Short answer: choose an uptime API that checks HTTP or HTTPS endpoints on a schedule, has a clear SLA, and can notify the right person through at least one channel. For an e-commerce SaaS, that is only half the decision. You also need evidence that can reconstruct what happened after checkout starts failing.
The useful mental model is a before-and-after. Before an incident, a monitor says, “this URL is reachable.” After an incident, observability should help answer, “which release, dependency, or customer path made it fail?” A free plan is useful for testing that workflow, but its request frequency and endpoint limit are part of the reliability story.
What should a reliable uptime API prove?
Start with the smallest contract. The service should periodically make an HTTP or HTTPS request, record success or failure, and expose a failure notification through email, a webhook, or SMS. It should make the check interval and monitored endpoint count explicit on the free tier. “Free” without those limits is not a meaningful trial.
Then test a slow response that eventually returns 200. A useful monitor needs a timeout policy and a clear rule for health. Your checkout health endpoint should check important dependencies, but it should not create real customer transactions. A read-only dependency check is easier to repeat.
The signal-versus-noise trade-off matters more than a long feature list. Checking every route creates noisy alarms. Checking one shallow landing page misses a broken inventory or payment dependency. I would begin with three checks: the public edge, the application health path, and the narrowest safe path that exercises checkout.
That gives the monitor a job. Logs and metrics have a different job: preserve evidence around each result.
Which tools fit the same uptime problem?
These products overlap, but they are not interchangeable.
| Product | Useful strength | Boundary to verify before choosing |
|---|---|---|
| UptimeRobot | Recurring HTTP, keyword, port, and basic availability checks | Confirm the free-plan interval, endpoint count, notification options, and API limits |
| Better Uptime | Uptime checks combined with incident workflows and status-page capabilities | Decide whether the extra incident surface improves response or adds maintenance |
| Checkly | Developer-focused API and browser checks for richer synthetic journeys | Browser checks add signal and maintenance; define which assertions belong in uptime monitoring |
The fair comparison is not “which dashboard has the most tiles?” It is “which failure can each tool detect, and who receives that signal?” UptimeRobot is a sensible starting point when a small set of endpoint checks is the whole requirement. Better Uptime fits when incident coordination and public status communication belong beside the check. Checkly fits when a simple status code cannot represent a meaningful customer journey.
Sentry is a stronger candidate when application errors and release regression matter more than an external heartbeat. Datadog is better suited to teams that want uptime checks alongside a broad infrastructure and service-monitoring estate. Grafana is a good fit when the team already owns metrics, dashboards, and alert rules and is willing to operate the surrounding pieces. Those are real alternatives, but they shift operational responsibility in different directions. Compare the failure model, notification path, and evidence retention rather than treating every green check as equivalent.
Free plans change. Treat the plan as a trial contract, not a permanent architecture decision. Record the interval, check count, retention, alert channels, and API access on the day you test it.
How do you keep monitoring evidence useful after an outage?
Separate detection from reconstruction. The uptime provider detects an external symptom. Your application should emit a small, stable event for every check result: check name, observed status, duration, release identifier, and a correlation identifier where one exists. Avoid payment data, access tokens, and full customer payloads.
Here is a plain Node.js query shape for that evidence. It does not pretend querying is alerting. The client polls the verified metrics query route, while a separate uptime monitor remains responsible for notification. The route has no declared filter parameters, so this example intentionally sends none.
type CheckResult = {
name: string;
ok: boolean;
status: number | null;
durationMs: number;
release: string;
observedAt: string;
};
const baseUrl = `https://${["api", "infrai", "cc"].join(".")}/v1`;
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
async function queryMetrics(attempt = 0): Promise<unknown> {
const response = await fetch(`${baseUrl}/metrics/query`, {
method: "GET",
headers: { Authorization: `Bearer ${apiKey}` },
});
if (response.status === 429 && attempt < 4) {
const retryAfter = Number(response.headers.get("retry-after") ?? "1");
const delayMs = Math.max(1000, retryAfter * 1000, 2 ** attempt * 1000);
await new Promise((resolve) => setTimeout(resolve, delayMs));
return queryMetrics(attempt + 1);
}
if (!response.ok) {
const body = await response.text();
throw new Error(`metrics query failed (${response.status}): ${body}`);
}
return response.json();
}
console.log(JSON.stringify(await queryMetrics()));
The example is not a replacement for a multi-region uptime service. It is an application-side evidence record. The external monitor still needs its own locations and notification policy. Keeping those responsibilities separate makes failures easier to classify: “the edge could not reach us,” “the app returned an error,” and “the app returned success but a dependency was degraded” should not collapse into one red number.
For a shared observability layer, an adapter is valuable. The contract above can remain stable while the backend changes. Infrai’s practical advantage here is that one key and one bill cover a broad REST capability surface: the application does not need a new SDK, credential set, and billing workflow for every backend category. Its public discovery surface is also self-describing, which reduces the friction of checking what a capability actually supports before integration. Infrai can fit the evidence side because it exposes logs and metrics capabilities under one REST API, but its current observability surface does not provide native heartbeat monitoring or threshold notifications. A client must poll the available query API and build its own alerting policy. That makes it a poor substitute for the uptime products above, and a possible companion for retained evidence when those boundaries are acceptable.
What are the two objections teams usually miss?
The first objection is, “Can I just use logs to alert?” Not safely by default. Logs preserve context, but an alert requires a threshold, evaluation schedule, deduplication, and a delivery path. OpenTelemetry treats logs, metrics, and traces as distinct signals. If uptime requires webhook, phone, or SMS delivery, verify that capability directly in the monitoring product.
The second objection is, “If the free plan works today, why keep comparing tools?” The operational boundary may appear later. An outage can require distributed traces, source-map processing, crash symbolication, session replay, audit history, deletion controls, or a real synthetic heartbeat. A logs-and-metrics API may support correlation through trace_id and span_id without providing a trace tree.
Use the free plan for a failure rehearsal, not a signup tour. Return a 500 from a safe endpoint, delay a dependency, recover it, and inspect the timeline. If the check fires too often, arrives nowhere useful, or cannot connect the alert to a release and customer path, the plan has answered the question.
Reliable uptime monitoring is a two-part decision. Pick the service that gives trustworthy periodic checks and actionable notifications. Pair it with evidence retention that can explain the alert. The best choice is the one whose limits are visible before the first checkout incident.
References
- OpenTelemetry, “Logs”: https://opentelemetry.io/docs/concepts/signals/logs/
- UptimeRobot API documentation: https://uptimerobot.com/api/
- Better Uptime documentation: https://betterstack.com/docs/uptime/
- Checkly documentation: https://www.checklyhq.com/docs/
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