Operations | Monitoring | ITSM | DevOps | Cloud

Charts in Netdata have always been interactive. You can zoom, pan, select time ranges, and see per-second granularity across thousands of metrics. But when you spotted something interesting, the next steps usually meant leaving the chart: opening another tab to check a related metric, navigating to the correlation tool, or pulling up a different time range for comparison. The investigation workflow lived outside the chart, even though the chart was where the investigation started.

For today's modern businesses, the data landscape demands security and flexibility. You need to connect your observability platform to rich, proprietary datasets that often reside in private networks without compromising security or managing complex network infrastructure. You may also face an extra layer of complexity in order to effectively query and visualize that data. Luckily, modern artificial intelligence tools have made these previously complicated processes much simpler.

In Grafana Cloud we use a simple yet generous formula that lets you query up to 100x your monthly ingested log volume in gigabytes for free. This works for the vast majority of our customers, but if you aren’t careful and strategic with your usage, you could find yourself with an overage bill.

Sometimes the hardest part of debugging a system isn’t fixing the problem—it’s figuring out what’s actually happening in the first place.

The way IT teams interact with technology has changed dramatically over the years. From early text-based interfaces to today’s dashboards and now conversational AI, each stage has reshaped how we monitor, diagnose, and understand complex IT environments. But while dashboards gave us visibility, they often led to more questions than answers. In this post, we briefly explore the evolution of UI in IT products and how conversational AI is bridging the gap between data and understanding.

When you’re managing a handful of nodes, the flat list in the nodes tab works fine. When you’re managing hundreds or thousands, it becomes a wall of hostnames. You end up applying the same filters repeatedly: all the production database servers, all the nodes in eu-west, all the Kubernetes workers in the staging cluster. The filters work, but they don’t persist, and there’s no way to share them with the rest of your team. Node groups solve this.