Short answer: choose the reliable uptime monitoring API that preserves raw attempts, owner labels, and exportable evidence; treat a free plan as a bounded trial, not the architecture.
A free plan is a useful test, not a decision rule. For a marketplace, the API must preserve a verifiable probe record, attach every check to a cost owner, and expose its failure boundaries. The plan tier comes after those invariants.
The practical design is a small evidence pipeline: scheduled probes produce immutable result events, Postgres keeps the normalized record, and an alert path consumes the same event instead of inventing a second incident state. This gives an operations team enough material to reconstruct what happened to buyers, sellers, and internal services.
How do I choose a reliable uptime monitoring API for a SaaS app?
A marketplace outage is rarely one binary fact. A checkout endpoint can answer in 200 ms while order creation fails downstream. The evidence record needs a timestamp, probe identity, target, location, result, latency, response status, failure reason, and cost owner.
I separate three clocks: scheduler, probe, and ingestion. This matters when a queue pauses for 40 seconds and someone mistakes ingestion time for user impact.
I initially treated the latest status as the useful unit. That was the wrong abstraction for cost attribution. A status answers what is true now; an attempt answers what was observed, where, for which owner, and after how many retries. Keep both if a dashboard needs the first one, but make the attempt the record of truth.
That distinction is small in code and large during an incident.
Small schema. Big consequence.
from dataclasses import dataclass
from datetime import datetime
from typing import Optional
@dataclass(frozen=True)
class ProbeResult:
probe_id: str
target: str
owner: str
scheduled_at: datetime
status_code: Optional[int]
latency_ms: Optional[int]
outcome: str
error_class: Optional[str]
def classify(status_code: Optional[int], error_class: Optional[str]) -> str:
if error_class is not None:
return "transport_error"
if status_code is None:
return "unknown"
return "success" if 200 <= status_code < 400 else "http_error"
The invariants are simple: one event has one probe attempt, a missing value stays missing, and an alert is derived from evidence rather than replacing it.
The decision record: pay for signals, not screenshots
For this marketplace, compare four shapes of monitoring service. The comparison is about evidence and attribution, not brand preference.
The trade-off is operational: a hosted API removes scheduler work but may limit retention and export, while a self-managed probe gives control and adds on-call responsibility. Neither is universally reliable; reliability depends on the boundary the team can actually operate and audit.
| Option | Useful evidence | Cost attribution risk | Failure boundary |
|---|---|---|---|
| Status-only checks | Status, latency, timestamp, location | A check can hide which path failed | Cannot prove a checkout step ran |
| Scripted synthetic checks | Assertions across login, cart, or order | More execution time and secrets | Test data can create false failures |
| Hosted check plus incident workflow | Probe history and notification state | Retention and export may differ | A notification is not endpoint proof |
| Self-managed probe and event store | Full schema and owner labels | Team owns scheduling and storage | Burden shifts inward |
This makes “free plan” a sharper question: free for which signal, for how long, and with what export or retention boundary? A free check that keeps only current state may suit a personal landing page. It is weak evidence for a marketplace dispute.
I would evaluate candidates with one five-minute fixture: a public health endpoint, an authenticated seller endpoint, and a synthetic order path using disposable data. Inspect raw results, timestamp precision, retry behavior, and export. Do not grade a dashboard screenshot.
Public products show why category labels are not interchangeable. Checkly documents synthetic monitoring around browser and API checks. UptimeRobot documents uptime checks and alerting. Better Uptime documents monitoring alongside incident-management features. Those are different control planes; verify which raw attempt data and retention rules apply to the plan under consideration.
How should retries, free limits, and costs be modeled?
Retries are where uptime data becomes deceptively clean. If a probe times out, retries, and succeeds, one successful check hides a transport failure. Store both attempts and define alert policy separately.
For cost attribution, use an event ledger. Each attempt receives an owner, environment, probe class, and retention class. Exact pricing changes; the accounting shape lasts longer.
def build_row(result: ProbeResult, attempt: int) -> dict:
return {
"attempt_key": f"{result.probe_id}:{result.scheduled_at.isoformat()}:{attempt}",
"probe_id": result.probe_id,
"owner": result.owner,
"target": result.target,
"attempt": attempt,
"status_code": result.status_code,
"latency_ms": result.latency_ms,
"outcome": result.outcome,
"error_class": result.error_class,
}
Put budget alerts on executions and retained evidence. Sample verbose response bodies only for failed attempts. Response bodies carry secrets and personal data, so they need a separate retention decision. A 5-minute cadence may miss a 30-second deployment regression; compare detection resolution with the incident objective.
The critical path from probe to incident record
The reliable path has four boundaries: schedule, execute, ingest, and notify. Each gets a timeout and an idempotency key. A notification failure must not erase a successful probe write, and a storage failure must not be silently reported as endpoint downtime.
def record_attempt(client, store, notifier, target, owner):
try:
response = client.get(target, timeout_seconds=3)
outcome = classify(response.status_code, None)
row = {"target": target, "owner": owner,
"status_code": response.status_code, "outcome": outcome}
except TimeoutError:
row = {"target": target, "owner": owner,
"status_code": None, "outcome": "transport_error"}
store.insert_once(row)
if row["outcome"] != "success":
notifier.emit(row)
return row
Persist the attempt before emitting the alert. insert_once must enforce uniqueness because schedulers and queue consumers can redeliver work. A /health response that says only “process is alive” is useful for liveness; it is not a checkout test.
I would reject a design that stores only current status and a rolling availability percentage. It is attractive because the schema is tiny, but it fails incident reconstruction: a later state overwrites the first timeout, and there is no durable link to the cost owner. It is valid for a low-stakes personal site or as a cache in front of a fuller evidence store.
The other mistake is retaining every request and response forever. Keep structured attempt metadata for the incident window, redact or hash sensitive identifiers, and govern response bodies separately. Observability data is still data. A hosted check is a poor fit when its retention or export boundary cannot support an incident review; a self-managed probe is a poor fit when the team cannot operate schedulers, retries, and secret rotation.
My decision rule is narrow: select the plan that exposes the evidence fields and execution boundary the incident objective requires, then test export and failure semantics with the same fixture. If it cannot distinguish a probe timeout from an ingestion delay, it is not enough for cost attribution.
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