Operations | Monitoring | ITSM | DevOps | Cloud

If you’re looking for a dashboarding solution today, chances are you’ve looked at Grafana or SquaredUp — or both. Grafana is a popular open source dashboarding tool with on-prem and cloud variants, while SquaredUp is the SaaS, cloud-based unified dashboarding solution. Both offer a comprehensive list of data sources that they can plug into and build dashboards. As such, they both also offer an integration with Azure - which is the focus of our discussion today.

Dashboards are supposed to answer questions, not create more of them. But investigations don't stop at a single view. The moment you want to understand one specific thing in detail like a failing VM, a degraded service, a slow pipeline, dashboards start to break down. You end up either building yet another dashboard or searching through many different ones. SquaredUp's Perspectives changes this.

Many critical services run inside private networks, where traditional monitoring tools and practices can’t offer full visibility. This makes it difficult to validate service availability and performance before problems impact your users. Synthetic Monitoring — a Grafana Cloud solution that helps you proactively monitor the performance of your applications and services — addresses this gap with a feature known as private probes.

Some questions in engineering never go away. Here’s one that every team eventually confronts: Do we roll up our sleeves and build the tooling ourselves, or do we buy something built for us? It’s a choice that has the power to speed teams up or hold them back. With the rise of AI-powered observability, this familiar software dilemma has re-emerged with higher stakes and faster-moving technology.

Having well-organized dashboards is just as important as having good dashboards. But dashboard organization shouldn’t just make things easy to find. It should provide structure that supports collaboration and efficient troubleshooting. It has to be more than a basic folder system. This post looks at how classic dashboarding tools handle organization today, where they fall short, and how SquaredUp Workspaces organize for visibility and shared context.

Continuous profiling offers deep visibility into production environments, revealing exactly how applications consume CPU and memory. It’s the go-to observability practice for directly connecting system behavior and performance to specific lines of code. But when teams consider deploying continuous profiling more broadly, a common question comes up: what’s the overhead? Is it safe to run continuous profiling on my production services 24/7, or does the cost outweigh the benefits?