Understand uptime checks, API availability, SLAs, SLOs and reliability metrics. Learn how to measure service availability, communicate outages and set useful reliability targets.
server monitoring
A server can answer every check on schedule while customers cannot finish the task they came for. That gap is where most server monitoring uptime programs fail, not because teams lack dashboards, but because the checks measure reachability instead of usable service. Table of Contents * The Moment Everything Looked Green
uptime monitoring
SLA monitoring continuously measures a service against its contractual commitments and checks whether the agreed terms are being met, rather than merely showing whether a service is up. For example, a commitment of 99.5% availability over 30 days permits approximately 3.6 hours of downtime in that period. An
uptime monitoring
Synthetic monitoring is a method that sends scripted probes on a fixed schedule to test whether websites, applications, APIs, and services are available and completing expected transactions. A controlled browser journey can reveal a change from 1.2 seconds to 3.0 seconds after a release, even before a real
uptime monitoring
A checkout API starts returning 500 errors at 2 a.m. Half the team is online, the incident channel is active, and someone asks whether the problem is serious enough to wake the rest of the organization. Without a pre-agreed service level objective, the answer depends on whoever happens to
uptime monitoring
A team opens the morning dashboard and sees 99.9% uptime for the month. The graph looks calm. Then support tickets start piling up. Customers couldn't log in during a dependency failure, checkout timed out from one region, and a brief DNS issue slipped between checks. The percentage
uptime monitoring
At 3:00 a.m., an SRE receives a page about failed customer requests. The synthetic uptime check is green, the endpoint responds from the probe location, and the dashboard says availability is normal. Meanwhile, production telemetry shows real users timing out after a recent deployment. The incident isn'
uptime monitoring
The outage is over, the pager has gone quiet, and the customer-facing systems look healthy again. Then the monthly report lands, and the numbers show a breach that nobody caught in real time. That gap is where service level agreement monitoring usually fails, not in the recovery itself, but in
uptime monitoring
At 99.9% uptime, a website can still be offline for about 8.76 hours each year. That allowance can disappear during one serious incident, which is why the question “what is website uptime” needs a more useful answer than a percentage printed on an SLA. (LogicMonitor explains the downtime
server monitoring
At 3:00 AM, an on-call engineer gets paged for elevated CPU. Grafana shows several spiking hosts, Prometheus has a dense set of time series, and UptimeRobot says one endpoint is intermittently unavailable. The engineer opens a separate log search, checks a deployment channel, and tries to determine whether the
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.
uptime monitoring
A lot of teams meet MTTF mean time to failure in the worst possible way. A pager goes off in the middle of the night. A pod died again. A runner vanished mid-build. A disk that looked fine yesterday is now gone, and the on-call engineer is left asking 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