Operations | Monitoring | ITSM | DevOps | Cloud

Cloud computing empowers organizations to access IT resources on demand, over the Internet, and on a pay-per-use basis. Thus, your company does not need to purchase, install, operate, and upgrade hardware for physical data centers. Instead, you can rent resources as needed from cloud service providers such as Amazon Web Services (AWS). For instance, AWS provides compute, storage, database, networking, machine learning, data lake, analytics, security, and IoT resources/services.

We all want growth, but often find ourselves unequipped to deal with it. It’s a bit like going to the gym, lifting weights, and seeing real results, only to realize that you no longer fit into your old clothes. Now you have to decide whether to modify them or buy new clothes. We can use this very simple analogy to understand horizontal vs. vertical scaling.

Google Cloud Platform (GCP) might not always be the loudest name in the cloud room. But it’s gradually become a powerhouse for organizations running data-intensive, AI/ML, and global-scale applications. We also can’t ignore that GCP offers a backbone powered by Google’s own infrastructure (the same one that runs YouTube, Gmail, and Search).

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

As AI adoption increases, the race to real, durable AI value intensifies. Almost every organization that can use AI is using AI — but one of the most troubling trends I’ve observed revolves around talent. Right now, most organizations use AI to increase internal efficiencies — do quick research, write quick emails, start a new project at the 50% mark rather than the 0% mark, etc. But some executives I’ve talked to are taking a more aggressive approach.

You spin up a few test environments. Someone forgets to shut one down. A cost spike hits your Azure bill. And no one saw it coming. Does this sound familiar? Surprise cloud bills are still a thing today. For teams trying to balance innovation with financial discipline, the Azure Budget feature offers a first line of defence against surprise cloud costs.

At CloudZero, our mission has always been clear: power efficient innovation in the cloud by connecting engineering decisions with business outcomes. But here’s the truth — we’re not just solving for visibility. We’re solving for action. In order to turn theoretical savings into actual waste elimination and tangible outcomes, you have to enable engineers to act on cost insights within the tools and workflows they already use. CloudZero’s new Jira integration does just that.

Running cloud infrastructure without forecasting is a lot like operating a delivery company without checking the weather. You might plan for smooth traffic and sunny skies, only to be hit with a sudden storm, delays, and unexpected costs. The same happens when your cloud usage spikes and your AWS bill catches you off guard. For engineers, CTOs, and CFOs alike, AWS forecasting isn’t just about estimating future costs.

Knowing only how much you spent on your AWS bill each month isn’t enough to provide the cost visibility you need to make cost-aware engineering and business decisions. It’s why AWS introduced tags. Tags enable AWS users to label their resources to track cost and usage in the vast AWS infrastructure. Tagging helps you understand who, what, and why your cloud spend is changing.

Can you believe one-third of 2025 is already behind us? Just in case the Easter Bunny didn’t leave anything good in your basket this year, we’ll treat you to a little Easter egg — if you’re willing to follow the clues — along with the scoop from this past month. Here’s what you might have missed in April: Read on for the details!