Operations | Monitoring | ITSM | DevOps | Cloud

AWS and Cloudflare often come up together when teams are building, securing, and scaling web applications. While they originally served different purposes, their offerings now overlap in key areas like content delivery (CloudFront vs. Cloudflare CDN), edge compute (Lambda@Edge vs. Workers), DNS and WAF, and API delivery. In this guide, we’ll explore what each platform does best, when to use one over the other, and see if you can combine both for optimal speed, security, and cost efficiency.

The cloud offers many benefits, including scalability, cost savings, and operational efficiency. But here’s the deal. Organizing, monitoring, and controlling a cloud environment can become complex as workloads, applications, services, and underlying infrastructure grow. Businesses can better control and monitor their cloud infrastructure services, resources, and data using cloud management software.

Global cloud spending will surge past $700 billion this year. Megacaps alone will spend more than $300 billion on AI in 2025, with much more on the way. The innovation potential of the cloud has never been higher, never been more hotly contested, and never come with a higher price tag. In the late 2000s, the cloud reshaped the global economy and enabled life as we know it. Now, in the mid 2020s, AI is poised to do the same.

Over the last few years, more organizations have switched from relying solely on one cloud service provider (CSP) to several. The primary reasons for the change are minimizing dependence on a single CSP, preventing vendor lock-in, and providing greater flexibility. Recent trends include using a mix of cloud providers to take advantage of the cost savings several CSPs offer and using best-of-breed services for different applications, teams, or departments.

Cloud computing has become the backbone of modern business operations, powering everything from day-to-day collaboration to large-scale digital transformation initiatives. As organizations deepen their reliance on cloud services, the financial stakes continue to grow. According to Gartner, global spending on public cloud services is projected to reach over $720 billion in 2025, a significant increase from nearly $600 billion in 2024.

At first glance, AWS Config seems like a no-brainer for tracking changes, catching misconfigurations, and proving compliance. But beneath the surface, Config pricing can get surprisingly intricate. Costs don’t just depend on the number of resources you monitor. They also hinge on how often those resources change, how many rules you evaluate, and how you manage historical data. In this guide, we’ll demystify AWS Config pricing.

In our post, 9 Continuous Delivery Tools For Reliable Releases, we explored why continuous delivery is critical to building robust development pipelines — and how it helps teams deliver customer value while boosting the bottom line. Today, we’re diving into the next evolution of that pipeline: continuous deployment. Today’s technology companies need to release quality features quickly and put them in users’ hands even quicker.

On May 1st, AWS corrected a long-standing billing bug tied to Elastic Load Balancer (ELB) data transfers between Availability Zones (AZs) and regions. That fix triggered a noticeable increase in charges for many users, especially for those with high traffic volumes or distributed architectures. The problem wasn’t new usage; it was a silent correction to an old error.

As your organization scales its Datadog footprint, you want to understand what’s driving cost changes and promote cost awareness. But to take meaningful action, you need more than a monthly bill—you need real-time, contextualized cost data tied to services and teams. Without this visibility, it’s hard to assign ownership, prevent cost overruns, or identify which changes are affecting spend.

We recently explored what the cloud is and how it has evolved over 60 years. Yet, cloud computingWe recently explored the cloud 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 unsurprising that the company’s cloud services subsidiary (AWS), which launched in 2006, is today’s largest Cloud Service Provider (CSP).

It came as a pleasant surprise. CloudZero recently discovered over $1.7 million in annualized savings. And, all from our own infrastructure. Otherwise, we would have spent $1.7 million on cloud resources we didn’t actually need. Cloud savings stories like this are common with CloudZero customers, and we often find companies spending more than they should in the cloud. If your team is also struggling with managing and controlling the cost of cloud computing, then this guide is for you!

Amazon Web Services (AWS) powers everything from scrappy startups to Fortune 500 giants. So, AWS interview questions are relevant for all kinds of roles, from infrastructure design to cost governance. Now, while some interviews test definitions and acronyms, the best ones dig deeper. They explore how well you understand AWS trade-offs—think of performance vs. cost, scalability vs. complexity, or security vs. usability.

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.

As your applications scale across cloud and SaaS providers, allocating costs and optimizing workloads become increasingly important—and challenging. Without access to cost data in their daily workflows, engineering teams can’t easily understand the cost of their resources and identify where they can reduce their spend. And while FinOps teams have access to cost data, they often review this information in silos.

As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost. But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game. Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks.

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.

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.

You’re probably familiar with finding surprise, hidden charges if you’ve been using the cloud for a while. Data transfer fees are the most common source of unanticipated Amazon Web Services (AWS) charges. They can be so costly that some companies, like Netflix and Pinterest, have spent fortunes on data transfer fees — up to $30 million a year. In this post, we’ll share tips for navigating AWS data transfer pricing and how you can optimize costs.

The cloud is the backbone of modern businesses, revolutionizing the trajectory of innovation, technology and business itself. While its promise of instant scalability and flexibility drives unprecedented growth, these same advantages can become a double-edged sword. The ease of spinning up new resources, automating deployments, and expanding services across regions—all of which make the cloud so powerful—can quickly lead to sprawling infrastructure and runaway costs if not carefully managed.