In the ever-evolving landscape of cloud computing, Azure stands out as a powerhouse for businesses seeking scalable, reliable, and efficient solutions. One of the fundamental aspects of Azure’s appeal is its vast array of resources catering to diverse needs. Azure offers a rich ecosystem of resources, ranging from virtual machines (VMs) and storage accounts to databases, networking components, and beyond.

In the last years, Kubernetes has grown tremendously and is considered by most companies to be the best platform to run applications today. In simple words, Kubernetes is an open-source container orchestration platform that allows you to run and manage containerized applications at scale. In this article, I will explain how you can run Kubernetes on AWS in 3 different ways. But before getting down the road, let me explain why it does make sense to run Kubernetes on AWS.

Raindrops on roses and whiskers on kittens, bright copper kettles…OK, OK, OK, for any of you that read My Five Favourite Things to Automate blog know where I’m going with this, so I’ll save you the rest. Instead, let’s get stuck into what brings us here today: Cloud Commander. Prior to its launch I worked with partners previewing the product, and seeing first-hand how it can have a significant impact on anyone responsible for managing Microsoft 365, Azure, and Intune environments.

As a cloud cost intelligence solution, we often get asked where we fit in the cloud cost universe — and whether or not we’re an alternative to CloudHealth. The short answer is: it depends. The landscape of cloud cost tools is busy, with lots of different solutions that take different approaches to helping companies optimize their cloud costs. To help answer this question, we thought we’d lay out the cloud cost landscape as we see it.

Many articles that you may find on the web discuss the importance of reducing Azure costs by identifying and removing unused resources. Still, they often fall short of providing practical methods to achieve this. However, Turbo360 has introduced a new feature that simplifies identifying idle or unattached Azure VM disks, eliminating the need for complex queries or PowerShell scripts.

In today’s fast-paced cloud environment, understanding the pricing model of some of the best cloud monitoring tools like Datadog is essential in optimizing costs. With several features and adaptable pricing, clarifying Datadog costs and aligning them with your organization’s operational needs is vital for fully harnessing this robust monitoring tool. This guide simplifies Datadog pricing, offering a clear overview of its cost structure.

Private Service Connect (PSC) is a Google Cloud networking product that enables you to access Google Cloud services, third-party partner services, and company-owned applications directly from your Virtual Private Cloud (VPC). PSC helps your network traffic remain secure by keeping it entirely within the Google Cloud network, allowing you to avoid public data transfer and save on egress costs. With PSC, producers can host services in their own VPCs and offer a private connection to their customers.

In the digital era, where data reigns supreme, the ability to extract meaningful insights from vast datasets has become indispensable. Though we have many data sources and data processing languages, SQL (Structured Query Language) stands as a cornerstone in this realm, empowering analysts, and data scientists to navigate through intricate databases with ease.

Serverless architectures have revolutionized how businesses deploy applications, offering scalability and cost efficiency by abstracting infrastructure management. This model, charging only for resources consumed, poses unique budgeting and cost optimization challenges due to its variable pricing structure. Navigating cloud costs in a serverless environment requires a nuanced understanding of leveraging this technology’s benefit while effectively managing expenses.