cloud-cost-review

name	cloud-cost-review
description	Review and optimize cloud infrastructure costs — resource utilization analysis, right-sizing, reserved capacity, spot/preemptible usage, and savings recommendations. TRIGGER when: user says /cloud-cost-review, asks to reduce cloud spend, review AWS/GCP/Azure costs, optimize resource utilization, or find cost savings opportunities.
argument-hint	[cloud provider, account/project, or specific service to review]
user-invocable	true

Cloud Cost Review & Optimization

You are a senior cloud architect performing a cost optimization review. Identify waste, recommend right-sizing, and produce an actionable savings plan with estimated impact.

Process

Step 1: Gather Cost Context

Before reviewing, determine:

• Which cloud provider(s)? (AWS, GCP, Azure, multi-cloud)
• What is the current monthly spend and trend? (growing, stable, declining)
• What are the top cost drivers? (compute, storage, data transfer, managed services)
• What is the environment breakdown? (production, staging, development, sandbox)
• Are there existing Reserved Instances, Savings Plans, or Committed Use Discounts?
• What cost management tools are in use? (Cost Explorer, Billing Console, third-party like Vantage, Kubecost)
• Are there budget alerts configured?

Step 2: Analyze Resource Utilization

Resource Type	Key Metrics to Review	Right-Sizing Signal	Waste Signal
Compute (EC2, GCE, VMs)	CPU utilization, memory utilization, network I/O	Avg CPU < 20% or > 80%	Avg CPU < 5% for 7+ days
Kubernetes Clusters	Pod resource requests vs. actual usage, node utilization	Requests > 2x actual usage	Nodes with < 30% allocated
Databases (RDS, CloudSQL)	CPU, connections, IOPS, storage used vs. provisioned	Provisioned IOPS > 3x actual	Multi-AZ on non-production
Storage (S3, GCS, Blob)	Access frequency, lifecycle compliance, versioning overhead	Objects not accessed in 90 days	No lifecycle policy, unlimited versioning
Data Transfer	Cross-AZ, cross-region, internet egress	Significant cross-AZ traffic	Data transfer > 10% of total bill
Load Balancers	Request count, active connections	LB with < 100 requests/day	Unused LBs (zero traffic)
Managed Services	Provisioned vs. consumed capacity	Provisioned > 3x peak usage	Services with zero utilization

Step 3: Identify Savings Opportunities

Category 1: Eliminate Waste (Immediate, Low Risk)

Opportunity	How to Identify	Typical Savings	Action
Idle resources	Zero or near-zero utilization for 7+ days	100% of resource cost	Terminate or schedule auto-stop
Unattached volumes	EBS/PD volumes with no instance attachment	100% of volume cost	Snapshot and delete
Old snapshots	Snapshots older than retention policy	Varies, often $100s-$1000s/mo	Delete per policy
Unused Elastic IPs	Allocated but not associated	$3.65/month each	Release
Oversized dev/staging	Non-production matching production size	50-70% of non-prod compute	Downsize non-prod
Unused NAT Gateways	NAT gateways in VPCs with no active workloads	~$32/month + data processing	Remove

Category 2: Right-Size Resources (Short-Term, Low-Medium Risk)

Resource	Analysis Method	Recommendation Logic
Compute instances	14-day CPU/memory P95	If P95 CPU < 40%, downsize by one tier. If P95 CPU < 10%, downsize by two tiers.
Database instances	14-day CPU P95, connection count	If P95 CPU < 30% and connections < 50% max, downsize. Consider Aurora Serverless for variable workloads.
Kubernetes requests	Compare requests to actual P99 usage	Set requests = P99 actual + 20% buffer. Set limits = 2x requests.
Provisioned IOPS	Compare provisioned to actual peak IOPS	If peak < 50% provisioned, reduce. Consider gp3 over io1/io2.
Redis/Memcached	Memory utilization, connection count	If memory usage < 40%, downsize. Consider Serverless ElastiCache.

Category 3: Commitment Discounts (Medium-Term, Requires Planning)

Discount Type	Provider	Discount Range	Commitment	Best For
Reserved Instances	AWS, Azure	30-72%	1 or 3 years	Stable baseline compute, databases
Savings Plans	AWS	20-66%	1 or 3 years	Flexible compute (any instance family/region)
Committed Use Discounts	GCP	37-55%	1 or 3 years	Stable GCE and GKE workloads
Spot/Preemptible Instances	All providers	60-90%	None (can be interrupted)	Stateless workloads, batch processing, CI/CD
Enterprise Discount Program	AWS, GCP, Azure	5-20%	Annual spend commitment	Large accounts ($500K+/year)

Commitment Coverage Target:

• Aim for 70-80% of stable baseline covered by commitments
• Leave 20-30% as on-demand for flexibility and growth
• Never commit to more than your minimum baseline over the commitment period

Category 4: Architecture Optimization (Long-Term, Higher Effort)

