The trend continues to accelerate – even faster now. Cloud computing was already booming before 2020. But in the following two years, remote work flourished, and cloud adoption soared. Some companies have since returned to the office. Others are adopting hybrid models, where staff work from home and in the office. Yet there’s more to the rise and rise of cloud computing than remote working.

We recently explored what the cloud is and how it has evolved over 60 years. Yet, cloud computing emerged into what it is today in 2002, when Amazon, the e-commerce giant, sought a more efficient way to expand its online marketplace. So, it is not surprising that the company’s cloud services subsidiary (AWS), which launched in 2006, is the largest Cloud Service Provider (CSP) today. This CSP marketshare guide shares the details of the cloud infrastructure market, the dominant players, and more.

Top GCP cost management tools in 2024 The cloud offers unparalleled scalability and agility, but keeping your Google Cloud Platform (GCP) bill under control is a challenge. Here’s where having a GCP cost management tool comes in—your secret weapon for optimizing your cloud spend and maximizing value.

Erik Peterson, our CTO and Founder here at CloudZero, recently hosted a webinar-style interview with Matthew “MJ” Jackson, DevOps Lead at Drift. This interview took a deep dive into the methods Drift used to take control of their cloud spending. If you own, manage, or otherwise have any stake in a SaaS company that heavily utilizes the cloud, you’ll want to read on to see the highlights of this conversation.

Before the development of AWS cost estimation tools, users faced challenges in predicting cloud costs due to complex pricing structures and multiple service configurations. This often led to surprise charges and made budgeting and optimizing cloud costs difficult. To address these issues, AWS introduced its own cost estimation tools. These include the Cost Explorer, the Pricing Calculator, AWS Budgets, AWS CloudWatch, AWS Trusted Advisor, and the AWS Cost and Usage Report.

Most organizations migrate to the Amazon Web Services (AWS) public cloud from on-premises data centers. However, others have switched from another Infrastructure-as-a-Service (IaaS) platform to AWS. The most common reason we hear for choosing AWS is cost savings. As the largest public cloud provider, AWS leverages economies of scale, making its cloud services more affordable than those a company would have to build itself. Yet, not every organization can claim that AWS has saved it money.

Businesses are increasingly interested in cloud economics. For instance, what are the financial implications of moving to the cloud versus staying on-premises? And what’s the best strategy for optimizing cloud consumption to get the best value from cloud resources? In this guide, we’ll look at some of the key concepts of economics in cloud computing, and how your business can leverage cloud cost intelligence to maximize the value of your cloud investment.

AWS ALB was developed to simplify the management of modern web traffic while enhancing application scalability, security, and performance. And like any other AWS service, understanding AWS ALB pricing is essential for managing cloud costs effectively. This guide offers a comprehensive overview of AWS ALB, detailing its key use cases and pricing structure.

The cloud offers on-demand computing resources, the ability to increase or reduce usage as needed, and the flexibility to pay only for what you use. But if you’ve been building on the cloud for some time, you know that the last part isn’t exactly straightforward. For example, leaving aggressive product features, engineering sessions, or testing projects unchecked can quickly waste thousands, even millions of dollars in a matter of days.

Republicans and Democrats don’t agree on much, but when it comes to generative AI, they’ve managed to find 32 billion points of agreement. On May 15, a bipartisan group of senators unveiled a roadmap that would have the U.S. government spending $32 billion annually by 2026 to drive AI-related innovation. The U.S. isn’t alone; AI spending in India is growing at an annual average rate of 31.5%, and will exceed $5 billion by 2027.