Monitoring & Observability
Full-stack observability with Prometheus, Grafana, Datadog, or ELK. Dashboards, alerting, log aggregation, and APM that surface issues before users notice.
Best fit
Who Monitoring & Observability is for
Full-stack observability with Prometheus, Grafana, Datadog, or ELK. Dashboards, alerting, log aggregation, and APM that surface issues before users notice.
Teams who hear about outages from their customers
The first signal is a support ticket, and by then the timeline has to be reconstructed from memory. Metrics and logs collected before the incident are what make that unnecessary.
Companies where every alert pages and none help
The channel is loud enough that people stopped reading it, so the one alert that mattered arrived beside a disk warning that has been firing for months. Routing and thresholds are the fix.
Teams whose logs never leave the servers producing them
Debugging means SSH into three machines and grep, and the machine that mattered was replaced by the autoscaler an hour ago. Aggregation makes yesterday's incident searchable today.
Teams who cannot tell which service is slow
A request touches half a dozen services and the only measurement is total latency at the edge. Tracing shows which hop spent the time, rather than which team is most confident it was not them.
Recognise one of these? Describe it and we will tell you what it involves.
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What Monitoring & Observability Includes
How it goes
How we run this engagement
The same four steps on every engagement, whether it is a one-off project or an ongoing retainer.
Step 1
You describe the work
A written request, answered by a senior engineer within one working day
Step 2
Audit & Plan
Full review of current stack, written action plan (AI-powered stack analysis + risk mapping)
Step 3
Execute
Implementation, migration or ongoing management begins
Step 4
Monitor & Support
Continuous oversight, alerts, regular reports (AI-correlated alerts, zero noise)
Overview
About Monitoring & Observability
Production-grade observability that gives your team real-time visibility into application performance and infrastructure health. We build dashboards and alerting with Prometheus, Grafana, Datadog, or ELK that catch problems before your users notice.
AI-Augmented Service
AI-powered alert correlation to reduce noise and surface root causes faster
Monitoring & Observability - Common Questions
It depends on your stack and budget. For self-hosted, we typically recommend Prometheus and Grafana with Loki for logs. For managed solutions, Datadog is excellent. We evaluate your needs and recommend the stack that gives you the best visibility without unnecessary cost.
Yes, that is one of the most common problems we solve. We review your existing alerts, remove duplicates and low-value noise, tune thresholds based on actual baselines, and implement proper routing so the right people get the right alerts.
Service Level Objectives define measurable reliability targets - for example, 99.9% availability or p99 latency under 200ms. If you run production services that customers depend on, SLOs give your team a shared, data-driven way to balance reliability with feature velocity.
Yes. We integrate alerting with PagerDuty, Opsgenie, or Slack and configure escalation policies, on-call schedules, and runbooks so your team knows exactly what to do when an alert fires.
Absolutely. Monitoring agents and exporters are deployed alongside your existing workloads with no disruption. We roll out instrumentation incrementally and validate data collection before configuring alerts.
Usually the opposite. Logging and metric bills grow because everything is kept at full detail for a long time. We sample what is noisy, keep full fidelity on the signals that actually diagnose incidents, set retention per data type instead of one blanket rule, and drop the series nobody has ever queried. You end up seeing more and paying less.