Understanding Your Score

How the Efficiency Score is calculated, what the health ratings mean, and how to interpret the savings estimate.

How the Score Is Calculated

The Efficiency Score is the average of four individual ratings, each derived from a specific efficiency indicator. Every indicator is rated independently as Good, Needs Improvement, or Poor based on the thresholds below.

Rating	Numeric Value
Good	100
Needs Improvement	50
Poor	0

The overall Efficiency Score is the average of the four numeric values, giving you a single percentage between 0% and 100%.

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Rating Thresholds

Each indicator uses a different scale depending on whether a lower or higher measured value is better.

Warehouse Idle Time

Measures the percentage of compute time spent idle (no queries running) across all warehouses over the last 21 days.

Rating	Idle Time %
Good	< 3%
Needs Improvement	3% – 8%
Poor	> 8%

Multi-Cluster Idle Time

Measures idle time on non-primary clusters over the last 7 days. Only applicable to Enterprise edition and above.

Rating	Idle Time %
Good	< 3%
Needs Improvement	3% – 10%
Poor	> 10%

Warehouse Sizing Efficiency

Evaluates whether your warehouses are right-sized for their workload based on query history from the last 7 days. Higher is better.

Rating	Sizing Efficiency
Good	≥ 80%
Needs Improvement	60% – 80%
Poor	< 60%

Clustering Column Efficiency

Measures how effectively clustering keys are configured and how well partition pruning performs over the last 7 days. Higher is better.

Rating	Clustering Efficiency
Good	> 90%
Needs Improvement	70% – 90%
Poor	< 70%

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Estimated Annual Savings

The savings estimate is calculated per indicator and then summed to produce a total. For each indicator:

1. The app measures the inefficiency over the lookback window (7–21 days depending on the indicator).

2. Costs are attributed using your cost-per-credit rate (auto-detected from RATE_SHEET_DAILY when available, or entered manually).

3. A weighted average between a lower and upper savings bound is computed (65% weight on the lower bound, 35% on the upper).

4. The result is annualized by extrapolating from the lookback window to 365 days.

The savings breakdown shows how much each optimization area contributes:

• Auto-Shutdown savings — eliminating idle warehouse compute

• Auto-Scaler savings — reducing unnecessary multi-cluster overhead

• Smart Pulse savings — right-sizing warehouses for their workloads

• Clustering savings — improving or removing ineffective clustering configurations

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Organization-Level Projection

If your Snowflake account is part of an organization, the app queries ORGANIZATION_USAGE.USAGE_IN_CURRENCY_DAILY to determine costs across all accounts. It then projects your account-level savings ratio across the organization to estimate the total optimization opportunity.

This projection is an estimate based on extrapolating the current account's efficiency patterns. Actual savings across accounts will vary.