Alerter en cas d’échec ou d’abus

Dashboards only help when someone is looking. Alerting flips that around: the system tells you the moment something goes wrong. 🔔

Aim alerts at user-facing symptoms like a high error rate, not at every internal blip. This keeps signal high and noise low.

An alert needs a clear threshold: fire when the error rate crosses, say, five percent. The line turns a metric into a yes-or-no decision.

Judge thresholds over a time window, like five minutes. A single bad call should not page you, but a sustained spike should.

Slow is a kind of broken. Alert when p99 latency climbs past your budget, since a server that hangs hurts users just like one that errors.

A sudden flood of calls can mean a runaway model or an attacker. Alert on abnormal traffic so you catch abuse before it drains your budget. 🚨

Pair alerts with a rate limit: cap calls per client so abuse is contained while you investigate, rather than melting your backend first.

Even a basic counter check can flag a spike when calls in a window blow past your normal baseline.

An alert is only useful if it reaches a human. Send it to a real channel like email, Slack, or a pager that someone actually watches.

Too many false alarms train people to ignore alerts. Tune thresholds so each page is real; alert fatigue is how outages get missed.

Attach a short runbook to each alert: what it means and the first steps to take. The responder then acts in seconds, not minutes.

One choice keeps alerts trustworthy.

You can now alert on real symptoms: error rate, latency, and traffic abuse, tuned with windows and runbooks so every page truly matters. ✅