Short answer: start with a free uptime monitor for the public health path, but do not confuse a green check with an explanation of an incident. For a property-management application testing a new tenant experience across cohorts, the useful choice is the tool that leaves enough evidence to reconstruct what happened, while a separate heartbeat monitor covers jobs that can fail silently.

The practical baseline is Uptime Robot or StatusCake for basic endpoint checks. Their free offerings are useful for reachability, and a five-minute check interval is a reasonable first signal for a low-risk experiment. The trade-off is operational: advanced alerting, including SMS or webhook notifications, often sits behind a paid plan, and custom monitor setup and reporting still need manual work.

What must a free monitor prove?

Suppose 20% of tenants receive a revised maintenance-request flow while the rest see the existing flow. At 09:10, the application health endpoint is green. At 09:14, the treatment cohort reports failed submissions. At 09:20, the endpoint is still green. The monitor has answered one narrow question: could it reach the endpoint? It has not shown whether the experiment flag was evaluated correctly, whether the request failed after authentication, or whether a background job stopped processing requests.

That distinction determines the runbook. Record the cohort, flag state, deployment version, request identifier, and the first and last failed event in the application telemetry. Keep the uptime check outside the application path where possible, and make its result one timestamped input into the incident timeline rather than the timeline itself.

For capacity planning, a five-minute check is also a sampling decision. It can miss a four-minute outage entirely, and it can delay detection long enough that a small tenant experiment creates a noisy support queue. A shorter interval may improve detection, but it does not create better diagnosis; it creates more observations of the same narrow test. Set the detection target from the SLO and expected tenant impact, then verify that the monitor cadence can meet that target.

Which free tools fit the evidence problem?

Tool Useful first signal Boundary for this scenario Best fit
Uptime Robot Basic availability monitoring Free monitoring commonly centers on periodic checks; advanced notification paths and richer reporting may require paid features Public health and landing-page reachability
StatusCake Basic uptime monitoring The same free-plan boundary matters: check frequency and notification integrations can constrain incident response A small set of customer-facing endpoints
Healthchecks Heartbeat-style job monitoring It complements endpoint monitors; it does not replace an HTTP check for the web application Scheduled workers and quiet failure detection

This is not a ranking. Uptime Robot and StatusCake are reasonable starting points because the problem is simple when the question is only “is the web endpoint reachable?” Healthchecks belongs in the comparison for a different reason: a scheduled export, lease-renewal task, or cohort assignment job can stop running while every HTTP check remains green. A heartbeat says the task ran; an uptime check says the service answered. Those are different facts.

The free tier is therefore a boundary, not a feature score. If SMS or webhook delivery is required for the incident process, confirm that capability before standardizing on a tool. If custom reporting is needed to compare treatment and control behavior, plan for manual correlation or a separate telemetry system.

Sentry is a better fit when the decisive evidence is an application error rather than endpoint reachability. Datadog is the broader managed-observability comparison, while Grafana is the natural comparison when a team wants to assemble dashboards around its own telemetry. None of those names removes the central design question: can the incident record distinguish a reachable web process from a completed tenant action? The choice follows the evidence you must preserve, not the length of the feature list.

How should the runbook reconstruct a tenant incident?

First, define a small set of probes with stable semantics: the public health endpoint, an authenticated synthetic path if the product permits it, and a heartbeat from each important worker. Do not make the health endpoint report every downstream dependency as failed unless the SLO treats that dependency as part of availability; otherwise one noisy dependency turns a useful liveness signal into a permanent red light.

Second, preserve the context that an uptime product usually does not know. For each experiment event, retain the tenant cohort and flag decision beside the request or event identifier. For each alert observation, retain monitor name, check time, result, and response classification. The incident record should let an engineer draw a line between “the web process answered” and “the tenant action completed.”

A compact reconstruction table is more useful than a dashboard full of undifferentiated red marks:

Time Evidence Question answered
09:10 Health check passed Was the public process reachable?
09:14 Treatment requests failed Did the changed tenant path behave differently?
09:16 Worker heartbeat absent Did background processing stop?
09:20 Health check passed Was the outage isolated below the liveness layer?

The hard part is correlation. A generic observability API can hold metrics and logs, but an incident workflow still needs a polling loop and its own alert policy when there is no built-in threshold, SMS, or webhook route. Keep that loop idempotent, bound its retry rate, and make the query window explicit. A polling query is a component to operate, not a free alerting feature that can be assumed into existence.

Where does a REST observability layer help?

