Service Cost Analysis

Overview

Service cost analysis is a systematic process for estimating infrastructure and operational costs for software projects. Research current pricing, model costs across usage scenarios, calculate ROI projections, and identify optimization opportunities. Use this skill to make informed budget decisions backed by real pricing data.

When to Use This Skill

This skill should be used when:

• Estimating infrastructure costs for new projects
• Comparing cloud provider pricing (AWS, GCP, Azure)
• Analyzing third-party service costs (SaaS, APIs)
• Creating cost projections across usage scenarios
• Calculating ROI and payback periods
• Identifying cost optimization opportunities

Visual Enhancement with Project Diagrams

When documenting cost analysis, include visualizations.

Use the project-diagrams skill to generate:

• Cost breakdown charts
• Scenario comparison charts
• ROI projection graphs
• Cost optimization decision trees

python .claude/skills/project-diagrams/scripts/generate_schematic.py "diagram description" -o diagrams/output.png

Cost Analysis Framework

Cost Categories

Research Protocol

For every service, follow this protocol:

1. Find Official Pricing

   • Use WebFetch or WebSearch to get current pricing
   • Look for pricing calculators
   • Note pricing model (per-hour, per-request, per-GB, etc.)

2. Identify Cost Drivers

   • What metrics drive cost?
   • Are there minimum commitments?
   • What are the tiered pricing breakpoints?

3. Document Assumptions

   • Usage estimates (requests, storage, users)
   • Growth projections
   • Geographic distribution

4. Calculate Scenarios

   • Low (conservative usage)
   • Mid (expected usage)
   • High (aggressive growth)

Service Cost Specification Schema

service_cost:
  # Identity
  service_name: "string"
  provider: "aws | gcp | azure | other"
  category: "compute | storage | database | networking | third_party | development"

  # Pricing
  pricing_model: "per_hour | per_request | per_gb | per_user | flat_rate | tiered"
  pricing_source: "URL to official pricing"
  pricing_date: "YYYY-MM-DD"

  # Cost Estimates
  monthly_cost:
    low: number
    mid: number
    high: number

  # Assumptions
  assumptions:
    - "string - assumption 1"
    - "string - assumption 2"

  # Usage Estimates
  usage:
    metric: "string (e.g., requests, GB, hours)"
    low: number
    mid: number
    high: number

  # Notes
  notes: "string - important considerations"
  optimization_opportunities:
    - "string - potential optimization"

Cloud Provider Cost Analysis

AWS Cost Analysis

Common Services:

AWS Pricing Research:

# Use WebSearch to find current pricing
WebSearch: "AWS [service] pricing 2025"
WebFetch: "https://aws.amazon.com/[service]/pricing/"

Example AWS Cost Specification:

service_costs:
  - service_name: "AWS RDS PostgreSQL"
    provider: "aws"
    category: "database"
    pricing_model: "per_hour"
    pricing_source: "https://aws.amazon.com/rds/postgresql/pricing/"
    pricing_date: "2025-01-06"

    monthly_cost:
      low: 50      # db.t3.micro, 20GB, single-AZ
      mid: 250     # db.t3.medium, 100GB, multi-AZ
      high: 800    # db.r5.large, 500GB, multi-AZ, provisioned IOPS

    assumptions:
      - "US East (N. Virginia) region"
      - "Reserved instances not applied (on-demand pricing)"
      - "Low: Development environment"
      - "Mid: Production with moderate traffic"
      - "High: Production with high availability requirements"

    usage:
      metric: "instance hours + storage GB"
      low: "730 hours db.t3.micro + 20GB"
      mid: "730 hours db.t3.medium + 100GB + multi-AZ"
      high: "730 hours db.r5.large + 500GB + multi-AZ + 3000 PIOPS"

    notes: |
      Consider Aurora for better scalability above 500GB.
      Reserved instances can reduce costs by 30-60%.
      Multi-AZ doubles compute cost but is recommended for production.

    optimization_opportunities:
      - "Reserved instances for predictable workloads"
      - "Aurora Serverless for variable workloads"
      - "Read replicas instead of larger instance"

GCP Cost Analysis

Common Services:

GCP Pricing Research:

WebSearch: "GCP [service] pricing 2025"
WebFetch: "https://cloud.google.com/[service]/pricing"

Azure Cost Analysis

Common Services:

Third-Party Service Costs

Common Categories:

Example Third-Party Cost:

service_costs:
  - service_name: "Stripe Payment Processing"
    provider: "stripe"
    category: "third_party"
    pricing_model: "per_transaction"
    pricing_source: "https://stripe.com/pricing"
    pricing_date: "2025-01-06"

    monthly_cost:
      low: 150      # $5,000 GMV
      mid: 750      # $25,000 GMV
      high: 3000    # $100,000 GMV

    assumptions:
      - "Standard pricing: 2.9% + $0.30 per transaction"
      - "Average transaction: $50"
      - "Low: 100 transactions/month"
      - "Mid: 500 transactions/month"
      - "High: 2000 transactions/month"

    notes: |
      Volume discounts available above $100K/month.
      Additional fees for international cards (+1.5%).
      Subscription billing may have different rates.

