Making the shift to AWS can bring myriad benefits including improved application performance and reduced costs. However, moving to AWS does not guarantee that your application will run efficiently by default. There is a tendency for companies that are migrating from more traditional data center operations to bring their traditional manner of operations with them to a cloud and end up missing some of the benefits their migration could provide.
You can’t talk about cloud optimization without mentioning Amazon Web Services’ (AWS) Auto Scaling. It is one of AWS’s most powerful features. Maximizing AWS auto scaling groups is essential for businesses that run apps and infrastructure on AWS. But when it comes to AWS Auto Scaling, lots of people labor under various misconceptions.
The FinOps journey’s third phase, “Operate”, is the last step in the FinOps cycle. But it is by no means the end. The first phase of the FinOps journey, Inform, is about gaining visibility into your cloud operations and creating accountability. Next, the Optimize phase focuses on discovering ways to optimize cloud services and resources, and creating frameworks designed to make spend more efficient.
Rightsizing an application is hard. Many applications are overprovisioned, running on more infrastructure than needed, to avoid failure when workloads grow. The alternative, an application crash when the load exceeds capacity, is also not acceptable to most users, developers, or businesses. The result, however, is unnecessary spending on idle resources during periods of low demand.
FinOps has three iterative phases: Inform, Optimize, and Operate. In the first part of this blog series, we discussed how the “Inform” phase is all about achieving visibility for resource allocation and creating accountability over the organization’s cloud spending. In this article, we will focus on the “Optimize” phase, and explore how it impacts your FinOps journey and the entire cloud spend optimization plans.
Back in 2010, Amazon Web Services (AWS) launched the t1.micro instance type. They followed this up with the first of the T2 instances (micro, small, and medium) in 2014, more sizes in 2015 and 2016, and finally unlimited bursting. Then, in 2018, AWS launched Amazon Elastic Compute Cloud (EC2) T3. This was augmented in early 2019 with a less expensive variant – the T3a. T3 and T3a were more cost-effective than their forerunners, providing AWS users with general-purpose, burstable instances.
Kubernetes is a powerful cloud-native automation tool that allows the environment to automate system configurations such as scaling up and down. Employing Kubernetes best practices can result in substantial cost savings when compared to static or manually managing systems. The effectiveness of this automation in saving and limiting costs is directly related to the appropriate configuration being applied to an environment.
We hate to be the ones to break it to you, but managing your cloud resources and usage manually is a massive waste of time. Don’t worry, you are definitely not alone – many enterprises make this mistake. Here’s why trying to manually determine the best EC2 instance type is a bad idea. Applications are constantly changing and evolving. So are their resource requirements. To achieve SLA-level performance, the infrastructure must have adequate resources to meet shifting requirements. /p>