DigiUsher - Subscription Level Reservations

Overview

The Subscription Level Reservations recommendation identifies opportunities to purchase Azure Reserved Instances at the subscription level, helping you achieve significant cost savings by committing to reserved capacity instead of using pay-as-you-go pricing.

How It Works

DigiUsher integrates with Azure Advisor API to retrieve native Azure Reserved Instance recommendations and processes them to provide comprehensive cost optimization guidance. The system analyzes both resource-level and subscription-level recommendations to identify the best reservation opportunities.

Supported Cloud Providers

Azure - Virtual machines and other reservable resources

Selection Criteria

Recommendations are generated when:

Azure Advisor identifies Reserved Instance opportunities with recommendation type ID 84b1a508-fc21-49da-979e-96894f1665df
Recommendations have extended properties with required pricing information
For resource-level recommendations: the resource is monitored by DigiUsher
For subscription-level recommendations: multiple recommendations are aggregated by subscription
Positive cost savings are projected from switching to reserved pricing

Configuration Options

Parameter	Default	Description
`skip_cloud_accounts`	[]	Cloud accounts to skip during analysis

Reserved Instance Benefits

Cost Savings

Significant Discounts: Up to 72% savings compared to pay-as-you-go pricing
Predictable Costs: Fixed pricing for the reservation term
Budget Planning: Easier cost forecasting with reserved capacity
Volume Discounts: Better pricing for larger commitments

Flexibility Options

1-Year Terms: Shorter commitment with moderate savings
3-Year Terms: Maximum savings with longer commitment
Payment Options: All Upfront, Partial Upfront, or No Upfront
Instance Size Flexibility: Ability to use reservations across different VM sizes within the same family

Savings Calculation

The system calculates savings using Azure Advisor data:

Monthly Savings = Azure Advisor Savings Amount × Exchange Rate

The calculation considers:

Current pay-as-you-go costs
Reserved Instance pricing for different terms
Currency conversion to organization currency
Maximum savings between monthly and yearly options

Example Output

{
  "resource_name": "prod-web-vm-pool",
  "cloud_resource_id": "subscription-level-recommendation",
  "cloud_account_name": "Production Azure",
  "subscription_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "resource_type": "Virtual Machine",
  "instance_family": "D-series",
  "region": "East US",
  "recommendation_type": "subscription_level",
  "reservation_term": "1_year",
  "payment_option": "partial_upfront",
  "current_monthly_cost": 2400.00,
  "reserved_monthly_cost": 1680.00,
  "monthly_savings": 720.00,
  "savings_percentage": 30.0,
  "annual_savings": 8640.00,
  "upfront_cost": 10080.00,
  "savings_currency": "USD"
}

Reservation Types

Virtual Machine Reservations

General Purpose: B, D, F-series virtual machines
Compute Optimized: F-series virtual machines
Memory Optimized: E, M-series virtual machines
Storage Optimized: L-series virtual machines

Other Reservable Services

SQL Database: Azure SQL Database capacity
Cosmos DB: Cosmos DB throughput capacity
Storage: Premium SSD managed disks
App Service: Dedicated App Service plans

Risk Assessment

Low Risk

Stable, predictable workloads
Long-running production systems
Well-understood capacity requirements

Medium Risk

Workloads with some variability
Growing applications with predictable patterns
Mixed development and production workloads

High Risk

Highly variable workloads
Short-term projects
Applications with unknown future requirements

Best Practices

Before Purchasing Reservations

Usage Analysis: Analyze historical usage patterns over 6-12 months
Growth Planning: Consider expected growth in capacity needs
Workload Stability: Ensure workloads are stable and long-term
Budget Planning: Plan for upfront costs in budget cycles

Reservation Strategy

Diversified Approach: Mix different reservation terms and types
Size Flexibility: Use instance size flexibility when available
Geographic Distribution: Consider reservations across multiple regions
Regular Reviews: Conduct quarterly reviews of reservation utilization

Ongoing Management

Utilization Monitoring: Track reservation utilization rates
Coverage Optimization: Optimize reservation coverage
Exchange Opportunities: Use reservation exchange when beneficial
Renewal Planning: Plan for reservation renewals before expiration

Implementation Strategy

Phase 1: Analysis

Review Azure Advisor recommendations
Analyze historical usage patterns
Calculate projected savings and ROI

Phase 2: Planning

Determine optimal reservation mix
Plan budget allocation for upfront costs
Create implementation timeline

Phase 3: Purchase

Purchase reservations based on analysis
Monitor initial utilization
Adjust strategy based on early results

Phase 4: Optimization

Continuously monitor utilization
Optimize reservation portfolio
Plan for renewals and adjustments

Payment Options Comparison

All Upfront

Pros: Maximum cost savings, no monthly payments
Cons: Large upfront investment, less cash flow flexibility
Best For: Organizations with available capital seeking maximum savings

Partial Upfront

Pros: Good savings with moderate upfront cost, balanced approach
Cons: Some upfront cost and monthly payments
Best For: Organizations wanting good savings with moderate upfront investment

No Upfront

Pros: No upfront cost, immediate monthly savings
Cons: Lowest savings rate compared to other options
Best For: Organizations with limited upfront capital but wanting immediate savings

Monitoring and Optimization

Key Metrics

Utilization Rate: Percentage of reserved capacity actually used
Coverage Percentage: Percentage of eligible usage covered by reservations
Savings Achieved: Actual savings vs. projected savings
ROI: Return on investment for reservation purchases

Optimization Actions

Reservation Exchanges: Exchange underutilized reservations
Scope Changes: Modify reservation scope for better utilization
Additional Purchases: Purchase additional reservations for uncovered usage
Renewal Strategy: Optimize renewal approach based on utilization

Azure Advisor Integration

Recommendation Quality

Machine Learning: Azure Advisor uses ML to identify optimal reservations
Usage Patterns: Analyzes historical usage to recommend appropriate reservations
Savings Calculation: Provides accurate savings projections
Regular Updates: Recommendations updated regularly based on current usage