Azure Cost Management: Deep Dive into Amortized Cost Analysis

Understanding Azure cost data is crucial for effective cloud financial management. The Azure Cost Management export dataset provides detailed insights into your spending patterns, but navigating the amortized cost data requires understanding key concepts like Metered Categories, pricing models, and cost calculations. This comprehensive guide will help you master Azure cost analysis.

Understanding Azure Cost Management Export Dataset

Prerequisites: Before diving into cost analysis, you’ll need to export your Azure cost data. If you haven’t set up cost exports yet, follow our comprehensive guide: How to Export Azure Cost Management Data: Complete Step-by-Step Guide

What is Amortized Cost?

Amortized cost spreads the upfront cost of reservations across the reservation term, providing a true cost-per-hour view that includes both reserved and on-demand usage patterns.

Understanding the Dataset Structure

Important Note on Data Granularity:

Each row in the Azure Cost Management export represents daily usage totals, not individual hours To analyze a specific time period (monthly, quarterly, etc.), you must:

• Filter the export date range to your desired analysis period
• Sum all daily quantity values to get total usage hours/units for the period
• Ensure your export covers the complete time period you want to analyze

Key Fields in the Dataset

Time Period Analysis Setup: When setting up your cost export, ensure you:

• Set the date range to cover your complete analysis period (e.g., full month, quarter)
• Export data at daily granularity for accurate calculations
• Include all necessary date filters in your analysis calculations

Filtering by Metered Category

Common Metered Categories for Analysis

Virtual Machines

• MeterCategory = “Virtual Machines”
• Includes compute costs for VMs
• Excludes storage and networking

SQL Database

• MeterCategory = “SQL Database”
• Includes database compute and storage
• Different pricing models available

Storage

• MeterCategory = “Storage”
• Blob, file, and disk storage costs
• Usually on-demand pricing

Filtering Example Query (KQL/PowerBI)

// Filter for Virtual Machines only
CostManagementData
| where MeterCategory == "Virtual Machines"
| where PricingModel in ("OnDemand", "Reservation")
| summarize
    TotalQuantity = sum(Quantity),
    TotalAmortizedCost = sum(CostInBillingCurrency),
    TotalOnDemandEquivalent = sum(Quantity * UnitPrice)
by PricingModel, ResourceName

Pricing Model Analysis

OnDemand vs Reserved Instances

OnDemand Pricing

• Pay-as-you-go model
• Higher per-hour cost
• Maximum flexibility
• PricingModel = “OnDemand”

Reserved Instance Pricing

• 1 or 3-year commitment
• Significant discounts (typically up to 62% based on Microsoft pricing calculator)
• PricingModel = “Reservation”
• Amortized hourly cost

Financial Calculation Steps

Step 1: Aggregate Daily Data for Analysis Period

Total Usage Hours Calculation:

Total Hours for Period = SUM(Daily Quantity for all days in period)

Example - Monthly Analysis:

• Day 1: VM runs 24 hours → Quantity = 24
• Day 2: VM runs 24 hours → Quantity = 24
• …
• Day 31: VM runs 24 hours → Quantity = 24
• Total Monthly Hours = 24 × 31 days = 744 hours

Step 2: Calculate Total Amortized Cost

For the Analysis Period:

Total Amortized Cost = SUM(Daily Quantity × Daily EffectivePrice) for all days

Example - Reserved Instance VM:

• Daily cost = 24 hours × $0.096/hour = $2.304
• Monthly cost = $2.304 × 31 days = $71.42

Step 3: Calculate On-Demand Equivalent Cost

What you would have paid without reservations:

On-Demand Equivalent = SUM(Daily Quantity × Daily UnitPrice) for all days

Example - Same VM at On-Demand Rates:

• Daily cost = 24 hours × $0.192/hour = $4.608
• Monthly cost = $4.608 × 31 days = $142.85

Step 4: Calculate Savings from Reservations

Financial Impact Calculation:

• Total Savings = On-Demand Equivalent Cost - Total Amortized Cost
• Savings Percentage = (Total Savings ÷ On-Demand Equivalent Cost) × 100

Example:

• Total Savings = $142.85 - $71.42 = $71.43
• Savings Percentage = ($71.43 ÷ $142.85) × 100 = 50%

Reserved vs On-Demand Financial Analysis

Step 5: Calculate Reservation Coverage Metrics

A. Coverage by Usage Hours

• Reserved Hours = SUM(Daily Quantity where PricingModel = ‘Reservation’)

• On-Demand Hours = SUM(Daily Quantity where PricingModel = ‘OnDemand’)

• Total Hours = Reserved Hours + On-Demand Hours

• Reservation Coverage % = (Reserved Hours ÷ Total Hours) × 100

• On-Demand Usage % = (On-Demand Hours ÷ Total Hours) × 100

B. Coverage by Resource Count

• Reserved Resources = COUNT(DISTINCT Resources where PricingModel = ‘Reservation’)

