The latest News and Information on Cloud monitoring, security and related technologies.
Achieving cost savings is one of the main drivers for cloud adoption. But for most companies, controlling cloud spend is much more challenging than anticipated. In a recent survey, 94% of IT decision makers report they are overspending in the cloud. Our own survey on cloud costs revealed 90% of executives say better cloud cost management and cost reduction is a top priority.
Cloud services are the number one source of unexpected overspending for companies today. As a result, cloud financial management is a major focus for most organizations. But how do you track the success of cloud efficiency? Full allocation of multicloud costs is a critical component for understanding your actual cloud services usage, establishing cloud cost management ownership, and creating accurate budgets and forecasts at the line of business, project, application and even team levels.
As organizations continue to expand their cloud footprint, managing costs without risking application performance is a priority. Because of this, Arm processors have become popular for their efficient, cost-effective processing power. Microsoft Azure’s new series of Azure Virtual Machines are powered by Ampere Altra Arm-based processors, which provide excellent price performance for scale-out and cloud-native workloads.
Microsoft Azure’s recently launched new Azure Virtual Machines (VMs) feature the Ampere Altra Arm-based processor. These new VMs are engineered to efficiently run horizontally scalable workloads such as web servers, application servers, and open source databases. They deliver excellent price-performance and represent an important addition to Microsoft Azure's portfolio of instance types.
In this tutorial, we'll deploy a simple web app to the cloud of your choice - composed of a database and a virtual machine where the frontend code will run. So that the configuration is reusable and consistent, we'll write it in Terraform. Usually Terraform configurations are cloud-specific, and changing clouds requires a complete rewrite. In this case, so that you can reuse the same configuration across clouds, we'll be using Multy.
