Cloud Cost Optimization

Overview

Most cloud cost problems aren't about usage being inherently expensive; they're about provisioned capacity sitting idle or oversized relative to what's actually needed, compounding silently because nothing forces a team to revisit a sizing decision made months ago. Cost optimization is the discipline of closing that gap: rightsizing resources to observed load, matching pricing models (on-demand, committed-use, spot) to how predictable and interruption-tolerant a workload actually is, and eliminating genuinely idle spend (unattached storage volumes, forgotten test environments) that provides zero value at any size. None of this is a one-time project, usage patterns and workloads change, so the waste that gets cleaned up today starts accumulating again immediately.

Quick Reference

Syntax

# Autoscaling tied to actual observed load, not a static guess
resource "aws_autoscaling_policy" "scale_up" {
  autoscaling_group_name = aws_autoscaling_group.app.name
  policy_type             = "TargetTrackingScaling"
  target_tracking_configuration {
    predefined_metric_specification {
      predefined_metric_type = "ASGAverageCPUUtilization"
    }
    target_value = 60.0
  }
}

Examples

# Find genuinely idle spend - an unattached volume costs the same
# whether it's attached to a running instance or sitting orphaned.
aws ec2 describe-volumes --filters Name=status,Values=available \
  --query 'Volumes[*].[VolumeId,Size,CreateTime]'

Visual Diagram

Common Mistakes

• Provisioning for peak load and never scaling back down, leaving steady-state capacity idle the vast majority of the time.
• Using on-demand pricing for a workload that's been running continuously and predictably for months, the exact case committed-use discounts are built for.
• Leaving orphaned resources (unattached storage volumes, unused load balancers, forgotten test environments) running indefinitely because nothing alerts on pure waste the way it alerts on an outage.
• Setting autoscaling minimums far above actual baseline load "to be safe," which quietly defeats much of autoscaling's cost benefit.

Performance

• Aggressive cost-cutting can trade away real performance margin, rightsizing too tightly against average load leaves no headroom for legitimate spikes, causing degradation exactly when it matters most.
• Spot/preemptible instances introduce interruption as a real operational concern; workloads using them need to handle reclamation gracefully (checkpointing, retry logic) or the "savings" show up as reliability cost instead.
• Committed-use discounts lock in capacity assumptions for their term, a workload that shrinks significantly during that period turns a discount into paid-for-but-unused capacity, so commitments should track only genuinely stable baseline usage.

Best Practices

• Tag every resource with owner/team/environment from creation, since cost attribution is what makes every other optimization actionable.
• Rightsize on a recurring cadence using actual utilization data, not a one-time pass, usage patterns drift.
• Match pricing model to workload predictability: committed-use for stable steady-state, spot for interruption-tolerant and flexible, on-demand for genuinely unpredictable or short-lived needs.
• Set up automated detection for idle resources (unattached volumes, empty load balancers) so waste gets caught continuously, not only during periodic manual audits.