• Total Resources = COUNT(DISTINCT All Resources)

• Resource Coverage % = (Reserved Resources ÷ Total Resources) × 100

Step 6: Financial Impact Assessment

Monthly Cost Breakdown Calculation:

For each MeterCategory (Virtual Machines, SQL Database, etc.):

1. Reserved Instance Costs:

   • Sum all daily costs where PricingModel = ‘Reservation’

2. On-Demand Costs:

   • Sum all daily costs where PricingModel = ‘OnDemand’

3. Total Actual Spend:

   • Reserved Instance Costs + On-Demand Costs

4. Hypothetical All On-Demand Cost:

   • Sum all (Daily Quantity × Daily UnitPrice) for all resources

5. Total Savings Achieved:

   • Hypothetical All On-Demand Cost - Total Actual Spend

Step 7: Cost Optimization Analysis

Financial Efficiency Metrics:

1. Average Cost per Hour:

   • Reserved Instance Average = Total Reserved Costs ÷ Total Reserved Hours
   • On-Demand Average = Total On-Demand Costs ÷ Total On-Demand Hours
   • Blended Average = Total Actual Spend ÷ Total Hours

2. Reservation Utilization Rate:

   • For each reserved instance type:
   • Utilization % = (Actual Reserved Hours Used ÷ Reserved Hours Purchased) × 100

3. Cost Avoidance Calculation:

   • Monthly Cost Avoidance = Monthly Savings from Reservations
   • Annual Cost Avoidance = Monthly Cost Avoidance × 12

4. Return on Investment (ROI) for Reservations:

   • ROI % = (Annual Savings ÷ Upfront Reservation Investment) × 100

Practical Financial Analysis Example

Monthly Cost Analysis Scenario:

Data Setup:

• Analysis Period: January 2025 (31 days)
• VM Type: Standard_D2s_v3
• On-Demand Rate: $0.192/hour
• Reserved Instance Rate: $0.096/hour (50% savings)

Step-by-Step Calculation:

1. Data Aggregation:

   • Reserved VM: 15 VMs × 24 hours/day × 31 days = 11,160 hours
   • On-Demand VM: 5 VMs × 24 hours/day × 31 days = 3,720 hours
   • Total Hours: 14,880 hours

2. Actual Costs:

   • Reserved Cost: 11,160 hours × $0.096 = $1,071.36
   • On-Demand Cost: 3,720 hours × $0.192 = $714.24
   • Total Actual Cost: $1,785.60

3. Hypothetical All On-Demand Cost:

   • All On-Demand: 14,880 hours × $0.192 = $2,857.20

4. Financial Impact:

   • Total Savings: $2,857.20 - $1,785.60 = $1,071.60
   • Savings Rate: ($1,071.60 ÷ $2,857.20) × 100 = 37.5%
   • Reservation Coverage: (11,160 ÷ 14,880) × 100 = 75%

5. Optimization Opportunity:

   • If remaining 5 VMs were also reserved:
   • Additional Monthly Savings: 3,720 × ($0.192 - $0.096) = $357.12
   • Potential Total Monthly Savings: $1,071.60 + $357.12 = $1,428.72
   • Potential Savings Rate: 50%

Financial Summary Matrix

Key Performance Indicators (KPIs) for Finance Teams

1. Cost Management KPIs

Reservation Coverage Rate

• Target: 70-80% for predictable workloads
• Calculation: Reserved Hours ÷ Total Hours × 100

Average Savings Rate

• Target: 40-50% based on Microsoft pricing
• Calculation: (OnDemand Equivalent - Actual Cost) ÷ OnDemand Equivalent × 100

Cost per Resource Hour

• Benchmark: Compare against previous periods
• Calculation: Total Cost ÷ Total Resource Hours

2. Financial Planning Metrics

Monthly Cost Trend

• Track month-over-month cost changes
• Identify seasonal patterns
• Plan budget allocations

Reservation ROI

• Payback Period = Upfront Investment ÷ Monthly Savings
• Annual ROI = (Annual Savings ÷ Upfront Investment) × 100

Cost Predictability Index

• Reserved Cost Ratio = Reserved Costs ÷ Total Costs × 100
• Higher ratio = More predictable monthly spending

Conclusion

Understanding Azure cost data requires careful attention to data granularity and proper aggregation of daily usage records. Key financial takeaways:

• Data Preparation: Always aggregate daily quantities for the complete analysis period
• Savings Calculation: Use actual Microsoft pricing (≈62% maximum discount) for realistic projections
• Financial Impact: Focus on total cost avoidance and ROI metrics for business cases
• Optimization: Target 70-80% reservation coverage for predictable workloads
• Monitoring: Track monthly trends and utilization rates for continuous optimization

The financial discipline of proper cost analysis enables data-driven decisions for cloud optimization and budget planning.