cost-optimization
Strategies and practices for optimizing cloud spending while maintaining performance and reliability
Source facts
- Repository
- ffsshhttiikk/opencode-agents-skills
- Last source activity
- February 28, 2026 at 22:48
- Detected SKILL.md language
- English
- Stars
- 3
- Forks
- 2
Install options
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Review the source files
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview- name
- cost-optimization
- description
- Strategies and practices for optimizing cloud spending while maintaining performance and reliability
- category
- cloud-computing
# Cloud Cost Optimization
## What I Do
I help organizations maximize the value from their cloud investments by eliminating waste, optimizing resource utilization, leveraging pricing models, and implementing financial governance. I enable cost-effective cloud operations without sacrificing performance.
## When to Use Me
- Reducing monthly cloud spend
- Planning cloud budget and forecasting
- Identifying unused or underutilized resources
- Implementing FinOps practices
- Optimizing enterprise-scale cloud costs
- Managing multi-cloud spending
- Setting up cost allocation and chargebacks
## Core Concepts
- **Right-sizing**: Matching resources to actual workload needs
- **Reserved Instances/Savings Plans**: Committed discounts (30-70% off)
- **Spot Instances**: Excess capacity at up to 90% discount
- **Cost Allocation Tags**: Tracking spending by team/project
- **Budgets and Alerts**: Proactive monitoring of spending
- **Cost Explorer/Analysis**: Understanding spending patterns
- **疏云: Eliminating idle resources**
- **Lifecycle Policies**: Automatic transition to cheaper storage tiers
- **Resource Scheduling**: Turning off non-production resources
- **FinOps Culture**: Cross-team accountability for cloud costs
## Code Examples
**AWS Cost Explorer Analysis (Python/Boto3):**
```python
import boto3
from datetime import datetime, timedelta
ce = boto3.client('ce')
# Get cost and usage by service
response = ce.get_cost_and_usage(
TimePeriod={
'Start': '2024-01-01',
'End': '2024-02-01'
},
Granularity='MONTHLY',
Metrics=['UnblendedCost', 'UsageQuantity'],
GroupBy=[
{'Type': 'DIMENSION', 'Key': 'SERVICE'},
{'Type': 'DIMENSION', 'Key': 'USAGE_TYPE'}
]
)
# Identify underutilized instances
def find_underutilized_instances():
ce = boto3.client('ce')
rightsizing = ce.get_right_sizing(
TimePeriod={
'Start': '2024-01-01',
'End': '2024-02-01'
},
Filter={
'Dimensions': {
'Key': 'RECORD_TYPE',
'Values': ['EC2', 'RDS']
}
}
)
for item in rightsizing['RightSizeingResults']:
if item['TotalActualHours'] > item['TotalRecommendedHours'] * 1.5:
print(f"Instance: {item['ResourceId']}")
print(f"Savings: ${item['EstimatedMonthlySavings']}")
```
**Terraform Cost Estimator:**
```hcl
# terraform/modules/cost-estimator/main.tf
variable "instances" {
type = list(object({
name = string
instance_type = string
count = number
hours_per_day = number
}))
}
locals {
# AWS pricing (example rates)
on_demand_rates = {
"t3.micro" = 0.0104
"t3.small" = 0.0208
"t3.medium" = 0.0416
"t3.large" = 0.0832
"m5.large" = 0.0960
"m5.xlarge" = 0.1920
}
reserved_rates = {
"t3.micro" = 0.0058
"t3.small" = 0.0117
"t3.medium" = 0.0233
}
monthly_cost_on_demand = sum([
for inst in var.instances :
inst.count * inst.hours_per_day * 30 *
local.on_demand_rates[inst.instance_type]
])
monthly_cost_reserved = sum([
for inst in var.instances :
inst.count * 720 *
local.reserved_rates[inst.instance_type]
])
}
output "monthly_cost_on_demand" {
value = local.monthly_cost_on_demand
}
output "monthly_cost_reserved" {
value = local.monthly_cost_reserved
}
output "potential_savings" {
value = local.monthly_cost_on_demand - local.monthly_cost_reserved
}
```
**Kubernetes Resource Optimization (YAML):**
```yaml
apiVersion: v1
kind: ConfigMap
metadata:
name: cost-optimization-config
namespace: kube-system
data:
config.yaml: |
# Resource requests/limits recommendations
recommendations:
frontend:
current:
requests:
cpu: "500m"
memory: "512Mi"
limits:
cpu: "1000m"
memory: "1Gi"
recommended:
requests:
cpu: "250m"
memory: "256Mi"
limits:
cpu: "500m"
memory: "512Mi"
estimated_savings: 40
# Auto-scaling configuration
autoscaling:
min_replicas: 2
max_replicas: 10
target_cpu_utilization: 70
target_memory_utilization: 80
# Pod disruption budget
pdb:
min_available: 1
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: frontend-hpa
namespace: production
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: frontend
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
behavior:
scaleDown:
stabilizationWindowSeconds: 300
policies:
- type: Percent
value: 10
periodSeconds: 60
scaleUp:
stabilizationWindowSeconds: 0
policies:
- type: Percent
value: 100
periodSeconds: 15
```
## Best Practices
1. **Implement FinOps framework** - Cross-team ownership and accountability
2. **Tag all resources properly** - Enable accurate cost attribution
3. **Use reserved instances for steady state** - Commit to predictable usage
4. **Right-size continuously** - Monitor and adjust regularly
5. **Schedule non-production resources** - Auto-stop dev/test environments
6. **Use spot for fault-tolerant workloads** - Batch jobs, stateless services
7. **Implement budgets and alerts** - Proactive monitoring
8. **Review unused resources weekly** - Orphaned volumes, snapshots, IPs
9. **Use lifecycle policies** - S3 Intelligent-Tiering, Glacier for cold data
10. **Conduct regular cost reviews** - Monthly optimization meetings
View on GitHub