Did you last hear about an outage from an angry customer rather than from your own systems? For a growing product, finding problems before users do is the difference between a small blip and a lost account.
Growing teams need monitoring that covers uptime, errors, performance and infrastructure, with alerts that fire only on problems users would notice. Pair it with a simple incident process: a clear owner, a communication channel, a status update routine and a blameless post-mortem after every significant incident.
What is the difference between monitoring and observability?
Monitoring tells you when something known is wrong, such as a server being down or error rates rising. Observability is the ability to understand why, by exploring the system's outputs. It is often described through three kinds of telemetry: metrics (numbers over time), logs (records of events) and traces (the path of a request across services). OpenTelemetry has become a widely used open standard for collecting all three.
What should you monitor first?
A useful starting point is Google's "four golden signals" from its Site Reliability Engineering guidance:
- Latency: how long requests take, especially the slow ones.
- Traffic: how much demand the system is handling.
- Errors: the rate of failed requests.
- Saturation: how full your resources are, such as CPU, memory, database connections or queue depth.
Add external uptime checks from outside your network, error tracking in the frontend, backend and mobile apps, and business signals such as sign-ups, orders or payments, which often reveal problems that technical metrics miss.
Which monitoring tools should a growing team use?
| Need | Example tools | Notes |
|---|---|---|
| Uptime checks | UptimeRobot, Better Stack, Amazon CloudWatch Synthetics | Check key pages and APIs from several locations |
| Error tracking | Sentry, Firebase Crashlytics (mobile) | Groups errors with stack traces and release versions |
| Metrics and dashboards | Prometheus with Grafana, Amazon CloudWatch, Datadog | Self-hosted gives control; managed saves time |
| Logs | CloudWatch Logs, Grafana Loki, Elastic, Datadog | Use structured (JSON) logs and set retention |
| Tracing | OpenTelemetry with Jaeger, Grafana Tempo, AWS X-Ray, Datadog APM | Most valuable once you run several services |
| Alerting and on-call | PagerDuty, incident.io, Better Stack | Routes alerts to the right person with escalation |
You do not need everything on day one. Uptime checks, error tracking and basic cloud metrics cover a lot for a small product.
How do you set alerts without drowning in noise?
- Alert on symptoms, not causes. "Checkout error rate is high" is more useful than "CPU is at a high level for one minute".
- Make every alert actionable. If nobody needs to do anything, it should be a dashboard item or a ticket, not a page.
- Use severity levels. Only urgent, user-facing problems should wake someone up.
- Define service level objectives (SLOs). Agree targets for availability and latency, and alert when you are burning through your error budget too quickly.
- Link alerts to runbooks that explain first steps.
What does a simple incident response process look like?
- Detect: an alert fires or a report comes in.
- Declare: someone confirms it is an incident, sets a severity and opens a dedicated channel.
- Assign roles: an incident lead coordinates; others investigate. In small teams one person may hold several roles, but someone must own the incident.
- Communicate: post regular updates internally and, for customer-facing issues, on a status page.
- Mitigate: restore service first (roll back, scale up, disable a feature flag), then find the root cause.
- Resolve and review: confirm recovery and schedule a post-mortem.
Fast rollback is one of the best mitigation tools you can have; see our guide to zero-downtime deployments.
How should you run a post-mortem?
Keep it blameless: focus on how the system and process allowed the problem, not on who made a mistake. A useful post-mortem records the timeline, impact, root cause and contributing factors, what went well, and specific follow-up actions with owners and dates. Then actually track those actions to completion.
How do you make monitoring part of everyday work?
Monitoring works best when it is not a separate project that one person set up once. Add instrumentation as part of building each feature, include release versions in error reports so you can link problems to deployments, and review the main dashboard briefly in regular team meetings. When a new service goes live, its alerts, dashboards and runbook should be part of the definition of done. Over time this builds a shared understanding of what "normal" looks like, which makes unusual behaviour much easier to spot.
How do you set up on-call fairly in a small team?
Rotate on-call so the load is shared, keep handovers written, and compensate people for out-of-hours work in a way your team agrees is fair. Reduce pages by fixing noisy alerts after every rotation. If round-the-clock cover is beyond your team, a maintenance partner can cover agreed hours; our article on maintenance plans explains what to ask for.
Key takeaways
- Start with uptime checks, error tracking and the four golden signals: latency, traffic, errors and saturation.
- Alert on user-facing symptoms, make every alert actionable and link it to a runbook.
- Use a simple incident process with a clear owner, regular updates and blameless post-mortems.
Want monitoring and incident response that catches problems before your customers do? Request a DevOps and cloud quote from AppifyDevs, or explore our case studies.