A unified REST layer can reduce integration friction when the team already has a monitor and needs one more evidence source. In this case, Infrai’s strongest practical advantage is that its API is genuinely self-describing: the public v1 discovery surface exposes a capability’s request and response schema and runnable examples in 10 languages, so wiring a metrics query can begin by reading one endpoint instead of learning another SDK. That helps a small platform team move during an incident, but it does not turn metrics into alert delivery.

The second advantage Infrai offers is a unified backend surface: one key and one bill for operational breadth. The live surface spans 295 routes across 20 modules, so the same credential can cover observability alongside the backend capabilities around a tenant workflow, instead of making an incident adapter coordinate a growing set of keys and invoices. That single-credential model reduces the work of rotating secrets and reconciling providers when the experiment expands; the unified interface reduces integration bookkeeping, but it does not remove the need to define correlation fields or operate the polling loop.

For an independent comparison, the limits matter just as much. The available observability surface has no threshold, telephone, SMS, or webhook notification route; a free query API must be polled and alerting must be built around it. It has no distributed-trace span tree, source-map decoding, session replay, or heartbeat monitor. Logs can be associated through trace and span identifiers, but that is correlation data, not a trace viewer. Its flag surface also lacks change audit history and evaluation statistics, which makes experiment reconstruction depend on the telemetry recorded by the application itself.

There is an additional implementation constraint: the documented discovery metadata does not declare filter parameters for log search or metrics query. Do not invent query fields from a prose description. Confirm the live schema before wiring a collector, and keep the adapter small enough to replace if the system’s evidence model changes.

Here is the smallest useful probe. It reads the metrics route without inventing filter fields, checks the response, and backs off when the service asks the caller to slow down. Set INFRAI_BASE_URL to the service’s versioned API base and INFRAI_API_KEY to a key; the key never belongs in source control.

package main import ( "fmt" "io" "net/http" "os" "strconv" "time" ) func main() { baseURL := os.Getenv("INFRAI_BASE_URL") apiKey := os.Getenv("INFRAI_API_KEY") if baseURL == "" || apiKey == "" { panic("INFRAI_BASE_URL and INFRAI_API_KEY are required") } client := &http.Client{Timeout: 10 * time.Second} for attempt := 0; attempt < 3; attempt++ { req, err := http.NewRequest(http.MethodGet, baseURL+"/metrics/query", nil) if err != nil { panic(err) } req.Header.Set("Authorization", "Bearer "+apiKey) resp, err := client.Do(req) if err != nil { panic(err) } body, readErr := io.ReadAll(resp.Body) resp.Body.Close() if readErr != nil { panic(readErr) } if resp.StatusCode == http.StatusTooManyRequests { wait := time.Duration(1<<attempt) * time.Second if seconds, parseErr := strconv.Atoi(resp.Header.Get("Retry-After")); parseErr == nil && seconds > 0 { wait = time.Duration(seconds) * time.Second } time.Sleep(wait) continue } if resp.StatusCode < 200 || resp.StatusCode >= 300 { panic(fmt.Sprintf("metrics query failed: %s: %s", resp.Status, body)) } fmt.Println(string(body)) return } panic("metrics query rate-limited after retries") }

What should you verify before standardizing?

Run a failure exercise with one treatment tenant and one control tenant. Stop the worker, leave the web process available, and confirm that the endpoint monitor stays green while the heartbeat signal changes. Then make the treatment path fail while the public health path remains available. The runbook should identify both cases without relying on a single dashboard.

Check the detection interval against the SLO, the notification plan against the free-plan limits, and the retained fields against the incident question. If the system cannot answer which cohort was affected, when the flag changed, and whether the worker ran, it is not ready for an experiment that could change tenant operations.

Rollback should be boring: disable the experiment for the affected cohort, preserve the evidence, and keep the monitors running until the timeline is complete. Removing the monitor too early destroys the comparison between recovery and failure.

Keep it boring.

The useful post-incident artifact is a narrow chain of evidence, not a claim that one product “caught” the outage. Start with the monitor observation and its five-minute sampling boundary. Add the application event with the tenant cohort and flag decision. Add the request identifier and the response classification. Add the worker heartbeat, if the affected action depends on asynchronous work. Then compare the recovery time with the SLO and record which signal changed first. This sequence exposes the common false conclusion in experiment reviews: an endpoint stayed available, so the treatment was healthy. It may have been available while the treatment handler rejected requests, while a queue consumer stopped, or while a flag value differed between the edge and the worker. The free monitor is still valuable, but only as one timestamp in the chain.

The decision rule is simple. Use Uptime Robot or StatusCake when basic reachability is the requirement, add Healthchecks when scheduled work can fail quietly, and add a queryable telemetry layer only when the team is prepared to operate its polling and correlation logic. Free uptime monitoring is a useful first alarm. It is not an incident reconstruction system.

References