Building a Production-Grade Uptime SaaS to Expose Architectural Blind Spots

A HackerNoon piece published on October 22, 2026 makes a case that most teams learn the hard way: the fastest way to find the blind spots in your architecture is to build the monitoring system that watches it. The article walks through the construction of a production-grade uptime SaaS, and the engineering decisions it surfaces are worth dissecting because they map directly onto the failure modes that take down real systems.

The premise is not new. What is new is the specificity. The author treats uptime monitoring as a distributed systems problem rather than a cron job that curls a URL. That framing matters. A checker that runs on a single box tells you whether that box can reach your service. A production-grade monitor tells you whether your service is reachable from the perspectives that your users actually occupy, and it does so without becoming a second outage.

Probe Topology and the Geography of False Positives

The first architectural decision in any uptime SaaS is where the probes live. A single-region checker is cheap and almost useless. If your probe sits in the same AWS region as your application, a regional control-plane failure takes down both the service and the monitor, and you learn about the outage from your customers. The HackerNoon guide pushes toward a multi-region probe fleet, and the reasoning is sound: reachability is a property of the path between two points, not a property of the server.

A workable topology uses at least three probe regions per target, ideally on different cloud providers and different autonomous systems. The probe agent itself should be a small static binary, ideally under 20 MB, that opens a TCP connection, performs a TLS handshake, issues an HTTP request, and records the timing of each phase separately. Splitting DNS resolution, TCP connect, TLS negotiation, time to first byte, and total transfer time into distinct metrics is what turns a binary up/down signal into a diagnostic instrument. When a target starts failing, the phase breakdown usually tells you why before you open a dashboard.

probe phases (milliseconds, p50 over 5m):
  dns_resolve      4.2
  tcp_connect     11.8
  tls_handshake   38.6
  ttfb           142.0
  transfer        19.4

That table is the difference between "the site is down" and "TLS handshakes are slow in eu-west-1 because the certificate chain grew by one intermediate." The latter is actionable in minutes; the former sends you hunting.

Storage Engines for Time-Series Check Results

The second decision is how to store check results. A naive schema writes one row per check per probe per target, and at a modest scale of 1,000 targets, 10 probes, and a 30-second interval, that is 28.8 million rows per day. Postgres will accept this for a while and then it will not. The guide recommends a columnar time-series store, and the trade-offs are worth spelling out.

The practical answer for most teams building this in 2026 is ClickHouse for raw check results and a rollup table for long-range queries. Raw results live for 30 days; five-minute rollups live for two years. The rollup job runs as a materialized view, which keeps the query path fast without a separate batch pipeline. If you are already running Prometheus, remote-writing probe metrics into it is a reasonable shortcut, but be honest about cardinality: every target-probe pair is a label combination, and label explosion is how Prometheus falls over.

The Alerting Pipeline and the Cost of Flapping

Alerting is where uptime SaaS products earn their reputation. The failure mode is well known: a target blips for one check, the pager fires, the on-call engineer acknowledges, the target recovers, and the cycle repeats at 3 a.m. The guide's answer is a confirmation window: a target is marked down only after N consecutive failures across M probes, with N and M configurable per target. A common default is three consecutive failures from at least two probes, which suppresses single-probe network blips without delaying genuine outages by more than 90 seconds at a 30-second interval.

The alerting pipeline itself should be a queue-backed state machine, not a cron job that scans a table. Each probe result lands on a message queue, a consumer updates the target state, and a separate notifier reads state transitions and dispatches to PagerDuty, Slack, or webhooks. Decoupling detection from notification means a slow webhook cannot delay detection, and a PagerDuty outage cannot cause you to miss an alert. The state machine is small enough to fit in your head:

UP -> DEGRADED  (1-2 consecutive failures)
DEGRADED -> DOWN (3+ consecutive failures, 2+ probes)
DOWN -> UP      (2 consecutive successes, 2+ probes)

Idempotency matters here. Every notification carries a transition ID, and the notifier deduplicates on that ID. Without it, a queue redelivery turns one outage into three pages.

What the Build Exposes About Your Own Architecture

The most useful part of the HackerNoon argument is not the SaaS itself but what building it teaches you about the systems you already run. Three blind spots surface almost immediately.

Egress and DNS dependencies. Your probe fleet depends on DNS resolution and outbound network paths that your application also depends on. When you build the monitor, you discover that your DNS provider is a single point of failure you had never inventoried. The fix is usually a secondary resolver and a cached fallback, but you only find it by watching the monitor fail.

Certificate and configuration drift. Probes that check TLS validity across regions surface expired certificates and mismatched chains that a single-region check would miss. In a multi-region deployment, it is common to find one region serving a stale certificate because the rotation job only ran in the primary.

The observability gap between synthetic and real traffic. Synthetic checks tell you whether a path is open. They do not tell you whether real users are succeeding. The guide is careful to position uptime monitoring as a complement to real-user monitoring, not a replacement. The teams that get the most value run both and correlate them: a synthetic check that passes while RUM error rates climb is a signal that the problem is downstream of the edge.

Operational Costs and the Build-Versus-Buy Calculation

The guide is honest about cost. A three-region probe fleet on small instances runs roughly $60 to $120 per month before storage. ClickHouse on a single node with 500 GB of NVMe handles a mid-sized customer base comfortably; the storage line item dominates once you retain raw results for a year. The build-versus-buy question is not really about money at small scale. It is about whether you want to own the pager for the pager.

For teams already running a serious observability stack, building a focused uptime checker is a weekend project that pays for itself the first time it catches a regional DNS failure. For teams without that stack, a managed provider is the right call, and the HackerNoon article is best read as a specification for what to demand from one: multi-region probes, phase-level timing, confirmation windows, and a queue-backed alerting path.

The broader lesson is that monitoring systems are architectural mirrors. Every assumption you make about your production environment, about DNS, about certificates, about network paths, about the difference between synthetic and real traffic, gets tested when you build the thing that watches it. The blind spots the HackerNoon guide describes are not unique to uptime SaaS. They are the same blind spots that show up in every distributed system, and the cheapest way to find them is to build the instrument that looks for them.