Operations | Monitoring | ITSM | DevOps | Cloud

In a single pipeline, it’s easy to control job order. But in a large engineering org with dozens of pipelines, hundreds of contributors, and countless shared environments and services, that control can start to slip. Pipelines interfere with each other. Deploys overlap. Test environments break. Someone merges code, triggers a build, and gets a failure they can’t reproduce. Unfortunately, this kind of instability is a routine byproduct of scale.

Logs are essential, but managing them can be tedious. They quickly consume storage, slow down your searches, and make troubleshooting feel like an endless chore. Loki monitoring helps simplify this process, offering a more efficient approach to logging that developers can appreciate.

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.