ROI Reporting

Overview

Gradient calculates Return on Investment (ROI) using sophisticated metrics that adapt to your workload characteristics. This documentation explains how Gradient determines and reports ROI, including our methodology for choosing the most appropriate metrics for different scenarios.

ROI Metrics

Gradient reports two key ROI metrics:

1. Savings to Date: Actual savings achieved through Gradient optimization

2. Projected 12 Month Savings: Estimated savings over the next year based on current patterns

How Gradient Calculates ROI

Determining the Right Metric

Gradient uses two different approaches to calculate ROI, choosing the most appropriate one based on your workload characteristics:

1. Cost Change Percentage: Direct comparison of costs before and after Gradient

2. Cost per Gigabyte (Cost/GB) Change Percentage: Normalized metric that accounts for varying data sizes

Selection Logic

Gradient automatically selects the most appropriate metric using the following logic:

1. First, we compute the correlation between input data size and runtime (Pearson correlation coefficient)

2. Then we use the correlation coefficient to select the appropriate metric between "Cost change %" and "Cost/GB change %"

   • If correlation ≥ 0.7 (strong correlation)

     • We use the maximum value between "Cost change %" and "Cost/GB change %"

   • If correlation < 0.7

     • We prefer to use "Cost change %"

     • However, if costs have increased due to increased data size then we fall back to "Cost/GB change %"

Project summary table showing metrics

ROI Calculation Formulas

Using Cost Change Percentage

Savings to Date = 
    Average Starting Cost × Average Cost Change % × Number of Submissions to Date

Projected Annual Savings = 
    Average Starting Cost × Average Cost Change % × Annual Run Frequency

Using Cost/GB Change Percentage

Savings to Date = 
    Average Current Data Size × Average Starting Cost per GB × 
    Average Cost per GB Change % × Number of Submissions to Date

Projected Annual Savings = 
    Average Current Data Size × Average Starting Cost per GB × 
    Average Cost per GB Change % × Annual Run Frequency

Understanding Cost/GB Metric

The Cost/GB metric is particularly useful when:

• Your data size varies significantly between runs

• Overall costs are increasing due to larger data volumes

• You need to measure efficiency improvements independently of data size

Think of Cost/GB like a car's miles per gallon (MPG): Even if you're driving more miles (processing more data), you can still measure if you're using fuel (resources) more efficiently. A lower Cost/GB indicates better efficiency, even if total costs are higher.

Aggregated ROI Reporting

Gradient calculates ROI at two levels:

1. Project Level: Using the formulas above for individual workloads

2. Organization Level: Aggregating savings across all projects

Best Practices for Interpreting ROI

1. Consider Data Size Variations

   • Monitor both cost changes and Cost/GB metrics

   • Understand which metric Gradient is using for your workload

   • Look for efficiency improvements even when total costs increase

2. Review Correlation Metrics

   • Understand how your workload's runtime correlates with data size

   • This helps explain which ROI calculation method Gradient is using

3. Monitor Trends

   • Track both immediate savings and projected annual savings

   • Consider seasonal patterns in your workload frequency

   • Review historical trends to understand optimization impact