Operations | Monitoring | ITSM | DevOps | Cloud

The internet is full of fascinating data just waiting to be visualized and queried. And with the latest update to Grafana Cloud, you can start doing it in minutes. Through public APIs, you can access information about global earthquake activity, weather forecasts, music catalogs, and millions of other datasets. And then there's all the data that sits inside company APIs, partner services, and internal platforms that power everyday products and operations.

At Grafana Labs, we are fully committed to the open source OpenTelemetry project and are actively engaged with the OTel community. Many Grafanistas spend a large proportion of their time contributing directly to OpenTelemetry upstream projects, helping make observability more powerful, reliable, and accessible for everyone as part of our big tent philosophy.

Sometimes the biggest challenges in software aren’t about code — they’re about consensus. What do we call things? What do we standardize? And how do you evolve a system that thousands of companies depend on without breaking everything along the way?

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.

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?