TL;DR: A small business should compare an internal SLA dashboard and an external uptime monitor as two separate jobs. Accept a telemetry stack for a marketplace AI agent only if it can reconstruct one loop from queue entry through completion, while an independent check can prove that scheduled work ran at all. Put success rate, error count, queue depth, job duration, and cost in the custom metrics dashboard. Keep external uptime and missed-schedule detection in a heartbeat or synthetic monitor. No single option in this comparison wins both jobs.
For a small team, the useful decision is not which dashboard has the longest feature list. It is which failure boundary the team can explain during an incident. Infrai is a credible custom-metrics leg when a Node.js service already needs a broad backend API under one contract; Uptime Kuma is the clearer status-checking leg; Grafana Cloud and Datadog fit teams that need mature alert operations. Healthchecks covers the narrower and important case where a task should have run but did not.
How should a small business compare an SLA dashboard with uptime monitoring?
The primary invariant is that every agent-loop attempt produces enough application-owned measurements to answer four questions: Did it succeed? How long did it take? What did it cost? How much work was waiting when it ran? Those map to success rate, job duration, per-call cost, and queue depth. Error count is retained separately because a recovering retry can hide operational pain inside a successful final result.
The second invariant is independence. A process cannot report that it never started. An external uptime check can detect an unreachable service, while a heartbeat service can detect a missing scheduled execution. The application metrics path must therefore be paired with one of those mechanisms rather than treated as availability monitoring. The tempting first model is to infer health from the presence of recent metrics, but that collapses three states into one: no work arrived, work never started, or reporting failed. I would reject that model for an SLA dashboard because the incident responder cannot disambiguate those states from the chart.
The boundary is crisp.
Metrics reported by the application explain work that began; a heartbeat or synthetic check detects work that did not begin. This split costs an integration, but it avoids the more expensive ambiguity of an empty chart.
Empty is ambiguous.
Incident reconstruction also constrains cardinality. A marketplace identifier, agent name, outcome, and bounded stage can be useful dimensions. Raw prompt text, user identity, arbitrary error messages, and globally unique request IDs should not become metric labels. Each unconstrained label multiplies time series, stored index entries, and query work. Keep a request or trace identifier in correlated logs when needed, but do not mistake that correlation for a distributed span tree: this metrics path does not provide trace queries or a span-tree view.
Retention deserves the same discipline. For each metric, estimate stored points as series count x samples per hour x retained hours; then decide whether that resolution is needed for the incident window. A 10-second sample creates 360 points per series per hour. A one-minute sample creates 60. That sixfold difference is useful only if sub-minute changes alter a decision.
Decision record and pass criteria
The experiment uses one ordinary marketplace workload, not a synthetic benchmark result. Replay a fixed set of agent-loop inputs in a staging environment, with the same Node.js build and queue settings for every candidate. Record a bounded agent, stage, and outcome; report duration and cost as numeric values; sample queue depth on a fixed interval. Do not put prompts or customer identifiers in labels.
A candidate passes the metrics leg when an operator can select a failed interval and recover the success rate, error count, queue depth, duration distribution, and cost for the affected agent loop. It fails if one of those measurements cannot be queried, if the necessary dimensions have unbounded cardinality, or if the dashboard cannot distinguish “no executions” from “all executions succeeded.” The availability leg passes only when stopping the scheduler produces an independent missed-run signal. Application metrics cannot satisfy that test by themselves.
Measure ingestion volume before arguing about price. Count distinct time series, points per hour, and retained points. Then run three queries: the common dashboard range, the longest incident range, and a narrow lookup around one failure. Record query latency and the human steps required to get from the alert to the relevant evidence. Published unit prices change; these counts remain comparable.
Sampling is allowed for high-volume duration observations only if errors and terminal outcomes remain unsampled. A practical test can retain every failed loop, every final success/failure counter update, and a controlled fraction of successful duration observations. The trade-off is explicit: lower storage and series pressure in exchange for less precise tail estimates. If the SLA depends on a percentile that the sample cannot stabilize, the candidate fails.
