Infrastructure Monitoring Checklist: What Mid-Market Teams Must Cover

Most tech teams say they "have monitoring." What they usually have is a cloud dashboard, a few CPU alarms, and a Slack channel that goes quiet until a customer complains. That is not a monitoring practice—it is wishful thinking with graphs.

Use this infrastructure monitoring checklist with your CTO, tech lead, and whoever gets paged. Mark each item done, partial, or missing. Prefer missing over "we think it's covered." Target outcome: you can answer what is down, who is woken up, and what to do next in under five minutes for every critical system. For context first, see 7 signs you need 24/7 monitoring and observability vs monitoring.

The checklist

1. List every production environment — cloud accounts, regions, Kubernetes clusters, VMs, managed databases, edge, and SaaS dependencies. Put the list in a shared doc, not someone's head.
2. Assign a primary owner and a backup for each critical system (name people, not "the team"). Publish the list in the same place as runbooks.
3. Rank services by business impact — revenue-critical, customer-facing, internal-only, best-effort — so paging priority matches money and customers, not habit.
4. Define "down" in business language for the top systems (e.g. checkout fails, plant line stops, API 5xx above X%). Write it next to each service so alerts map to impact.
5. Draw dependency maps for top journeys — app → DB → cache → queue → third-party API — and include them in incident channels.
6. Instrument golden signals on every critical service: latency, traffic, errors, and saturation (CPU, memory, disk, connections, queue depth).
7. Enable host/node metrics — disk space, inodes, memory pressure, network errors — with thresholds that fire before the disk is full at 3 a.m.
8. Turn on managed-service metrics for RDS/Aurora, ElastiCache, load balancers, and the Kubernetes control plane; don't assume "the cloud is watching it for you."
9. Track capacity headroom — not only "is it red now?" but "how close to the cliff?" Schedule a weekly glance at growth vs limits.
10. Build journey-level dashboards that answer one question (checkout health, login path, order pipeline). Delete or archive wallpaper panels nobody opens in an incident.
11. Ship structured application logs (JSON or equivalent) with request/correlation IDs so you can follow one failure across services.
12. Centralize logs off the hosts that die with the incident; set retention that covers debug needs and compliance without blank-check cost.
13. Redact secrets and PII before logs leave the app — treat log pipelines like production data paths.
14. Link every critical alert to related logs in one or two clicks; if debug still starts with SSH archaeology, fix the tooling path first.
15. Rewrite alerts around user/business symptoms — error rate, latency SLO burn, queue lag — not CPU alone. Downgrade or delete vanity thresholds.
16. Give every paging alert a severity, an owner, and a runbook URL. If an alert has none of these, it is noise until you add them.
17. Measure alert noise weekly — pages per week, false positives, alerts with no owner — and kill or fix the top offenders.
18. Suppress flapping and duplicates; group related firings so one outage is one conversation.
19. Add monitors for silent failures — certificate expiry, disk fill, backup job failure, crons that never ran, stuck deployments.
20. Write SLOs for your top 1–3 customer journeys (availability or latency). Keep them short enough leadership can fund them. See cloud reliability engineering vs DevOps.
21. Define an error-budget response — even informal: what you stop shipping when burn is high — and put SLO burn alerts next to raw thresholds.
22. Review SLO outcomes monthly alongside tickets closed; treat burn as capacity planning, not a postmortem prop.
23. Add outside-in synthetic checks for critical URLs/APIs from outside the VPC (and multi-AZ/region paths, not only a health endpoint that always returns 200).
24. Monitor TLS and DNS expiration; page before browsers and clients do.
25. Watch third-party dependencies — payment, auth, email, webhooks — with failure visibility and a clear "is it us or them?" path.
26. Stand up an on-call rotation for business-critical systems (daytime-first with after-hours escalation is fine) and write the escalation path: primary → secondary → manager.
27. Document runbooks for the top recurring incidents — restart, failover, rollback, drain queue — and link them from the alerts that should trigger them.
28. Run a post-incident review for every customer-impacting event; track actions to done with owners and dates.
29. Prepare stakeholder communication templates (status page / Slack / email) so engineers are not drafting prose mid-fire.
30. Schedule backups and date your last restore test; unverified backups are speculation. Know RPO/RTO for the most critical data stores.
31. Alert on backup job success/failure, not only "database process is up."
32. Make config and IaC changes auditable so "what changed?" is answerable in the first ten minutes of an incident.
33. Assign a monitoring-cost owner and review cardinality, retention, and unused dashboards on a fixed cadence.
34. Enforce least-privilege access to monitoring tools; audit who can change production alerts and dashboards.
35. Version and patch agents/exporters like any other production dependency; remove staging clones and abandoned projects from production paging.

Score your coverage

Print this table (or copy it) and mark each row. If three or more rows are Missing, fix inventory, alerts-with-runbooks, and outside-in checks before buying another tool.

Buy tools last. First: complete the inventory, rewrite pages so every alert has a runbook, and add outside-in checks. Put a lightweight monthly ops review on the calendar. If you lack capacity to operate this continuously, use a monthly retainer that includes infrastructure monitoring instead of hoping spare cycles appear.

A real checklist is proof you know what matters, can detect failure early, and can respond with ownership—not a longer software invoice.