Cost-Aware SLOs for Kubernetes

SLOs keep services healthy. They should also keep spend healthy. Add cost signals to your reliability targets so scaling decisions stay inside budget.

Define dual SLOs

• Reliability: e.g., 99.9% availability, p95 < 250ms.
• Cost: e.g., waste < 20% (requests vs usage), unit cost <$0.30 per 1k requests, burn rate ≤ 1.2x budget.

Treat cost SLOs as first-class; they get dashboards, alerts, and postmortems.

Instrument the signals

• Emit cost.waste.cpu and cost.waste.memory gauges per service.
• Track cost.unit (dollars per 1k requests or per tenant) alongside latency.
• Annotate deployments with estimated cost deltas and surface them in Grafana.

Tie autoscaling to SLOs

• Scale-out: allow only when both reliability and cost SLOs stay green after the change.
• Scale-in: aggressively rightsize when latency SLOs are green but cost SLOs are red.
• Freeze: if cost SLOs breach, cap HPA max and open a ticket automatically.

Review cadence

• Weekly: services breaching cost SLOs without reliability pressure.
• Monthly: align budgets with product growth; adjust cost SLOs accordingly.
• Post-incident: include “cost regression?” in every RCA.

Rollout tips

• Pilot with one high-traffic service; add alerts and comments on its PRs.
• Publish acceptance criteria: we will not merge changes that push cost SLOs red.
• Celebrate “cost SLO saves” just like latency saves—normalize the behavior.

Reliability and cost are not enemies. When they share SLOs, engineers have clear rules for when to scale and when to optimize.***