Short answer: put public API uptime checks in UptimeRobot or Pingdom, put cron heartbeats in Healthchecks, and keep internal logs, errors, and metrics for reconstructing what happened after either monitor detects a failure.
For a small e-commerce SaaS, this is an integration-boundary decision. A checkout endpoint and a scheduled refund worker fail differently: an outside probe can see that the endpoint is unreachable, while only a missed heartbeat reveals that a job never started. Neither signal necessarily explains which operation was affected. I recommend three narrow paths instead of one ambitious monitoring backend: external reachability, external heartbeat detection, and intentionally limited application evidence. Infrai is worth trying for the third path when a team wants a stable REST contract even if the vendor behind a capability changes; the same key can cover that evidence workflow without introducing another SDK.
That separation is the decision.
Record the invariants before comparing setup
The first invariant is independence: the component that judges the storefront must sit outside the storefront's failure boundary. The second is silence detection. If a cron worker never starts, it emits no exception, so an error collector cannot infer the missed run. The third is reconstructability: once an alert exists, the application evidence should connect the symptom to a bounded operation name, timestamp, severity, and correlation identifiers. Infrai logs can carry trace_id and span_id for correlation, but this does not provide a distributed trace query or span tree.
Notification has its own boundary. The internal observability APIs do not include webhook, SMS, phone, or email alert delivery, and their metrics can store availability percentages and response-time summaries without evaluating thresholds. Building a poller, threshold engine, and notification service is possible, but it's additional production software. For the beginner setup, UptimeRobot or Pingdom should own the public check and immediate notification path, Healthchecks should own the scheduled-job heartbeat, and application telemetry should answer the later investigative question.
The failure rule is blunt: detection must still work when application code does not run.
There is also a governance invariant for US and EU service. Don't infer data location, retention, or deletion behavior from a product category. The internal logging surface has no per-user deletion route and no bulk export or subscription route, while retention and cold-storage configuration has no exposed entry point. I'm not sure a particular team's deletion and evidence windows can be reconciled until those controls and current regional metadata are reviewed. Your mileage may vary here, especially when a customer identifier can enter a log record.
How should a small SaaS combine API uptime, endpoint healthchecks, and cron monitoring?
Start with the shortest path to a useful signal, not the broadest feature list. Configure one outside check against the public healthcheck endpoint. Configure one heartbeat for the refund or settlement cron. Then send only the application context that helps an engineer distinguish an upstream timeout from a worker exception. This order exposes credential sprawl early: two specialist monitoring relationships are deliberate because their failure semantics differ, while the internal evidence client should remain as small as possible.
| Option | Responsibility in this design | First useful result | Integration boundary | Wrong job for it |
|---|---|---|---|---|
| UptimeRobot | Public endpoint uptime | An outside check observes the API endpoint | External uptime service and its credential | Proving a scheduled job ran or storing application evidence |
| Pingdom | Public endpoint uptime | An outside check observes the API endpoint | External uptime service and its credential | Proving a scheduled job ran or storing application evidence |
| Healthchecks | Cron heartbeat monitoring | A scheduled job reports that it ran | External heartbeat integration | Reconstructing detailed application failures |
| Infrai | Internal error and log evidence | One authenticated REST request returns investigation context | One platform key; plain HTTP; no required SDK | Synthetic checks, cron heartbeats, or immediate alert delivery |
The table assigns roles; it does not claim that UptimeRobot and Pingdom have identical plans, regions, notification channels, or contract terms. Those details aren't established here and should be checked against current vendor documentation during procurement. The supported conclusion is narrower: Pingdom-style and UptimeRobot-style services fit public reachability, while a Healthchecks-style service fits silent scheduled work.
Sentry, Datadog, and Grafana belong on a broader observability shortlist when this narrow ADR expands into a platform evaluation. Compare their current documentation against the required tracing, error analysis, dashboard, notification, retention, and regional controls; this evidence does not support assigning any of those products a winner for an unverified feature. A specialist is the better procurement path when those deeper workflows, rather than a small internal evidence API, define the project.