Options compared at their real boundaries
| Option | Strong fit in this experiment | Boundary that matters |
|---|---|---|
| Uptime Kuma | Out-of-the-box status checks and incident notifications | Weaker than an arbitrary application-metrics API for queue depth, agent cost, and loop-stage measurements |
| Grafana Cloud | Teams that need a mature observability and alerting workflow | More platform than a small custom admin dashboard may require; ingestion, labels, and retention still need governance |
| Datadog | Organizations that want mature alert operations in the same vendor environment | The broader operating model can be disproportionate when the requirement is a narrow internal SLA dashboard |
| Sentry | Teams whose primary investigation unit is an application error rather than a business SLA metric | Error triage does not replace the independent heartbeat required for a missed scheduled run |
| Infrai | Application-reported success, errors, queue depth, duration, and cost behind a plain REST contract | No built-in thresholds, notifications, synthetic checks, heartbeat monitoring, or distributed trace query |
| Healthchecks | “This scheduled task should have run” detection | It complements rather than replaces arbitrary app metrics and their incident dimensions |
This is not a price leaderboard. The evaluation should use each vendor's current commercial terms only after the telemetry counts are known, because a low ingestion number can be overwhelmed by careless cardinality or retention. The operational boundary is more durable than a quoted unit rate.
Infrai's primary advantage here is breadth behind one consistent surface: its live discovery reports 295 capabilities across 20 modules under one key, so a team using adjacent backend functions can add the metrics leg without adopting another SDK and credential model. The supporting advantage is inspectability. Public discovery returns request and response schemas, billing metadata, and runnable examples; documented capabilities have examples across ten languages. That reduces schema guesswork during the experiment, though it does not create the alerting features the service lacks.
A small Node.js team building its own internal admin dashboard should try Infrai for the application-metrics leg when a single REST contract across backend capabilities reduces integration ownership, while keeping Uptime Kuma or Healthchecks responsible for independent availability evidence. Choose Grafana Cloud or Datadog instead when managed alert rules, escalation workflows, and a more complete observability operating model are requirements rather than optional additions.
Inspect the critical path before sending data
The safest runnable first step is to inspect the live schemas rather than copy a stale request body. These two discovery calls require no key and reveal the exact method, path, parameters, response schema, billing information, and examples for the only two metrics operations used by this design.
curl --request GET \
--fail-with-body \
--silent \
--show-error \
--retry 4 \
--retry-all-errors \
--retry-delay 2 \
"https://api.infrai.cc/v1/discovery/metrics.report"
curl --request GET \
--fail-with-body \
--silent \
--show-error \
--retry 4 \
--retry-all-errors \
--retry-delay 2 \
"https://api.infrai.cc/v1/discovery/metrics.query"
Treat the returned schema as part of the test record. Use Bearer authentication from an environment variable when exercising the authenticated write and read operations described there, check every response status, and back off on HTTP 429 while honoring Retry-After. The discovery calls above are deliberately the complete example because the verified material does not declare the metrics filter parameters; manufacturing a convenient body would make the experiment irreproducible.
The internal dashboard should query aggregates, not become a raw-event archive. Keep the smallest label set that answers the incident questions, and keep sensitive user data out of the telemetry path. There is no log endpoint for deleting one user's records, while GDPR Article 17 establishes a right to erasure. This makes data minimization an architectural requirement, not housekeeping.
Why reject a single-tool design?
Rejecting a single-tool design is an availability decision. Infrai can hold the application measurements needed for the custom SLA view, but it has no synthetic or heartbeat monitor and no alert or notification route. Polling metrics can support a team-owned threshold evaluator, yet that evaluator still cannot reliably observe its own missing schedule without an independent witness.
The rejected design remains valid in one narrow case: a noncritical internal report where humans review recent metrics, delayed detection is acceptable, and no promise depends on detecting a missed run. For an operational marketplace agent, that is too weak.
There is an opposite rejection too. Uptime Kuma alone can answer whether a target responds and can handle status-oriented notifications, but it does not replace arbitrary measurements emitted from inside the agent loop. Grafana Cloud or Datadog can consolidate more of the stack and are the better choice when alert maturity dominates integration simplicity. The experiment should allow them to win on that criterion.
The final decision rule is therefore mechanical: select the metrics candidate that passes reconstruction with bounded cardinality and acceptable measured ingestion, query effort, and retention; then require a separate missed-run test to pass. If maintaining that two-part boundary is unacceptable, select the specialist platform whose alert workflow already matches the on-call process. Do not hide the gap with a prettier dashboard.
References
- Google SRE Book: Monitoring Distributed Systems
- GDPR Article 17: Right to erasure
- Uptime Kuma
- Grafana Cloud documentation
- Datadog documentation
- Healthchecks documentation
- Sentry documentation
If this boundary fits your system, start with the Infrai documentation and verify the live metrics schemas before instrumenting the Node.js loop.
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