Monitoring & Observability Setup
Your users should not be the ones who tell you production is down. We build monitoring stacks that catch problems before they become incidents - and alert setups that wake the right person with the right context.
Get StartedThe Problem
Two monitoring antipatterns show up constantly. The first: nothing. Engineers find out about outages from customer support tickets. The second: alert soup - dozens of Prometheus alerts firing constantly, all tuned to the same generic thresholds, none of which tell you what is actually broken.
Good observability means knowing exactly what is happening in your system at any point. That requires metrics instrumented to your application logic, logs that are structured and searchable, traces that follow a request across service boundaries, and alerts that fire when something is genuinely wrong - not just when CPU briefly spikes.
Our Approach
Define what matters
We work with your engineering team to define SLIs, SLOs, and error budgets. What does 'healthy' look like for your application? This drives everything else.
Metrics and logging infrastructure
We deploy Prometheus for metrics, Grafana for visualization, and Loki for log aggregation. If you are on a managed stack (Datadog, New Relic), we work with that instead.
Application instrumentation
We add metrics and tracing to your application code using OpenTelemetry. RED metrics (Rate, Errors, Duration) for every service. Distributed traces across service boundaries.
Alerting and on-call setup
We configure PagerDuty or Opsgenie with intelligent alert routing. We tune alert thresholds to minimize alert fatigue while catching real issues.
What You Get
- Prometheus metrics collection for all services
- Grafana dashboards (infrastructure, application, business metrics)
- Log aggregation with Loki or ELK stack
- Distributed tracing with Tempo or Jaeger
- SLO dashboards with error budget tracking
- Alerting with PagerDuty or Opsgenie integration
- On-call runbook documentation
- Synthetic monitoring for critical user journeys
Tech Stack
Real Example
Context: E-commerce platform with zero observability. First they heard about production issues was customer support tickets.
Deployed full Prometheus/Grafana/Loki stack in 2 weeks. Mean time to detect incidents dropped from 45 minutes to under 3 minutes.