Monitoring & Alerting

Why Monitoring Matters

Production systems fail. This isn’t pessimism; it’s reality. Servers run out of memory. Databases hit connection limits. Third-party APIs go down. Traffic spikes arrive unexpectedly. The question isn’t whether something will break, but whether you’ll know before your customers do.

Without monitoring, teams operate blind. Performance degrades 1% per week until someone notices the application is twice as slow as launch. Memory leaks consume resources until processes crash. Disk fills up because nobody watched the logs grow. These problems are easy to fix when caught early and expensive when they cause outages.

Good monitoring changes how teams think about reliability. Instead of hoping nothing breaks, you measure what matters and respond when measurements cross thresholds. You catch problems during business hours instead of getting paged at 3 AM. You identify trends before they become incidents. Monitoring transforms operations from reactive firefighting to proactive maintenance.

What We Build

We implement monitoring systems that answer real questions, not dashboards full of charts nobody looks at.

Application Metrics:

• Request rates, error rates, and latency distributions (the RED method)
• Endpoint-level breakdowns to identify slow or failing routes
• Database query performance and connection pool utilization
• Cache hit rates and queue depths
• Custom business metrics like signups, purchases, or API usage

Infrastructure Metrics:

• CPU, memory, disk, and network across all hosts
• Container-level metrics for Kubernetes workloads
• Auto-scaling group health and capacity
• Load balancer connection counts and response times
• Database replica lag and transaction throughput

Business Metrics:

• Revenue and transaction volume
• User signup and activation rates
• Feature usage and adoption
• Funnel conversion at each step
• Whatever numbers your business cares about

Synthetic Monitoring:

• Automated checks that simulate user journeys
• API health endpoints polled from multiple regions
• SSL certificate expiration tracking
• DNS resolution monitoring
• External dependency availability

Our Experience Level

We’ve built monitoring for applications serving millions of requests and for MVPs with a handful of users. The tools change, but the principles remain constant.

We’ve deployed Prometheus and Grafana in Kubernetes clusters, configured Datadog for teams that needed managed infrastructure, and built custom solutions when standard tools didn’t fit. We’ve set up PagerDuty rotations, written runbooks for common incidents, and tuned alerting until on-call shifts became manageable.

Specific implementations we’ve done:

• SLO-based alerting — Alerts that fire based on error budgets, not arbitrary thresholds
• Multi-cluster monitoring — Centralized visibility across production, staging, and regional deployments
• Cardinality management — Prometheus configurations that don’t explode with high-cardinality labels
• Grafana dashboards — Layouts that show system health at a glance and support drill-down investigation
• Integration with incident management — Alerts that create incidents, page the right people, and track resolution

When to Use It (And When Not To)

Every production system needs monitoring. The question is how much.

For an MVP or early-stage product, basic health checks and error tracking might be enough. Know when the application is down. Know when errors spike. That’s the minimum viable monitoring.

For a product with paying customers, invest more. Track performance metrics. Set up alerting for degraded service. Build dashboards that show system health. You’re making reliability promises to customers; you need to know if you’re keeping them.

For products with SLAs or compliance requirements, monitoring becomes infrastructure. You need to prove uptime. You need audit trails. You need to demonstrate that you detected and responded to incidents appropriately.

We’ll tell you what level of monitoring fits your situation. Sometimes “basic health checks” is the honest answer. Sometimes you need the full observability stack.

Common Challenges and How We Solve Them

Alert fatigue that makes pages meaningless. When everything alerts, nothing alerts. Teams ignore notifications because most are false positives. We tune thresholds based on real incident data, implement alert grouping, and ruthlessly eliminate alerts that don’t require action. If nobody needs to do anything, it shouldn’t wake anyone up.

Dashboards that nobody looks at. Fifty Grafana dashboards and nobody opens any of them. We build hierarchical dashboards. Top-level views answer “is everything okay?” Service-level views answer “what’s wrong with this service?” Detail views support debugging. Each dashboard has a purpose.

Metrics cardinality explosion. Someone adds a user ID label and suddenly Prometheus can’t keep up. We establish labeling standards, implement pre-aggregation where appropriate, and review new metrics for cardinality before deployment.

Missing context when incidents occur. Alerts fire but nobody knows what to do. We write runbooks for every alert. Each runbook explains what the alert means, how to investigate, and common remediation steps. On-call engineers spend time fixing problems, not figuring out what the alert means.

Monitoring that watches the wrong things. Plenty of infrastructure metrics but no visibility into user experience. We implement monitoring from the user’s perspective: synthetic checks that simulate real workflows, real user monitoring that captures actual performance, and business metrics that show whether the product is working.

Cost surprises from metrics volume. Datadog bills arrive and someone has to explain why monitoring costs more than infrastructure. We right-size retention periods, sample appropriately, and use cheaper solutions for non-critical metrics. Good monitoring doesn’t require unlimited budget.