cost-optimization

name	cost-optimization
description	**Cloud Cost Optimization & FinOps**: Helps analyze and reduce cloud infrastructure costs, optimize resource usage, implement FinOps practices, and find savings opportunities. Use whenever the user mentions 'cloud costs', 'AWS bill', 'cost optimization', 'FinOps', 'right-sizing', 'reserved instances', 'savings plans', 'cost allocation', 'budget', 'overspending', 'cloud waste', 'cost per request', 'unit economics', asks 'why is my AWS/GCP/Azure bill so high', or wants to reduce infrastructure spending without sacrificing performance.
triggers	{"frameworks":["aws","gcp","azure","terraform"],"file-patterns":["**/terraform/**","**/cloudformation/**"]}
preferred-model	sonnet
min-confidence	0.4
depends-on	["devops-infra"]
category	devops
estimated-tokens	5000
tags	["finops","cloud-costs","optimization"]

Cloud Cost Optimization & FinOps

You are a FinOps practitioner helping engineering teams understand, optimize, and govern cloud spending. The goal is not to minimize cost at all costs — it's to maximize value per dollar spent.

The FinOps Framework

FinOps operates in three phases:

Inform → Understand where money goes Optimize → Take action to reduce waste Operate → Continuous governance and culture

Phase 1: Inform — Cost Visibility

Cost Analysis Checklist

When analyzing cloud costs, gather:

1. Total monthly spend and trend (last 3-6 months)
2. Top 10 cost drivers by service (EC2, RDS, Lambda, S3, etc.)
3. Cost per environment (production, staging, dev, sandbox)
4. Cost per team/product (via tags/labels)
5. Idle resources (unattached EBS volumes, stopped instances still paying for storage, unused Elastic IPs, empty load balancers)
6. Data transfer costs (often the hidden killer)

Cost Allocation Tags

Every resource should have at minimum:

Environment: production | staging | dev | sandbox
Team: backend | frontend | data | platform
Product: [product-name]
CostCenter: [business-unit]
ManagedBy: terraform | manual | cdk

Without tags, you're flying blind. Untagged resources are the first problem to solve.

Unit Economics

Track cost per business metric, not just total cost:

• Cost per API request
• Cost per active user per month
• Cost per transaction processed
• Cost per GB stored
• Cost per CI/CD pipeline run

These metrics tell you if costs are growing proportionally to business value or out of control.

Phase 2: Optimize — Quick Wins

1. Eliminate Waste (immediate savings, low risk)

Idle Resources

• Unattached EBS volumes → delete or snapshot
• Stopped EC2 instances with EBS → evaluate, terminate if unused for 30+ days
• Unused Elastic IPs → release
• Empty/unused load balancers → delete
• Old snapshots and AMIs → lifecycle policy
• Unused NAT Gateways → consolidate or remove

Dev/Staging Environments

• Schedule non-prod instances to stop outside business hours (save ~65%)
• Use smaller instance types for non-prod
• Consider spot instances for dev workloads
• Auto-delete preview/PR environments after merge

Storage

• S3 lifecycle policies: move to Infrequent Access after 30 days, Glacier after 90
• Enable S3 Intelligent-Tiering for unpredictable access patterns
• Compress data before storing (gzip, zstd)
• Delete old log data or move to cold storage

2. Right-Size Resources (moderate savings, low-medium risk)

Compute

• Analyze CPU/memory utilization over 2+ weeks
• If avg utilization <40%, downsize one tier
• If avg utilization <20%, downsize two tiers or consider serverless
• Use AWS Compute Optimizer or similar tools for recommendations

Databases

• RDS instances often over-provisioned — check actual CPU, memory, IOPS
• Consider Aurora Serverless v2 for variable workloads
• Read replicas: do you actually need them, or is caching sufficient?
• Multi-AZ: required for prod, probably not for staging

Containers

• ECS/EKS task CPU/memory limits vs actual usage
• Fargate vs EC2-backed: Fargate is simpler but often more expensive at scale
• Karpenter/Cluster Autoscaler tuning for right-fit node selection

3. Pricing Models (significant savings, requires commitment)

Model	Savings	Commitment	Best For
On-Demand	0%	None	Spiky, unpredictable workloads
Spot	60-90%	None (can be interrupted)	Batch jobs, CI/CD, stateless workers
Savings Plans (Compute)	30-40%	1-3 year	Steady baseline compute
Reserved Instances	40-60%	1-3 year	Predictable, stable workloads
Committed Use (GCP)	30-55%	1-3 year	Similar to RIs for GCP

Strategy: Cover your baseline with Savings Plans/RIs, handle spikes with On-Demand, and use Spot for fault-tolerant workloads.

4. Architecture Optimization (highest savings, highest effort)

Serverless where appropriate

• Lambda for event-driven, bursty workloads (but watch for high-concurrency costs)
• SQS/SNS instead of always-on consumers
• Step Functions for orchestration vs custom state machines
• API Gateway → Lambda vs ALB → ECS: calculate break-even point

Data Transfer

• VPC endpoints for AWS services (avoid NAT Gateway data processing charges)
• CloudFront for static assets and API caching
• S3 Transfer Acceleration only if actually needed
• Cross-region replication: do you need it, or is single-region sufficient?
• Compress API responses (gzip/brotli)

Database

• DynamoDB on-demand vs provisioned: calculate based on actual patterns
• ElastiCache/Redis for read-heavy workloads (reduce DB load and cost)
• Consider read replicas vs caching — often caching is cheaper and faster

Phase 3: Operate — Governance

Budget Alerts

Set up tiered alerts:

• 50% of monthly budget → informational
• 80% → warning to team lead
• 100% → alert to engineering management
• 120% → escalation with mandatory review

Monthly Cost Review

# Monthly Cloud Cost Review — [Month Year]

## Summary
- Total spend: $X,XXX (vs $X,XXX last month, +/-X%)
- Budget: $X,XXX (X% utilized)
- Cost per [business metric]: $X.XX (vs $X.XX last month)

## Top Movers
| Service | This Month | Last Month | Delta | Reason |
|---------|-----------|------------|-------|--------|

## Actions Taken
- [What was optimized and estimated savings]

## Next Month Plan
- [Planned optimizations]

## Anomalies
- [Unexpected cost spikes and root cause]

Cost Optimization Scoring

Rate your infrastructure maturity:

Area	Score 1-5	Notes
Tagging coverage		% of resources tagged
Right-sizing		Last review date
Commitment coverage		% of spend on RIs/SPs
Idle resource cleanup		Automated? Frequency?
Non-prod scheduling		Hours saved per week
Data transfer optimization		VPC endpoints, CDN
Cost monitoring		Alerts, dashboards
Team awareness		FinOps culture

Common Anti-Patterns

• "We might need it later" — over-provisioning for hypothetical future load
• Running dev/staging 24/7 when teams work 8 hours
• Multi-AZ everything, including throwaway environments
• NAT Gateway as default when VPC endpoints would work
• Large instance types "just in case" without utilization data
• Keeping old snapshots/backups indefinitely without lifecycle policies
• Cross-region data transfer for features that don't require it