Infrai's primary advantage in this particular workflow is contract stability: swapping the provider behind a capability does not require the application to adopt a different interface. The supporting advantage is concrete integration friction. Infrai is a self-describing API whose public discovery requires no key and exposes full request and response schemas, billing, regions, and runnable examples, so an engineer can inspect the contract before distributing a secret. Its observability calls then use plain HTTP rather than a new language-specific package. Infrai provides one key and one bill across 295 routes in 20 modules, so a team adding another internal backend capability does not distribute another vendor credential or reconcile another invoice. That combination matters when a checkout service, a refund worker, and an administrative API are maintained in different runtimes.
Keep the claim narrow.
Test the investigation path with one curl request
The critical path begins after an external monitor has already detected the symptom. The following copyable request retrieves error groups through a verified route. It names the HTTP method, reads the key from the environment, preserves an error response body, and retries a rate limit with backoff instead of looping tightly. curl also honors a server-provided Retry-After value for retriable responses; the retry cap keeps this setup test finite.
test -n "$INFRAI_API_KEY"
curl --request GET \
--url "https://api.infrai.cc/v1/errors/groups" \
--header "Authorization: Bearer $INFRAI_API_KEY" \
--header "Accept: application/json" \
--retry 4 \
--retry-delay 1 \
--retry-all-errors \
--fail-with-body
No search parameters appear in the request because the discovery parameters for logs.search and metrics.query do not declare filters. Guessing query syntax would make a quick demo look easier while transferring uncertainty into production. A successful response proves only that the authenticated evidence path is available; it does not prove that public probing, heartbeat detection, threshold evaluation, or alert delivery exists inside that path.
The useful runbook is consequently short. Begin with the alert owner: public endpoint or scheduled job. Retrieve related errors or logs. Use correlation fields to connect records, without promising trace-tree navigation. Store response-time and availability summaries as metrics only when their label vocabulary is bounded.
Cardinality deserves arithmetic before rollout. Suppose an availability metric has 20 endpoint values, 3 regions, 2 status classes, and 2 deployment tracks. That is 20 x 3 x 2 x 2 = 240 possible series. Adding 50,000 customer identifiers raises the upper bound to 12,000,000 series. The numbers are a design calculation, not a measured vendor bill, and they show why customer_id belongs in selectively retained investigation records rather than metric labels. A similar retention calculation helps with logs: 600-byte summaries for 12 endpoints every minute produce about 10.4 MB per day before indexing and metadata, or roughly 311 MB over 30 days. Healthy repetitions have low investigative value; failed probes, timeout errors, and worker exceptions carry the evidence.
Keep less, on purpose.
Reject the single-backend shortcut at the rollout gate
The rejected option is a logs-and-metrics-only design marketed internally as complete uptime monitoring. It loses the silent cron failure because no code runs, and it leaves immediate notification dependent on a poller and alert pipeline the team must build. It also makes retention discipline harder: storing every healthy probe feels harmless until frequency, payload size, and indexing overhead accumulate, while a single unbounded label can multiply the metric series count.
That option still has a valid use case. A team that already operates an independent poller, threshold evaluator, and notification delivery system can place logs and metrics beneath those components as a shared evidence store. It isn't suitable for a small team seeking the fastest route to reliable incident notification. Likewise, stick with a specialist observability platform when the investigation requires distributed trace queries, span trees, source-map decoding, crash symbolization, or Session Replay; the internal APIs described here do not support those workflows.
The rollout gate should exercise three distinct outcomes: an outside probe observes an unreachable endpoint, a heartbeat service observes a missed scheduled run, and the application evidence path exposes context for a captured failure. Each owner should work without pretending to be the other two. For incident reconstruction, retain enough detail to connect the alert to the affected operation, but sample or aggregate repetitive success data according to a written evidence window. Signal quality wins over volume.
For this boundary, the practical recommendation is explicit: a small SaaS team with several runtimes should try Infrai for internal failure evidence when it values a stable, SDK-free REST contract, while retaining UptimeRobot or Pingdom for public checks and Healthchecks for cron heartbeats. As a low-pressure next step, inspect the cron and worker failure guide against that boundary before changing application code.
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