Operations | Monitoring | ITSM | DevOps | Cloud

AWS EC2, Azure Virtual Machines, and Google Compute Engine (GCE) appear similar on paper but produce different bills due to how each provider prices capacity, discounts, idle time, and commitment terms. The same VM configuration can cost 20-40% more or less depending on which cloud you choose and how your workload runs. On paper, all three offer similar virtual machines. In reality, they price capacity, discounts, and idle time very differently.

Previously I have written about how useful public cloud storage can be when starting a new project without knowing how much data you will need to store. However, as datasets grow over time, the costs of public cloud storage can become overwhelming. This is where an on premise, or co-located, self-hosted storage system becomes advantageous: it provides the greatest range of benefits, including cost, performance, security, and data sovereignty.

Third-party data now drives forecasting, analytics, and machine learning across modern cloud teams. But acquiring it has long meant custom contracts, delayed access, and limited visibility into how data costs scale inside analytics workflows. AWS Data Exchange reduces much of that friction by integrating third-party data into the AWS ecosystem.

Imagine you want to launch an application without first building and managing the servers that run it. You write the code, pick how it should run, and then let a platform take care of the rest. That’s the core promise of AWS Elastic Beanstalk. In this snackable guide, you’ll understand AWS Elastic Beanstalk well enough to decide if it belongs in your AWS architecture.

During the October 20th AWS outage, not one Control Plane customer went down. Here’s the architecture that made that possible, and how to assess your own.

IP address exhaustion in Kubernetes doesn’t announce itself with clear error messages. Pods fail to schedule, services degrade unpredictably, and the symptoms look like a dozen different problems before anyone realizes the cluster has run out of available IP addresses. By the time the root cause becomes clear, multiple services are affected and recovery requires coordination across infrastructure layers.

In regulated industries like fintech, healthcare, and government, DevOps teams often find themselves acting as human compliance gateways. The pressure to maintain strict security standards while accelerating release cycles creates a compliance tax: a heavy burden of manual environment setups, security review tickets, and the inevitable scramble for evidence before an audit. This manual labor, or toil, is more than a drain on productivity. It creates a dangerous gap between policy and actual operations.

On February 10, 2026, Amazon Web Services (AWS) experienced an outage that triggered widespread reports of CloudFront failures and DNS resolution issues. While AWS later acknowledged the incident, StatusGator detected the disruption earlier using Early Warning Signals, giving customers valuable lead time before the provider confirmed anything publicly.

Since the launch of ChatGPT in November 2022, the world has seen a huge shift in our personal, business, and creative lives. Although we often use AI daily, its addition to our lives has not been without problems. It has caused writer strikes and worries about how AI handles our data and what it means for privacy, and many people are worried about AI taking over their jobs.

AI-native SaaS products aren’t failing because the models are bad. They’re failing because the architecture can’t keep up with how AI actually behaves in production. What looks affordable in staging can erode your margins once real customers, workflows, and automation come into play. Designing AI-native SaaS architecture is now as much a margin decision as it is a technical one.