cost-optimization-review

Cost Optimization Review

Step 1: Gather context

Ask the user:

What workload or AWS environment would you like me to review for cost optimization? Please share:

• Workload name and code packages/directories to analyze
• Traffic patterns (steady, spiky, predictable growth, seasonal)
• Monthly spend estimate (optional — helps calibrate severity)
• Budget constraints or targets (optional)

If context is already provided or you are in a codebase with IaC, proceed directly.

Step 2: Compute Cost Discovery

Analyze all compute resource configurations.

You MUST examine:

• EC2 instance types and sizes in ASG/launch templates
• ECS task definitions (CPU/memory allocations)
• Lambda memory and timeout configurations
• Fargate task sizes
• EKS node group configurations
• Batch compute environments

For each compute resource, document:

• File path and line numbers
• Instance type/size configured
• Scaling configuration (min/max/desired)
• Whether Graviton is used (or x86 where Graviton is available)
• Pricing model implications (on-demand only vs Spot-eligible vs Reserved-eligible)

You MUST flag as HIGH RISK:

• Fixed-size compute (no auto-scaling) for variable workloads
• Over-provisioned Lambda (>1024MB for simple operations)
• x86 instance types where Graviton equivalents exist (cost + sustainability)
• Large instance types for workloads that could use smaller instances with horizontal scaling
• Dev/test environments with production-sized resources
• No scheduled scaling for environments with clear off-hours

Step 3: Storage and Data Cost Discovery

Analyze storage configurations.

You MUST examine:

• S3 bucket configurations (storage class, lifecycle policies, versioning)
• EBS volume types and sizes
• RDS storage configurations (type, allocated size, auto-scaling)
• DynamoDB capacity mode and provisioning
• ElastiCache node types and cluster sizes
• EFS configurations (throughput mode, lifecycle)
• Backup retention policies (AWS Backup, RDS snapshots)
• Log retention settings (CloudWatch log groups)

You MUST flag as HIGH RISK:

• S3 buckets without lifecycle policies (accumulating indefinitely)
• CloudWatch log groups with "never expire" retention
• DynamoDB with provisioned capacity that could use on-demand (or vice versa based on pattern)
• Over-provisioned IOPS on EBS/RDS
• EBS volumes not using gp3 (gp2 is more expensive for same performance)
• Backup retention > 35 days without business justification
• S3 versioning enabled without lifecycle rules to expire old versions

Step 4: Data Transfer Cost Discovery

Analyze network and data transfer patterns.

You MUST examine:

• NAT Gateway usage (could VPC endpoints replace?)
• Cross-region data transfer patterns
• VPC endpoint configurations (or lack thereof for S3/DynamoDB)
• CloudFront distributions (or lack thereof for static content)
• Cross-AZ traffic patterns (multi-AZ services communicating across AZs)
• API Gateway configurations (REST vs HTTP API pricing)

You MUST flag as HIGH RISK:

• S3/DynamoDB access going through NAT Gateway (VPC endpoint would be free)
• No CloudFront for static content delivery
• REST API Gateway where HTTP API would suffice (70% cheaper)
• Cross-region replication without business justification
• Public S3 access through internet where CloudFront would reduce transfer costs

Step 5: Pricing Model Assessment

Analyze whether pricing models align with usage patterns.

You MUST evaluate:

• Steady-state compute → Savings Plans or Reserved Instances opportunity
• Variable/batch compute → Spot Instance opportunity
• Serverless vs provisioned alignment (Lambda/Fargate for spiky, EC2/ECS for steady)
• DynamoDB on-demand vs provisioned (on-demand for unpredictable, provisioned for steady)
• Aurora Serverless v2 vs provisioned (for variable database load)
• S3 Intelligent-Tiering for unknown access patterns

Step 6: Environment and Lifecycle Management

Analyze non-production environment configurations.

You MUST examine:

• Dev/test/staging environment sizing vs production
• Scheduled scaling or shutdown for non-production
• Resource lifecycle policies (TTL on test resources)
• Cost allocation tags on resources
• Budget and anomaly detection configurations

You MUST flag as IMPROVEMENT OPPORTUNITY:

• Non-production environments running 24/7 at production scale
• No cost allocation tags on resources
• No AWS Budget or Cost Anomaly Detection configured
• No lifecycle policies on test/temporary resources

---STOP--- Checkpoint: Discovery complete — ready to evaluate against WA Framework

Discovered compute resources ({X} instances/functions), storage configurations ({Y} buckets/volumes/tables), data transfer patterns, pricing model alignment, and environment lifecycle settings across {packages analyzed}.

Shall I proceed with evaluating these findings against the WA Cost Optimization pillar questions and quantifying savings estimates?

Do NOT proceed past this point until the user explicitly confirms.

Step 7: Evaluate against WA Framework questions

For each question, provide: Status, Evidence (file:line), Gaps, Risk.

COST 1 — How do you implement cloud financial management?

• Evidence: cost allocation tags, budget configs, anomaly detection rules

COST 2 — How do you govern usage?

• Evidence: SCPs limiting instance types, quotas, resource constraints

COST 3 — How do you monitor usage and cost?

• Evidence: Budget alarms, Cost Anomaly Detection configs, billing alarms

COST 4 — How do you decommission resources?

• Evidence: lifecycle policies, TTL configs, cleanup automation, retention rules

COST 5 — How do you evaluate cost when you select services?