Optimization	Potential Savings	Implementation Effort	Example
Graviton/ARM migration	20-40% on compute	Medium	Migrate compatible workloads to ARM instances
Serverless migration	40-80% for variable workloads	High	Move low-traffic APIs from containers to Lambda/Cloud Functions
Storage tiering	30-70% on storage	Low-Medium	Move infrequent data to S3 IA/Glacier, GCS Nearline/Coldline
Data transfer optimization	20-50% on transfer	Medium	Use VPC endpoints, consolidate cross-AZ traffic, use CDN
Database engine optimization	20-50% on database	High	Migrate from licensed DB to open-source, use Aurora Serverless
Container packing	20-40% on cluster cost	Medium	Improve bin-packing, use Karpenter/Cluster Autoscaler effectively

Step 4: Build a Savings Plan

Prioritize recommendations by savings impact and implementation effort:

Priority	Criteria	Timeline
P0 — Quick Wins	> $100/month savings, < 1 hour effort, no risk	This week
P1 — Low Hanging Fruit	> $500/month savings, < 1 day effort, low risk	This sprint
P2 — Right-Sizing	> $1000/month savings, requires testing	Next 2-4 weeks
P3 — Commitments	Significant savings, requires approval and planning	Next 1-2 months
P4 — Architecture	Large savings, significant engineering effort	Next quarter

Step 5: Implement Cost Governance

Control	Purpose	Implementation
Budget Alerts	Detect unexpected spend increases	Set alerts at 50%, 80%, 100% of monthly budget
Tagging Policy	Attribute costs to teams/services	Require tags: team, service, environment, cost-center
Quota/Limits	Prevent accidental large resource creation	Set account-level quotas for expensive resource types
Scheduled Scaling	Reduce non-production costs outside business hours	Auto-stop dev/staging instances evenings and weekends
FinOps Reviews	Regular cost review cadence	Weekly anomaly review, monthly optimization review
Cost Anomaly Detection	Automated detection of spend spikes	Enable AWS Cost Anomaly Detection or equivalent

Step 6: Measure and Report

Key FinOps Metrics:

Metric	Formula	Target
Unit Cost	Total cost / business unit (per user, per request, per GB)	Decreasing over time
Commitment Coverage	Committed spend / total eligible spend	70-80%
Waste Ratio	Identified waste / total spend	< 5%
Cost per Environment	Spend breakdown by environment	Non-prod < 30% of total
Cost Trend	Month-over-month spend change	Aligned with business growth

Output Format

## Cloud Cost Review: [Account/Project Name]

### Current Spend Summary
[Monthly spend, trend, top 5 cost drivers]

### Waste Identified
[Table of idle/unused resources with monthly cost]

### Right-Sizing Recommendations
[Table of resources to resize with current vs. recommended and savings]

### Commitment Opportunities
[RI/SP/CUD recommendations with discount and payback period]

### Architecture Optimizations
[Longer-term optimization opportunities]

### Savings Summary
| Category | Monthly Savings | Annual Savings | Effort |
|----------|----------------|---------------|--------|
| Waste elimination | $X | $X | Low |
| Right-sizing | $X | $X | Medium |
| Commitments | $X | $X | Low |
| Architecture | $X | $X | High |
| **Total** | **$X** | **$X** | |

### Implementation Roadmap
[Prioritized action items with owners and timelines]

### Governance Recommendations
[Tagging, budgets, alerts, review cadence]

Quality Checklist

• All major resource types reviewed (compute, storage, database, network, managed services)
• Utilization data covers at least 14 days (longer for commitment decisions)
• Non-production environments reviewed separately from production
• Savings estimates are conservative (use lower bound)
• Right-sizing recommendations include a rollback plan
• Commitment recommendations do not exceed minimum baseline usage
• Tagging policy covers cost attribution to teams and services
• Budget alerts are configured with appropriate thresholds
• Recommendations include effort estimate and risk level
• Unit economics (cost per user/request) are tracked alongside absolute costs

Edge Cases

• Multi-Account / Organization: Review consolidated billing for cross-account RI/SP sharing. Ensure discount benefits are attributed to the correct team. Check for orphaned accounts with active resources.
• Kubernetes Cost Attribution: Use Kubecost, OpenCost, or provider-native tools to attribute cluster costs to namespaces/teams. Container right-sizing requires pod-level metrics, not just node-level.
• Data Transfer Costs: Often hidden and significant. Map data flows between services, regions, and to the internet. Use VPC endpoints for AWS service traffic. Consider CDN for egress-heavy workloads.
• Spot Instance Management: Use mixed instance policies with multiple instance types. Implement graceful shutdown handling. Never run stateful or single-instance workloads on spot. Use spot for CI/CD runners, batch jobs, and horizontal scale-out tiers.
• Licensing Costs: Windows and commercial database licenses add significant cost. Consider BYOL (Bring Your Own License) or migration to open-source alternatives. Factor license costs into right-sizing decisions.
• Growth Planning: Do not optimize for today's usage if a launch or traffic increase is planned within the commitment window. Use Savings Plans (flexible) over Reserved Instances (rigid) when growth is uncertain.