Operations | Monitoring | ITSM | DevOps | Cloud

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.

This is the fourth blog in our Azure Monitoring series, and this time, we’re digging into cost efficiency. Azure makes it easy to scale, but just as easy to overspend. Idle VMs, forgotten disks, and silent data transfer fees add up fast. The result is budget overruns that catch teams off guard and force reactive cuts. This blog breaks down the Azure metrics that actually help you reduce waste, improve visibility, and keep cloud spend aligned with business priorities. Missed our earlier posts?

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!

Over the next few years, the cloud computing market is expected to grow at a compound annual growth rate (CAGR) of 18.3%. Cloud-based services are becoming increasingly popular among businesses of all sizes, contributing to the market’s growth. When you consider the other benefits involved, that makes a lot of sense for business.