SLO-Driven Observability
Turn metrics, logs, traces, and alerts into reliability decisions.
SLO-Driven Observability
Observability is useful when it helps teams make reliability decisions. Service-level objectives (SLOs) connect telemetry to user experience, alerting, incident response, and product tradeoffs.
Use this guide when setting up Prometheus/Grafana, improving alert quality, or formalizing SRE ownership with Assistance.
Start with user journeys#
Pick a small number of critical user journeys before adding more dashboards:
SLO design checklist#
- Define the user journey and owner.
- Choose an SLI that can be measured from the user's perspective where possible.
- Set an SLO target that leaves room for maintenance and real-world failure.
- Define the measurement window and data source.
- Decide which burn-rate alerts page humans and which create tickets.
- Add dashboards for current health, error budget, and recent incidents.
- Review SLOs after incidents and major product changes.
Alert quality rules#
Good alerts are actionable, owned, and tied to user impact. Remove or downgrade alerts that only say a host is noisy, a metric moved without consequence, or a condition self-heals before anyone can act.
Reference architecture#
1Applications and infrastructure2 -> Metrics, logs, traces, events3 -> Prometheus/OpenTelemetry/log pipeline4 -> SLI recording rules and dashboards5 -> Alertmanager/on-call/ticket routing6 -> Incident review and reliability backlog7 -> Product and platform SLO review cadenceReliability review cadence#
SLOs need a review loop, not just dashboards.
Record decisions that change the SLO, alert route, or reliability backlog. Otherwise the observability system drifts into a collection of graphs nobody trusts.
Assistance engagement fit#
- Managed Prometheus for metrics, alerting, and operational signal ownership.
- SRE as a Service for SLOs, incident response, and reliability improvement loops.
- Disaster Recovery Planning for recovery objectives, backup validation, and drills.
- Agent Observability for AI/agent workloads with quality, latency, and cost signals.