Operations | Monitoring | ITSM | DevOps | Cloud

We’re entering a new era at CloudZero, and we’re coming in hot. This week, we’re not just launching a new feature. We’re taking a major step forward in our mission to make cloud cost efficiency a seamless, engineer-first, business-aligned reality. My team and I are thrilled to introduce CloudZero Optimize, our smartest, most action-oriented optimization solution yet.

At Grafana Labs, we’ve always believed that observability should be open and accessible — that belief extends beyond metrics, logs, and traces to the costs associated with managing observability at scale. That’s why we’re excited to share that we’ve adopted the FinOps Open Cost and Usage Specification ( FOCUS), a community-driven, open standard for cloud billing data.

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.