Operations | Monitoring | ITSM | DevOps | Cloud

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!

When something breaks in production, logs are usually where you start. They help you figure out what happened, where, and why. But with microservices architecture, logging isn't simple anymore. In a traditional monolithic application, logs live in one place. With microservices, they're scattered across multiple services, containers, and sometimes even data centers. What used to be a simple grep command now feels like solving a mystery without most of the clues.

Both Grafana Tempo and Jaeger are distributed tracing tools designed for modern microservice architectures. Jaeger, released as an open-source project by Uber in 2015, has matured into a graduated CNCF project. Tempo, announced by Grafana Labs in October 2020, is a newer entrant focused on high-volume tracing with a unique storage architecture. Before comparing these tools in detail, let's quickly review what distributed tracing is and why it matters.

The world is changing every day. From geopolitical shifts to legislation like GDPR which requires localized processing – these all create a complex and uncertain landscape where data storage, processing, and cloud services could potentially come to a sudden halt or suffer heavy disruption overnight. As a result, organizations are increasingly interested in potential routes for shifting cloud services to safer alternatives closer to their country of operation.

We work in a digital landscape where IT teams are expected to move faster, scale smarter, and deliver flawless digital experiences, automation is no longer optional; it’s foundational. But while most organizations have started down the automation path, far fewer can answer the critical question: How do we measure success? The problem isn’t just a lack of metrics. It’s that the wrong metrics are still steering the ship.

As organizations move beyond traditional hypervisors, Harvester continues to lead the way as an open source, Kubernetes-native virtualization solution. With the release of Harvester 1.5, users now have greater flexibility and ecosystem alignment than ever before—with General Availability (GA) for ARM64 and support for CSI-compatible storage backends. Harvester is part of a growing shift toward cloud-native infrastructure that unifies VMs and containers under the Kubernetes API.

IT Ops Problem Solver Series – Part 2: This article is a summary of a full report in our IT Ops Problem Solver Series. In this series, we’ll tackle the biggest problems facing IT Ops leaders and explore how some of Galileo’s clients are addressing them. In this part of the series, we delve into IT performance challenges and how to address them effectively.