• Evidence: serverless for variable loads, provisioned for steady, Spot for batch

COST 6 — How do you meet cost targets when you select resource type, size, and number?

• Evidence: instance types, scaling min/max, memory allocations, right-sizing evidence

COST 7 — How do you use pricing models to reduce cost?

• Evidence: Savings Plan configs, Reserved capacity, Spot fleet configs

COST 8 — How do you plan for data transfer charges?

• Evidence: VPC endpoints, CloudFront distributions, regional placement

Step 8: Quantify savings estimates

For each finding, estimate:

• Current cost indicator: resource configuration that drives cost (instance type, provisioned capacity, retention)
• Optimized configuration: what the resource should be
• Relative savings: percentage reduction for that resource category
• Effort: Low / Medium / High to implement
• Risk: impact on reliability or performance

Step 9: Risk Assessment

For each finding, assess using Impact × Likelihood:

Impact: Minor (< 10% savings in category) | Moderate (10-30% savings in category) | Severe (> 30% savings in category or fundamental pricing model misalignment)

Likelihood (of waste continuing): Low (planned optimization exists) | Medium (no plan but low growth) | High (growing unchecked, no visibility)

Impact	Likelihood	Risk Level
Severe	High	Critical
Severe	Medium	High
Severe	Low	High
Moderate	High	High
Moderate	Medium	Medium
Moderate	Low	Medium
Minor	High	Medium
Minor	Medium	Low
Minor	Low	Low

---STOP--- Checkpoint: Risk assessment complete — ready to produce the final report

Assessed {N} findings across compute, storage, data transfer, pricing models, and environment management. Risk distribution: {X} Critical, {Y} High, {Z} Medium, {W} Low. Top savings opportunities identified with effort estimates.

Shall I produce the full Cost Optimization report with prioritized remediation plan?

Do NOT proceed past this point until the user explicitly confirms.

Step 10: Produce the report

# Cost Optimization Review: {Workload Name}

## Executive Summary
- **Date**: {date}
- **Packages Analyzed**: {list}
- **Findings**: {X} Critical, {Y} High, {Z} Medium, {W} Low
- **Overall Cost Efficiency**: {1-5} — {one-line justification}
- **Top 3 Savings Opportunities**: {brief list}

## Cost Optimization Scorecard
| Domain | Score (1-5) | Key Strength | Key Gap |
|--------|-------------|--------------|---------|
| Compute Right-Sizing | {score} | {strength} | {gap} |
| Storage Lifecycle | {score} | {strength} | {gap} |
| Data Transfer | {score} | {strength} | {gap} |
| Pricing Models | {score} | {strength} | {gap} |
| Environment Management | {score} | {strength} | {gap} |
| Cost Visibility | {score} | {strength} | {gap} |

## Critical and High Risk Findings
{For each: ID, domain, title, description, evidence (file:line), cost impact, recommendation, effort, AWS services}

## Medium Risk Findings
{Same format, condensed}

## Low Risk Findings
{Summary table: ID | Domain | Title | Recommendation}

## Savings Summary
| Category | Finding | Current Config | Optimized Config | Relative Savings | Effort |
|----------|---------|---------------|-----------------|-----------------|--------|
| Compute | {title} | {current} | {recommended} | {%} | {effort} |
| Storage | {title} | {current} | {recommended} | {%} | {effort} |
| Transfer | {title} | {current} | {recommended} | {%} | {effort} |

## Prioritized Remediation Plan

### Quick Wins (< 1 week)
| Finding | Action | Savings Impact | Effort | Risk |
|---------|--------|---------------|--------|------|
{Switch gp2→gp3, add lifecycle policies, set log retention, add VPC endpoints}

### Foundation (1-4 weeks)
| Finding | Action | Savings Impact | Effort | Dependencies |
|---------|--------|---------------|--------|--------------|
{Right-size instances, implement auto-scaling, schedule non-prod, add Graviton}

### Strategic (1-3 months)
| Finding | Action | Savings Impact | Effort | Dependencies |
|---------|--------|---------------|--------|--------------|
{Savings Plans commitment, serverless migration, architecture optimization}

## Next Steps
{Top 5 concrete cost-saving actions the team should take this week}

Step 11: Offer follow-up

After delivering the report, offer:

Would you like me to:

• Generate IaC changes for quick-win optimizations?
• Model Savings Plans vs Reserved Instances for your usage?
• Design a scheduled scaling policy for non-production?
• Create a FinOps tagging strategy with cost allocation tags?
• Estimate cost for an architectural alternative (serverless, containers)?
• Implement S3 lifecycle policies for identified buckets?

Calibration Guidance

• A workload with auto-scaling, lifecycle policies, VPC endpoints, and cost allocation tags is MATURE — focus on advanced optimizations (Savings Plans, Spot, architectural alternatives)
• Every finding MUST have code evidence — don't flag "over-provisioned" without checking actual configurations
• Savings estimates should be relative (% reduction) when absolute spend is unknown
• Always assess trade-offs: cost optimization MUST NOT introduce reliability risks without explicit acknowledgment
• Flag when a cost optimization would degrade the reliability or performance posture
• "Cannot Determine" is valid when you can't assess actual utilization from IaC alone (recommend Compute Optimizer, Cost Explorer)
• Acknowledge good cost practices before listing waste