Monitor application uptime with separate signals for service response, readiness to handle traffic, scheduled-job completion, and performance trends. No single green check proves that every user journey or background task is working: each check should be designed to detect a specific failure.
What application uptime monitoring needs to detect
Availability monitoring is most useful when its signals match the failures you care about. A successful HTTP response can show that an endpoint answered, but it does not necessarily show that the service is ready, that a scheduled task finished, or that a user-facing workflow works end to end.
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- Response: Can a process or endpoint answer a request?
- Readiness: Is the service prepared to handle its intended work?
- Job completion: Did scheduled work report success on time?
- Performance and health trends: Are signals changing in ways that may affect users?
Choose a check for each failure mode, then decide where it runs, how quickly it should alert, and who responds.
What should a health endpoint return?
Define the endpoint’s contract before wiring it into an alert. A health endpoint may only mean that the process can respond; a readiness endpoint can make the stronger claim that the service is prepared to serve traffic. The status code alone is not enough unless its meaning is documented for that application.
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Health and readiness are different signals
Prometheus provides a concrete, product-specific example: its management API documents /-/healthy as always returning HTTP 200 for Prometheus health, while /-/ready returns HTTP 200 when Prometheus is ready to serve traffic and answer queries. These semantics describe Prometheus, not a universal rule for application endpoints.
Decide what readiness guarantees
For your application, specify what must be true for readiness to succeed. Decide which dependencies, if any, are part of that guarantee, and whether the service can handle the work it is expected to receive. A lightweight endpoint that always returns success may be useful for process liveness, but it should not be presented as proof that every dependency or customer workflow is healthy.
Use a separate end-to-end check when you need evidence about a user-visible journey. Keep its scope clear too: a successful synthetic request verifies only the path and conditions that the check actually exercises.
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How to tell if a cron job stopped running
A cron heartbeat monitors whether a scheduled task reports in on time. The monitor expects a request to a unique URL at a configured frequency; if it does not arrive within the frequency and grace period, Better Stack says it triggers an incident and alerts the configured on-call team. See its cron and heartbeat monitor documentation for the product’s setup and behavior.
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Put the success signal after the work that matters
Send the success heartbeat only after the task has completed the work whose success you need to verify. Better Stack’s example sends it after a database export, upload, and cleanup. If a script can encounter an error yet continue to its end, make its failure path explicit: do not send a success signal for work that failed.
Configure the schedule and first check
- Create a heartbeat monitor and set its expected run frequency and grace period to match the job’s schedule and acceptable delay.
- Have the job call its unique heartbeat URL after the successful completion point. Better Stack’s example uses a daily run at midnight and reports after the backup workflow.
- Configure a failure path to report failure rather than success. Better Stack documents appending
/fail; its example can include an exit code and output for diagnosis. - Account for initial setup: the monitor remains pending until its first request, and its monitoring period begins with that first heartbeat.
Treat the heartbeat URL as a secret. Do not publish it or expose it in public logs, repositories, or documentation; anyone who can call it may be able to falsify the job’s signal.
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Which monitoring approach should you use?
Different probe methods observe different parts of a system. A centralized probe checks from a control-plane perspective; passive checks infer health from existing traffic; active mesh checks generate additional probe traffic. In Kuma’s Dataplane Health documentation, passive circuit-breaker evaluation, centralized Kubernetes or Universal service probes, and active mesh health checks are described as distinct approaches.
| Approach | What it observes | Trade-off or blind spot |
|---|---|---|
| Health or readiness endpoint | Whether an endpoint responds, or meets the readiness contract its application defines. | A green result does not establish that all user journeys or background jobs work. |
| Cron heartbeat | Whether the job sent its expected completion or failure signal on schedule. | It depends on the job calling the correct URL at the right point; the URL must be protected as a secret. |
| Passive mesh check | Health inferred from existing traffic and circuit-breaker behavior. | It depends on traffic being available to observe, so it may not reveal a problem before relevant requests occur. |
| Centralized service probe | Service status from a Kubernetes or Universal control-plane probe. | It depends on control-plane availability. |
| Active mesh check | Health from probes sent between mesh dataplanes. | It adds traffic; Kuma notes that extra traffic grows quickly as the number of dataplane proxies rises. |
The table describes the scope and trade-offs established in Prometheus, Better Stack, and Kuma documentation; it is not an independent performance comparison. For any option, set the check interval or expected job frequency, grace period and retries, alert routing, operational dependencies, and diagnostic detail to fit the failure you need to catch.
What metrics should you monitor?
Metrics add trend and diagnostic context to binary availability checks: they can help show how performance or health signals behave over time, while a reachability check answers whether a probe received a response. The documentation cited here does not establish a universal metric list or alert threshold for every application.
Choose metrics based on the service’s own failure modes and objectives. Alert on conditions connected to user impact, and use metrics to investigate changes that a simple up-or-down check cannot explain. A metrics scrape by itself does not verify that an external user can complete a full journey.
Quick Recap
Build an actionable monitoring plan
- Match signals to failures: Separate process response, readiness, scheduled completion, and user-facing journey checks where each matters.
- Set detection timing: Choose probe intervals or job frequency and grace periods based on how quickly you need to know about a failure.
- Define response ownership: Route incidents to an on-call responder and ensure the alert describes what failed.
- Preserve diagnostic evidence: For jobs, report failure explicitly and include useful output or exit information where supported; for services, pair availability signals with metrics that help explain a change.
- Review operational dependencies: Account for monitoring-service availability, control-plane dependence, probe traffic, and protection of heartbeat URLs.
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