Server monitoring guides for CPU, memory, disk, GPU, virtual machines, containers and databases. Learn which signals to collect and how to investigate resource bottlenecks.
server monitoring
The page is frozen, the API is timing out, and the pager just lit up at 2 a.m. On a Linux host, the first instinct is to open a terminal and check CPU usage, but the number in front of you only helps if you know what it means.
server monitoring
At 2 a.m., a server can still answer ping while the application on top of it has been returning errors for most of the night. The on-call engineer sees a green host check, the support queue keeps filling, and the only thing that looks healthy is the wrong layer.
server monitoring
18.5 billion connected IoT devices were active in 2024, and that count is projected to reach 21.1 billion by the end of 2025 according to IoT Analytics. That number changes the monitoring conversation. At small scale, teams can still survive with dashboards, spot checks, and a few hand-built
server monitoring
A CPU alert usually arrives at the worst time. A dashboard turns red, an application feels slow, and the first question in chat is blunt: “Is the server out of CPU?” That question sounds simple, but it rarely has a simple answer. A busy CPU can mean healthy demand, a
server monitoring
Choose Linux server monitoring tools by the work you need them to do: diagnose one machine, retain host metrics across a fleet, monitor services, or connect infrastructure signals to application telemetry. A terminal utility, a self-hosted monitoring stack and a hosted service have different operating costs and responsibilities. For an
server monitoring
A lot of teams are in the same spot right now. The database isn't exactly down, the app isn't exactly healthy, and the monitoring stack keeps saying everything is green until users start filing tickets. That's where most database health checks fail. They confirm
server monitoring
The page goes off at 3 AM. Apache is still answering on the port, the uptime check is green, and users are still filing complaints because every page feels stalled. That's the failure mode that trips up a lot of teams. The server is technically up, but the
server monitoring
A cloud system rarely fails in a dramatic, obvious way. More often, an engineer gets a 3 AM alert that says something vague like "latency high" or "service unavailable," opens three dashboards, and still can't tell whether the problem is CPU pressure, a noisy
server monitoring
A Windows Server starts lagging, users report timeouts, and the usual first move is to open Task Manager and hope the noisy process is obvious. Sometimes it is. More often, the server looks busy but not broken, and the underlying problem sits one layer deeper in memory pressure, disk queues,
server monitoring
Clusters rarely fail in a clean, obvious way. A pod starts restarting, node pressure rises, latency slips, and the on-call engineer is flipping between kubectl, cloud metrics, and old Grafana dashboards to work out what changed. That approach can survive in a dev cluster. It gets expensive fast once Kubernetes
server monitoring
A monitoring stack usually breaks at the worst possible moment. The dashboard turns into a wall of red, three different tools disagree about what failed, and the on-call engineer still has to answer the same basic question: is this a host problem, a network problem, an application problem, or a
server monitoring
Support tickets start piling up fast when an application slows down. The first question is almost always the same. Is it the database? That moment exposes the difference between having access to SQL Server and being able to monitor SQL Server well. A few ad hoc checks in SSMS might