Operations | Monitoring | ITSM | DevOps | Cloud

June 12, 2025. A date most of the Internet won’t remember — but anyone relying on Google Cloud will. In the span of minutes, a routine quota update snowballed into global disruption. APIs stopped responding. Dashboards stayed green. And across continents, teams scrambled to figure out if the problem was theirs — or Google's. It wasn’t a cyberattack. It wasn’t a datacenter fire.

Over $44.5 billion in cloud spend goes to waste annually, per the FinOps Foundation. No wonder reducing unnecessary costs is critical to protecting your margins. A logical place to start? Cloud service pricing. Providers like AWS, Azure, and Google Cloud continue to evolve their pricing models. They are offering new discounts, regional rates, and shifting commitments. All to win your business. Yet, a cloud pricing comparison alone doesn’t give you a complete picture.

GCP’s recent outage on June 12th was a reminder of just how interconnected modern architectures are. The 2 hour and 28 minute outage affected dozens of companies and spanned 80+ Google services and products. But what was really illuminating was just how far the outage spread due to hidden dependency risks. Many companies that don’t run on GCP were startled to find their services suddenly affected because they had dependencies or depended on vendors that did use GCP.

Backing up files in the cloud or locally is a crucial step you must take when managing your files. Protecting your files with multiple types of data backup methods will help you recover your files instantly, protect against ransomware, and ensure you can always access your files no matter what happens to your device. But what are the two types of cloud storage backup solutions which are fast, simple to use, and the best way to protect your files?

Today, we’re announcing the general availability of Lumigo Copilot, the most intelligent AI-powered observability assistant on the market, built for the complexities of modern microservices. Copilot emerged from a simple realization: Distributed systems produce too much fragmented data across too many layers, making troubleshooting slow, reactive, and deeply manual. Copilot changes that.

AWS gives you the power to scale, deploy, and innovate at speed. However, with that speed comes a good amount of complexity. Services multiply, resources balloon, and performance issues sneak in when you least expect them. That’s where monitoring comes in. But it isn’t about checking boxes on dashboards. It’s about getting the most value for every dollar you spend or, maximizing your return on investment (ROI) from AWS monitoring. So, how do you actually do that?

In this blog series we are examining the Five Myths of Kubernetes Resource Optimization. So far we’ve looked at Myth 1: Observability and Monitoring and Myth 2: Cluster Autoscaling. Stay tuned for the entire series! The third myth addresses another common assumption of many Kubernetes practitioners: Choosing the right instances will eliminate waste in a cluster.

Shopware is a powerful platform, but its performance depends entirely on how it is used. In this article, we explore the most common and avoidable causes of slowdowns in production environments, including plugin overload, cache fragmentation, and misconfigured admin settings. Whether you are preparing for a high-traffic event or simply aiming to keep your storefront fast and responsive, this guide will help you identify the root causes of performance regressions.

Running workloads on Google Kubernetes Engine (GKE) delivers impressive scalability and flexibility. Yet, it can also introduce a tricky challenge: tracking GKE costs accurately. Remember, GKE costs rarely scale linearly. Overprovisioned nodes, idle autoscalers, and orphaned workloads can quietly balloon your bill in the background. And while GKE’s native tools offer some visibility, they often miss the full picture.