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.
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.
Learn about Singular’s AWS Graviton adoption process including their mental model, project execution, and measurement of success, resulting in an improved price-to-performance ratio by 35%.