Cost Estimation Templates

Monthly Cost Summary

monthly_cost_summary:
  project: "[Project Name]"
  date: "YYYY-MM-DD"
  currency: "USD"

  scenarios:
    low:
      description: "MVP / Development"
      users: "< 100"
      total: 0
    mid:
      description: "Initial Production"
      users: "1,000 - 10,000"
      total: 0
    high:
      description: "Scale / Growth"
      users: "> 10,000"
      total: 0

  by_category:
    compute:
      services: []
      low: 0
      mid: 0
      high: 0
    storage:
      services: []
      low: 0
      mid: 0
      high: 0
    database:
      services: []
      low: 0
      mid: 0
      high: 0
    networking:
      services: []
      low: 0
      mid: 0
      high: 0
    third_party:
      services: []
      low: 0
      mid: 0
      high: 0
    development:
      services: []
      low: 0
      mid: 0
      high: 0

  totals:
    monthly:
      low: 0
      mid: 0
      high: 0
    annual:
      low: 0
      mid: 0
      high: 0

ROI Analysis Template

roi_analysis:
  project: "[Project Name]"

  investment:
    development_cost: 0
    infrastructure_setup: 0
    training_and_onboarding: 0
    total_initial_investment: 0

  ongoing_costs:
    monthly_infrastructure: 0
    monthly_third_party: 0
    monthly_personnel: 0
    total_monthly_operating: 0
    annual_operating: 0

  revenue_projections:
    year_1:
      monthly_revenue: 0
      growth_rate: "% per month"
      annual_revenue: 0
    year_2:
      annual_revenue: 0
    year_3:
      annual_revenue: 0

  metrics:
    payback_period_months: 0
    year_1_roi: "percentage"
    year_3_roi: "percentage"
    break_even_users: 0

  assumptions:
    - "Revenue assumption 1"
    - "Cost assumption 2"
    - "Growth assumption 3"

Cost Optimization Strategies

Compute Optimization

Storage Optimization

Database Optimization

Third-Party Optimization

Cost Analysis Report Structure

# Cost Analysis Report: [Project Name]

## Executive Summary
- Total estimated monthly cost: $X - $Y
- Primary cost drivers: [List top 3]
- Key optimization opportunities: [List top 3]

## Cost Breakdown by Category

### Compute ($X/month)
[Service details and cost justification]

### Storage ($X/month)
[Service details and cost justification]

### Database ($X/month)
[Service details and cost justification]

### Third-Party Services ($X/month)
[Service details and cost justification]

## Scenario Analysis

### Low Scenario (MVP)
- Users: X
- Monthly cost: $Y
- Suitable for: [Use case]

### Mid Scenario (Production)
- Users: X
- Monthly cost: $Y
- Suitable for: [Use case]

### High Scenario (Scale)
- Users: X
- Monthly cost: $Y
- Suitable for: [Use case]

## ROI Analysis
[If applicable]

## Optimization Recommendations
1. [Recommendation with potential savings]
2. [Recommendation with potential savings]
3. [Recommendation with potential savings]

## Assumptions and Risks
- [Key assumption 1]
- [Key assumption 2]
- [Cost risk 1]

## References
- [Pricing source 1]
- [Pricing source 2]

Quality Checklist

Before completing cost analysis:

• All major services identified
• Current pricing verified from official sources
• Pricing dates documented
• Usage assumptions documented
• Three scenarios calculated (low/mid/high)
• Cost optimization opportunities identified
• ROI calculated (if applicable)
• Total monthly and annual costs calculated
• Primary cost drivers identified

Best Practices

Do's

• Always cite official pricing sources
• Include pricing date (pricing changes frequently)
• Document all assumptions explicitly
• Consider all cost categories (don't forget networking)
• Calculate both monthly and annual costs
• Identify optimization opportunities proactively

Don'ts

• Don't use outdated pricing data
• Don't forget data transfer costs (often overlooked)
• Don't ignore startup vs. steady-state costs
• Don't assume linear scaling of costs
• Don't forget development and operational tooling
• Don't skip personnel costs in TCO analysis