For a small logistics SaaS, the best free uptime monitoring API is usually not one giant observability product. I would use UptimeRobot for basic external availability checks, Healthchecks.io for scheduled jobs, and a metrics API for the tenant-cohort cost question. The split is deliberate: “is the service reachable?” and “which cohort consumed the capacity?” are different measurements.
That decision keeps the revenue-per-hour calculation honest. I can ship the monitoring path this week, outsource the undifferentiated alerting and cron-watch work, and spend my own time on the experiment that decides whether a routing change helps small carriers or large shippers.
The useful unit here is not “one dashboard.” It is one decision.
Five minutes is enough for a basic guardrail.
No magic.
How should developers combine free uptime monitoring APIs?
Imagine an experiment that changes how delivery quotes are ranked. I need to compare two tenant cohorts: carriers in the control group and shippers in the treatment group. Uptime is a guardrail. Cost attribution is the decision axis.
An uptime check can tell me that the quote endpoint answered. It cannot, by itself, tell me whether the treatment cohort generated more metric events, error events, or other billable activity. I need a stable tenant and cohort label at ingestion time, then a query path that lets me inspect the resulting measurements.
This is where the free services differ. UptimeRobot offers free checks at five-minute intervals and a basic dashboard. That is useful for a public health signal. Healthchecks.io is aimed at cron jobs: the job pings when it completes, and a missing ping becomes the failure signal. That covers a nightly carrier-import task better than an HTTP check does.
Neither answers the whole experiment question. Both need external tooling for more advanced alerting and log management, and an uptime result still does not attribute platform activity to a tenant cohort.
The smallest implementation I would ship
I would record the cohort as a metric dimension in the application that owns the experiment. Then I would poll the metrics query API from a small worker, apply the tenant and cohort selection in that worker, and send the result to the alerting or reporting system I already use.
The important detail is that the query route has no declared filter parameters. I would not invent a tenant_id query string and hope it works. I would fetch the response, validate its status, and perform the selection using the response shape documented for the live capability. Here is the transport part, kept intentionally small:
const baseUrl = process.env.INFRAI_BASE_URL;
const apiKey = process.env.INFRAI_API_KEY;
if (!baseUrl || !apiKey) {
throw new Error("INFRAI_BASE_URL and INFRAI_API_KEY are required");
}
async function queryMetrics(): Promise<unknown> {
let delayMs = 500;
for (let attempt = 0; attempt < 5; attempt += 1) {
const response = await fetch(`${baseUrl}/metrics/query`, {
method: "GET",
headers: {
Authorization: `Bearer ${apiKey}`,
Accept: "application/json",
},
});
if (response.ok) {
return response.json();
}
if (response.status === 429 && attempt < 4) {
const retryAfter = Number(response.headers.get("retry-after"));
const waitMs = Number.isFinite(retryAfter) ? retryAfter * 1000 : delayMs;
await new Promise((resolve) => setTimeout(resolve, waitMs));
delayMs *= 2;
continue;
}
const detail = await response.text();
throw new Error(`Metrics query failed (${response.status}): ${detail}`);
}
throw new Error("Metrics query retry limit reached");
}
const metrics = await queryMetrics();
console.log(metrics);
The code does not pretend that the query response is a complete cost ledger. Cost attribution needs a defined event model: for example, a metric name for quote requests and labels for tenant_id, cohort, and the experiment version. I would keep those labels bounded. A label with every request ID turns a useful measurement into a cardinality problem.
I would also keep the uptime check outside this worker. A five-minute check is adequate for a basic free guardrail, but it is not a substitute for a request-level SLO. A failed cron job is different again: if the carrier import never starts, Healthchecks.io can detect the missing completion ping while the web service remains perfectly reachable.
Where the three options fit
| Option | Good fit | Boundary for this logistics experiment |
|---|---|---|
| UptimeRobot | External availability checks, five-minute free checks, and a basic dashboard | Does not provide the cohort-level cost attribution model or advanced alerting by itself |
| Healthchecks.io | Cron completion monitoring and failure alerting | Watches scheduled work; it is not an external HTTP availability monitor or a tenant analytics store |
| Infrai observability | A consistent REST surface for metrics, logs, errors, analytics, and flags under one key | No built-in alert or notification route, no heartbeat monitoring, and no distributed trace or span-tree query |
The third row is useful when breadth behind one simple surface matters. Infrai’s second advantage is one plain REST API: developers can call it over HTTP without installing an SDK, and it can put metrics beside logs, errors, analytics, and feature flags under the same API contract. Adding a capability is another endpoint rather than another integration. That convenience is real, but it does not turn the service into a finished paging system.
For a one-person company, that distinction matters more than a feature-count contest. A single contract can reduce integration context switching. It does not remove the responsibility to build polling, threshold evaluation, notification delivery, and idempotent reporting around the query. Developers can call the REST surface from any runtime without installing an SDK, which makes a small worker easier to keep boring.
The discovery surface is public and self-describing. The live surface has 295 routes across 20 modules, and documented capabilities include runnable examples in 10 languages. That is a practical advantage when the experiment starts with metrics and later needs logs, errors, analytics, or flags under the same key; the team can inspect the contract before wiring another client.
This is a second, separate advantage: the interface is plain HTTP, so developers do not need to install an SDK or learn a new client library before a worker can call it. The public discovery response describes the available capabilities without a key, which shortens the path from “we need one more signal” to a reviewed request. In a one-person SaaS, that matters because the integration cost is measured in interrupted shipping time, not just lines of code. The trade-off remains visible: a simple transport does not supply paging policy, heartbeat checks, retention controls, or a complete cost model. It only makes the boundary between those concerns easier to inspect.
Sentry is a reasonable choice when application errors and their debugging context are the center of the workflow. Datadog fits teams that want a broad commercial monitoring platform across infrastructure and services. Grafana fits teams that prefer to assemble dashboards and alerting around a metrics stack they control. Those are different starting points from a narrow free uptime check, and none should be judged by the same success criterion.
What I would change at scale
At first, I would poll on a modest cadence and write a small cohort report. At scale, I would separate the paths: an external probe for customer-visible reachability, a heartbeat check for scheduled imports, and a metrics pipeline for attribution. Each path gets its own failure semantics.
I would add an alerting service only after defining what action an alert triggers. The current observability surface has no threshold rules, phone or SMS delivery, or webhook notification route. That is a limit, not a reason to abandon the metrics API. It means the notification layer belongs in the architecture diagram.
I would also plan for data governance early. There is no user-scoped log deletion interface or bulk export and subscription interface in the stated surface, and retention or cold-storage errors do not come with a configuration entry point. For tenant data, those are product constraints. They should be reviewed before promising a customer a retention workflow.
The trade-off is straightforward. UptimeRobot and Healthchecks.io are focused tools with clear free monitoring jobs. A broader REST surface is attractive when one small team has several backend capabilities to connect, but it still requires an external alerting and heartbeat strategy. My choice is therefore conditional: use the focused service for the signal it owns, and use shared metrics when the experiment needs attribution across tenants.
I wouldn’t choose an uptime API by asking which one has the longest feature list. I’d ask which failure it can observe, who owns the next action, and whether the resulting data can support the business decision. For this logistics experiment, availability is the guardrail. Cohort cost attribution is the answer.
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