Monitoring & Observability
Know what's happening in production before alerts fire. We don't just collect metrics — we build the observability culture your team needs.
Observability Ecosystem
What we cover
Notice the problem before the alert fires. Metrics, logs, and traces — three pillars, one coherent observability system your team will reach for instead of opening SSH.
Observability Strategy
Metrics, logs, and traces: a unified architecture across the three pillars. Which tool belongs where, and why — with clear decisions, not a pile of options.
Prometheus Setup and Configuration
Prometheus deployment, scrape configuration, recording rules, and remote write for long-term storage via Thanos or Cortex.
Grafana Dashboard Design
RED method (Rate, Errors, Duration) and USE method (Utilization, Saturation, Errors) dashboards for services and infrastructure. Panels your team will actually use.
Alertmanager Ruleset
Meaningful, low-noise alert rules. PagerDuty, OpsGenie, or Slack routing for on-call. Threshold calibration to reduce alert fatigue from day one.
Distributed Tracing
Jaeger or Tempo for request tracing across services. Identify slow endpoints and dependency bottlenecks with trace visualisation.
SLI / SLO Definition
Define Service Level Indicators and SLO targets per service. Error budget tracking and burn rate alerting — the foundation for data-driven reliability decisions.
Observability Setup Process
From metric inventory to on-call integration — an alert system your team can sleep through.
- 1
Metric Inventory
Analyse existing alerting gaps, silent-failure scenarios, and which metrics are actually actionable. Start from reality, not assumption.
- 2
Prometheus Stack
Scrape configuration, recording rules, and remote write to Thanos for long-term metric storage.
- 3
Dashboards & SLOs
RED/USE methodology Grafana dashboards, SLI/SLO definition, and error budget tracking configuration.
- 4
Alerting & On-Call
Meaningful, low-noise alert rules, Alertmanager routing, and PagerDuty/Slack/Teams on-call integration.
From Alert to Action: Incident Flow
When a HighCPU metric breaches its threshold, an actionable Slack notification is created before any engineer looks at a screen. Zero manual intervention.
Alert Rule
alert: HighCPUUsage
expr: avg(rate(container_cpu_usage_seconds_total[5m])) by (node) > 0.85
for: 5m
labels:
severity: critical
team: platform
annotations:
summary: "Node {{ $labels.node }} CPU high"
description: "CPU {{ $value | humanizePercentage }} - sustained 5 minutes"
runbook_url: "https://runbooks.internal/k8s-highcpu"Matched Route
severity = critical
→ ops-critical receiver
Deduplication
group_wait: 30s
group_interval: 5m
Silencing
Outside business hrs
repeat_interval: 1h
Node: worker-node-03 | CPU: 92% | Duration: 7 minutes
Pod: api-gateway-7f8b4c-xk9p2 | NS: production
Why this matters: Nobody has to be watching a screen to notice that production ran at 92% CPU for 7 minutes. The alert arrives with the cause and runbook link already attached — average MTTR drops from 45 minutes to 8 minutes.
The three pillars of observability
Prometheus · Grafana · Thanos
CPU, memory, latency, error rate — numerical data over time. Foundation for trending, capacity planning, and SLO tracking.
Loki · Fluent Bit · OpenSearch
Chronological record of events. Answers 'what happened, when, and in which service' during an incident.
Jaeger · Tempo · OpenTelemetry
Request flow across services. Identifies which service is slow, where dependencies bottleneck, and why latency spikes.
Technologies
Who is this for?
Teams running production systems who want to catch problems before users notice. Specifically, engineering teams with no alerting at all, or where every alert pages the entire on-call rotation (alert fatigue) — and who want meaningful signals that are actually worth waking someone up for.
Ready to talk through your stack?
A direct technical conversation about where you are and what needs fixing.
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