On this page
What monitoring and logging cover
Metrics tell you how the system is behaving: request rates, error rates, response times, queue lengths, resource use. Logs tell you what happened in detail: which request failed, with what error, for which user. Alerts tell someone when a metric or log pattern means action is needed. Together they turn “the site feels slow” into a specific, answerable question.
Without them, every incident starts with someone logging into servers one by one, searching through log files and guessing. With them, you can usually see the problem, when it started and what changed at that moment.
Warning signs
- Debugging means SSH-ing into several servers and searching log files by hand.
- Logs are plain text with no request IDs, so tracing one user’s problem is guesswork.
- There are dashboards, but nobody looks at them.
- Background jobs or webhooks fail and nobody notices for days.
- A SaaS customer reports an error before your team knows anything is wrong.
Another common sign is that incidents take much longer to understand than to fix. When the actual fix is a one-line change but finding it took hours of searching, better visibility is usually the missing piece.
What’s included
- Structured application logs with timestamps, levels and request identifiers.
- Central collection of logs from applications, web servers, containers and system services.
- Metrics for traffic, errors, latency and saturation of key resources.
- Dashboards covering the handful of numbers that indicate real trouble.
- Alerts on error spikes, slow responses, failed jobs and resource exhaustion.
- Retention settings matched to how far back you realistically need to look.
- Documentation on where to look first when something goes wrong.
We also agree who receives which alerts and what they are expected to do. An alert without an owner tends to be ignored, so each one is routed to a person or channel with a short note on the first steps to take.
How we set it up
- Identify the user journeys and background processes that matter most.
- Improve application logging where needed, adding structure and request IDs.
- Deploy collectors and exporters, and connect them to a central store.
- Build focused dashboards and alerts, starting conservatively.
- Review alert history after a few weeks, remove noise and fill gaps.
Alerts are tested by triggering real failures in a controlled way, such as stopping a service in staging, so you know the notification actually arrives and contains enough information to act on. An alert that has never fired in a test cannot be trusted.
Tools we use
A common self-hosted stack is Prometheus for metrics, Grafana for dashboards and alerting, and Grafana Loki for logs. Uptime Kuma covers external uptime checks. Cloud-native options include AWS CloudWatch, Google Cloud Logging and Monitoring, and Azure Monitor. Error tracking tools can capture application exceptions with context. We choose based on your scale, budget and whether you prefer to self-host.
Whichever stack is chosen, it runs separately from the systems it watches, so an outage on your main server does not also take down the tools you need to investigate it.
The few numbers that matter most
Rather than dozens of charts, we start every dashboard with four signals that describe the health of almost any service:
| Signal | What it measures | Example alert |
|---|---|---|
| Traffic | Requests or jobs per minute | Sudden drop to near zero during business hours |
| Errors | Share of requests failing | Error rate rises clearly above normal |
| Latency | How long requests take | Slowest requests exceed an agreed threshold |
| Saturation | How full resources are | Disk, memory or queue backlog approaching limits |
Everything else is added only when it answers a question someone actually asks.
What affects timeline and cost
Basic metrics and log collection for a few services can be set up quickly. More time is needed for many services, applications that need logging improvements, custom business metrics, long retention requirements or integration with on-call tools. Hosted services bill by data volume; self-hosted stacks need a server and storage of their own.
Log volume is the usual driver of cost. We filter noisy, low-value logs at the source and set retention deliberately, so you keep what you need for debugging without paying to store everything forever.
Common mistakes
- Logging passwords, tokens or personal data that should never be stored.
- Building dashboards with fifty panels that nobody reads.
- Alerting on causes like CPU instead of symptoms users actually feel.
- Keeping the monitoring system on the same server it monitors.
Related: Server Monitoring, Performance Optimization and Maintenance & Support. See all DevOps & Deployment services, the DevOps guide, or contact us.
Frequently asked questions
Self-hosted or a hosted monitoring service?
Self-hosting keeps costs predictable and data in your control; hosted services reduce maintenance. We explain the trade-off for your scale.
Will our application need changes?
Sometimes small ones, such as structured logging or a request ID. These make debugging much faster and are usually quick to add.
How long should we keep logs?
Long enough to investigate issues and meet any obligations you have, but no longer than necessary. We agree a period with you.
Can alerts go to Telegram or Slack?
Yes. Grafana and most monitoring tools support common notification channels.
Can you monitor background jobs and cron tasks?
Yes. Jobs can report success or failure, and a missed or failed run raises an alert.
Talk to us about monitoring & logging
Centralised logs and metrics that make debugging a five-minute job.