute Optimizer is a powerful tool that offers recommendations to optimize your Amazon EC2 instances, helping you identify suitable instance types, reduce underutilized resources, and enhance performance. In this blog post, we will explore AWS Compute Optimizer and demonstrate how to automatically apply its recommendations, resulting in significant cost savings and improved resource efficiency.

If you’re scratching your head and trying to catch up with all the re:Invent launch announcements from the AWS Cloud Financial Management team, let me walk you through how your FinOps experience may be improved for better with the latest capabilities that were just released last week at AWS re:Invent 2023. I’ve also included recordings of these launch announcements, so you can watch these at your own pace.

Rightsizing recommendation preferences allows you to adjust both CPU headroom and thresholds, configure a new 32-day lookback period option, and set instance family preferences at the organization, account, or regional level. With this feature, Compute Optimizer provides greater transparency on how the recommendations are generated and the ability for you to configure EC2 rightsizing recommendations for higher savings and performance sensitivity, aligning recommendations with your business needs. Let’s explore what you can achieve through this new feature.

There are many benefits to running your Amazon OpenSearch Service workloads on Graviton2 based instances coupled with the gp3 EBS volume type. If you’re currently maintaining an Amazon OpenSearch Service workload, these changes are easy to make and can provide ~10% in savings with minimal effort.

When it comes to cost optimization, you often focus on the top spenders, but the cost of the services that typically fall under the “Others” category can be just as high as the top cost drivers. It’s worth looking into the sources of these costs and identifying opportunities for cost and performance optimization. In this blog, I’ll use a few examples to demonstrate how you can dive deeper and understand the cost elements of these “other” services and what you can do to optimize the spend.

This post will show how you can optimize your x86 Amazon Elastic Cloud Compute workloads with no architectural changes. We will focus on improving price-to-performance without introducing engineering overhead, large planning cycles and significant time investment. The optimizations mentioned today require no application engineering and can be done quickly. The focal point of this post is showing the benefits of running your x86 EC2 workloads on AMD based EC2 instances to achieve at least 10% cost savings.

We’re excited to announce that the cost data for Amazon Elastic Container Service (Amazon ECS) tasks and AWS Batch jobs is now available in the AWS Cost and Usage Reports (CUR). With AWS Split Cost Allocation Data, you can easily understand and optimize cost and usage of your containerized applications, and allocate application costs back to individual business entities based on how shared compute and memory resources are consumed by your containerized applications. Learn how to opt into and view your Split Cost Allocation Data.