Category: Amazon EC2
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.
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.
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.
One of the most common customer requests we receive is related to supporting containerized applications. Compute Optimizer now has recommendations to help you identify optimal CPU and memory configurations for Amazon Elastic Container Service (Amazon ECS) services running on AWS Fargate.
Learn how you can optimize your current AWS footprint with little to no architectural changes. Focus on improving price-to-performance without introducing engineering overhead, large planning cycles, and significant time investment.
Use multiple overlapping Savings Plans to reduce commitment risk, increase discount coverage, and relieve the burden of long-range usage predictions.