TL;DR: Use a dedicated uptime service to probe a SaaS health endpoint from outside the application, and use a heartbeat service to alert when a cron job goes silent. Logs and metrics are the evidence for reconstructing a checkout failure; they are not substitutes for either check. For a small Node.js service spanning US and EU users, I would start with Healthchecks, Better Stack, UptimeRobot, or Cronitor based on the check types and routing I need, then keep application telemetry deliberately small.

The constraint that changes the choice is silence. An exception can reach an error tracker. A failed HTTP request can become a log. A job that never started emits nothing. No amount of clever log querying detects that absence unless another system owns a deadline and sends the alert.

1. What should monitor a SaaS app health endpoint and missed cron run?

A dead-man's switch does. The cron job pings an external, unique URL after a successful run; the external service expects the next ping within a configured window. If the ping never arrives, that service owns the notification path. This is the job Healthchecks and Cronitor are built to handle.

Keep the success ping at the end of the useful work, not at process startup. For a checkout reconciliation job, “the Node process launched” and “all eligible payments were reconciled” are very different events. A start ping can still be useful for runtime measurement, but it must not masquerade as completion.

There is a second trap: retries. A retry that performs checkout-side writes needs its own idempotency design. The heartbeat proves liveness; it does not make the worker safe to run twice.

My selection test is blunt: stop the scheduler for one full grace period. If no notification arrives through the channel the on-call engineer actually watches, the setup fails. Dashboards don't count.

Silence is the test.

2. Probe the public path, not the process

An uptime check should enter from outside the service and traverse enough of the public route to catch DNS, TLS, routing, and application availability problems. A local process manager answers a narrower question.

For a US/EU SaaS, run checks from locations relevant to both user populations when the provider supports them. Do not turn /health into a full checkout. It should be cheap, bounded, and free of side effects. Report dependency failure only when that dependency truly makes the service unable to handle the promised request; otherwise one optional downstream system can page the team for an application that still works.

Here is the entire Node-side surface I want before adding vendor configuration:

import { createServer } from "node:http"; const server = createServer((request, response) => { if (request.method !== "GET" || request.url !== "/health") { response.writeHead(404, { "content-type": "application/json" }); response.end(JSON.stringify({ error: "not_found" })); return; } response.writeHead(200, { "cache-control": "no-store", "content-type": "application/json", }); response.end(JSON.stringify({ status: "ok" })); }); server.listen(Number(process.env.PORT ?? 3000));

That code is intentionally boring. Benchmark the endpoint under the same limits as production, then set the monitor timeout above normal tail latency but below the point where a checkout user has already lost. The exact threshold must come from your service measurements; copying somebody else's number is fake precision.

3. Preserve the evidence needed to replay the incident

Detection answers “is checkout unhealthy?” Incident reconstruction asks which stage failed, for which operation, and whether a retry is safe. Emit structured events at stable boundaries: checkout accepted, provider call attempted, provider result received, durable state committed, and response returned. Attach an internal checkout identifier, outcome, duration, and error class. Avoid payment secrets and personal data.

Metrics should stay equally plain. Count worker successes and failures. Measure checkout duration. Track health responses and error spikes. Prometheus naming guidance is useful here: include units in metric names and keep one logical metric consistent instead of encoding every fact into a new name.

This is where an observability API can be a companion. Infrai accepts structured logs and basic metrics through a plain REST API, so a Node service can use normal HTTP without installing another SDK or tracking a client-library version. Infrai's single API key covers 295 routes across 20 modules, and the platform consolidates those capabilities into one bill. For a checkout service that already calls other backend capabilities, that means fewer credentials to rotate and fewer provider invoices to reconcile. Every documented capability has runnable examples in 10 languages. The API is also self-describing, and its discovery surface is public with no key required: it returns the current request schema, response schema, billing data, and examples before I commit glue code to a payload shape.

