Learn how to optimize infrastructure costs. Right-sizing resources, using reserved instances, and cost monitoring strategies.
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Cost optimization reduces cloud spending without sacrificing performance. This guide covers strategies.
# Before: Over-provisioned
resource "aws_instance" "web" {
instance_type = "m5.2xlarge" # 8 vCPU, 32GB RAM
}
# After: Right-sized
resource "aws_instance" "web" {
instance_type = "t3.medium" # 2 vCPU, 4GB RAM
}
# Purchase reserved instances for predictable workloads
resource "aws_ec2_capacity_reservation" "reserved" {
instance_type = "m5.large"
instance_platform = "Linux/UNIX"
instance_count = 10
end_date_type = "unlimited"
}
# Use spot instances for non-critical workloads
resource "aws_spot_instance_request" "worker" {
ami = "ami-12345"
instance_type = "m5.large"
spot_price = "0.05"
tags = {
Name = "worker-spot"
}
}
# Infracost for cost estimation
infracost breakdown --path terraform/
# AWS Cost Explorer
aws ce get-cost-and-usage \
--time-period Start=2024-01-01,End=2024-01-31 \
--granularity MONTHLY \
--metrics BlendedCost
Optimize costs by right-sizing, using reserved/spot instances, and monitoring spending. Regular reviews help identify savings.
For Infrastructure Cost Optimization: Reducing Cloud Spending, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Infrastructure Cost Optimization: Reducing Cloud Spending, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Infrastructure Cost Optimization: Reducing Cloud Spending, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For Infrastructure Cost Optimization: Reducing Cloud Spending, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
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