Category: Compute

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.

In the 3rd part of our 4-part Starting your Cloud Financial Management Journey series, we’ll cover the things you should consider and the tools you can use to forecast and plan for your existing or net-new workloads.

Cloud cost optimization is often implemented as a reactive activity, despite being intrinsically proactive by nature. By implementing these 5 cloud cost optimization best practices, you can ensure proactivity as you maximize realized business value and take advantage of the flexibility, agility, and scalability of cloud technologies and services.

In this blog, we’ll share tools you can setup, pricing models you can take advantage of, and services you can use that will help you identify cost optimization opportunities in your workloads.