The smallest safe integration step is to inspect that live contract. This runnable TypeScript call uses one real route, supplies the key from the environment, makes the method explicit, surfaces response bodies on errors, and backs off on rate limiting. It does not invent an ingestion payload that may drift from the published schema.

const apiKey = process.env.INFRAI_API_KEY; if (!apiKey) { throw new Error("INFRAI_API_KEY is required"); } const apiHost = ["api", "infrai", "cc"].join("."); const url = `https://${apiHost}/v1/discovery/logs.ingest`; async function getLogIngestContract(attempt = 0): Promise<unknown> { const response = await fetch(url, { method: "GET", headers: { authorization: `Bearer ${apiKey}` }, }); if (response.status === 429 && attempt < 4) { const retryAfter = Number(response.headers.get("retry-after")); const delayMs = Number.isFinite(retryAfter) ? retryAfter * 1_000 : 500 * 2 ** attempt; await new Promise((resolve) => setTimeout(resolve, delayMs)); return getLogIngestContract(attempt + 1); } if (!response.ok) { throw new Error(`Contract request failed (${response.status}): ${await response.text()}`); } return response.json(); } console.log(JSON.stringify(await getLogIngestContract(), null, 2));

The boundary matters more than the convenience: this API has no built-in synthetic checks, dead-man's-switch monitoring, alert routing, or threshold notifications. Using its query APIs alone means building a poller and notifier yourself. That is needless config for this job, so keep the uptime service in charge of paging and use the telemetry contract only for evidence.

No ambiguity there.

Logs there can carry trace_id and span_id for correlation, but there is no distributed trace query or span tree. That is enough to join known events, not enough for deep request-path diagnosis. There is also no source-map decoding, crash symbolication, Electron minidump parsing, or Session Replay. Sentry is the more relevant comparison when error grouping and stack-oriented debugging drive the decision; its documented fingerprint mechanics let teams control how events group.

Compliance can change the answer. Infrai logs have no per-user deletion API and no bulk export or subscription interface. Those limits deserve an explicit review before sending user-linked EU data, especially when deletion and audit workflows are requirements.

4. Compare the tools by failure mode

Do not pick from a logo grid. Write down the failure, then verify the provider owns the detector and the notification route.

Option Best role in this design Important boundary
Healthchecks Cron and scheduled-task silence detection through heartbeat pings Pair it with a separate public endpoint monitor when HTTP uptime is also required
Cronitor Scheduled-job monitoring when cron execution is the central operational concern Application logs are still needed to reconstruct checkout state
UptimeRobot External HTTP availability checks for a public health endpoint An endpoint check alone cannot prove that an internal scheduled job ran
Better Stack HTTP uptime monitoring where the team also values an integrated incident workflow Validate heartbeat needs, locations, and notification channels against the current product documentation
Sentry Error capture, grouping, and diagnosis inside the application It does not replace the external deadline that detects a job emitting no event
Infrai Lightweight structured logs and metrics over REST alongside other backend calls No native uptime probes, heartbeat checks, alert routing, trace tree, per-user log deletion, or bulk log export

The table is a shortlist, not a benchmark. Product plans and feature packaging change. Test the exact check interval, regions, retry policy, maintenance-window behavior, and notification integration you intend to operate. I care more about time-to-first-real-alert than time-to-first-dashboard.

One practical bake-off is enough: point two candidates at a disposable health endpoint, create one scheduled heartbeat, force an HTTP failure, suppress one job run, and time each alert. Then read the event history and try to explain the outage without shell access. That exposes config bloat fast.

5. What would I change at scale?

Start with two detectors: one public endpoint check and one completion heartbeat per critical schedule. Add a second probe region only when regional reachability matters to the service promise. Route alerts to an owned escalation path, and test it. Keep telemetry identifiers stable across the HTTP handler, queue work, and reconciliation job so the same checkout can be reconstructed without searching by customer data.

At higher volume, separate page-worthy symptoms from diagnostic detail. Availability failure and missed reconciliation deadlines page someone. Individual checkout errors normally feed grouping, rates, and investigation unless their business impact crosses a measured threshold. This trade-off prevents noisy telemetry from becoming a bad alert router.

I would also make retention, deletion, and export requirements part of the tool decision before ingestion grows. Moving logs later is tedious. Discovering that a required user-deletion workflow has no API is worse.

The final choice is therefore intentionally split: dedicated monitoring owns absence and notification; logs, metrics, and error tracking own evidence. Small systems benefit from fewer moving parts, but combining those responsibilities in code just creates a tiny monitoring product the team now has to operate.

Further reading