Observability for DevOps & SRE
Observability for DevOps & SRE
DevOps and SRE teams face a constant tension: shipping software faster without compromising system reliability. Solving it takes more than monitoring tools — it takes an observability practice built into their daily workflows, with data that lets them make fast decisions with confidence.
Apiwan designs and implements the observability layer specifically for engineering teams: from defining SLOs and Error Budgets to intelligent alert management and incident response automation. The result is a more autonomous team, lower MTTR, and a data-driven engineering culture.
Who is this service for?
Ideal for organizations that:
- Have DevOps or SRE teams that need deep operational visibility and tools to manage system reliability.
- Want to implement SLOs but don’t know where to start or how to translate them into concrete metrics and monitors.
- Suffer from alert fatigue: too many alerts, little real signal, teams that start ignoring them.
- Need to reduce MTTR (mean time to resolve incidents) through better visibility and more agile response workflows.
- Are adopting SRE practices and want observability to be the technical pillar that supports them.
What does the service include?
Design and implementation of SLOs, SLIs, and Error Budgets
We translate business reliability objectives into measurable technical metrics. We define the right Service Level Indicators (SLIs) for each critical service (availability, latency, error rate), set SLO targets aligned with business agreements, and configure Error Budgets that let teams manage the balance between delivery speed and stability.
All of this lives in Datadog: real-time SLO dashboards, alerts when the burn rate accelerates, and compliance reports for stakeholder reviews.
Alert management: from noise to signal
Alert fatigue is one of the most costly problems in IT operations. We audit the current state of monitors, eliminate redundant or poorly calibrated alerts, and design an alerting strategy based on business symptoms rather than isolated technical causes. We implement:
- Composite monitors and multidimensional alerts
- Severity classification and smart routing by team or service
- Alert suppression during scheduled maintenance
- Grouping of correlated alerts to reduce notification volume
Integration with ITSM and on-call tools
We connect Datadog with the team’s operational stack: Jira (automatic ticket creation from alerts), ServiceNow (enterprise incident management), PagerDuty and Opsgenie (escalation and on-call rotation), and Slack or Teams (contextualized notifications in team channels).
The goal is that when an alert fires, the context needed to resolve it is already available in the right channel, with the right team.
Incident lifecycle management with Datadog Incident Management
We implement Datadog’s Incident Management module to standardize incident response: declaration, classification, role assignment (Incident Commander, Communications Lead), action timeline, postmortems, and incident metrics (MTTR, frequency, severity).
This turns incident management into a repeatable process, with continuous learning and data that helps identify patterns and prevent recurrence.
Operational runbooks and response automation
We build runbooks linked to Datadog monitors: when an alert fires, the team gets direct access to the recommended diagnostic and resolution procedure. Where applicable, we implement first-response automations (service restarts, resource scaling, deployment rollbacks) integrated with alert workflows.
Observability in CI/CD pipelines
We extend visibility beyond production: we instrument CI/CD pipelines so engineering teams can see the impact of each deployment on production metrics. Automatic correlations between deploy events and changes in latency, error rate, or SLOs make it possible to catch regressions in minutes, not hours.
Engineering metrics we enable
| Metric | What does it measure? |
|---|---|
| MTTR (Mean Time to Restore) | Incident recovery speed |
| MTTD (Mean Time to Detect) | Time between failure and detection |
| Deployment Frequency | Frequency of production releases |
| Change Failure Rate | 10% of deployments that cause incidents |
| Error Budget Burn Rate | Consumption speed of the error budget |
| Alert-to-Ticket Ratio | Proportion of alerts that generate real action |
