Operations | Monitoring | ITSM | DevOps | Cloud

When it comes to observability, different people have different levels of expertise. Some can readily rip out 50-line PromQL queries, complete with masterful usage of functions like group_left and label_replace.

Generative AI has emerged as a powerful force for synthesizing new content—text, images, even music—with astounding proficiency. However, monitoring, optimizing, and maintaining the health of these complex AI systems is challenging, and traditional observability tools are struggling to keep pace. At Grafana Labs, we believe that every data point tells a story, and every story needs a capable narrator.

Monitoring application health is a lot like monitoring your personal health. Vital signs such as heart rate, blood pressure, and overall well-being can spot problems before they escalate, helping us maintain good health. Similarly, application health requires constant monitoring of performance indicators like CPU usage, memory consumption, and application response times.

We consistently roll out helpful updates and fun features in Grafana Cloud, our fully managed observability platform powered by the open source Grafana LGTM Stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). In case you missed it, here’s a roundup of the latest and greatest updates for Grafana Cloud this month. You can also read about all the features we add to Grafana Cloud in our What’s New in Grafana Cloud documentation.

As part of our big tent philosophy here at Grafana Labs, we believe you should be able to access and derive meaningful insights from your data, regardless of where that data lives. One of the ways we stay true to that philosophy is through our Enterprise data sources.

Prometheus has become an essential technology in the world of monitoring and observability. I’ve been aware of its importance for some time, but as a performance engineer, my experience with Prometheus had been limited to using it to store some metrics and visualize them in Grafana. Being a Grafanista, I felt I should dig deeper into Prometheus, knowing it had much more to offer than just being a place to throw performance test results.

Over the summer we told you about an update to our core Prometheus data source, which was part of a larger shift in our effort to meet users where they are. It’s a change we’re really excited about, as it represents our biggest step yet toward enabling the creation of truly vendor-neutral data sources for Grafana.

Even though I’m a Prometheus maintainer and work inside the Prometheus code, I found many of the details of PromQL, the Prometheus query language, obscure. Many times I would look something up, or go deep into the code to find out exactly what it did, only to forget it again the next month. So, trying to live up to my job title of Distinguished Engineer at Grafana Labs, I resolved to write the definitive guide: what really happens when I execute a PromQL query?

In the observability space, ease-of-use has always been a key differentiator for Grafana. As much as we want to offer a powerful observability platform to our users, we also want to ensure they can get up and running as quickly as possible. Still, for those of you sitting down to build your first dashboard, we totally understand that a little guidance can go a long way.

When you investigate an incident, context is everything. Let’s say you’re working on-call and get pinged in the middle of the night. You open the alert and it sends you to a dashboard where you recognize a latency pattern. But is the spike normal for that time of day? Is it even relevant? Next thing you know, you’re expanding the time window and checking other related metrics as you try to figure out what’s going on. It’s not to say you won’t find the answers.