Measure AI Waste Before It Costs You
A Croms is one unit of preventable AI workflow waste — any execution cost, latency penalty, structural inefficiency, or failure risk that pre-execution intelligence would eliminate.
The Principle
The lower your Croms, the more efficient and governed your workflow.
Most teams don't know how much waste their AI workflows generate until the compute bill arrives. Croms make waste visible before execution.
How Croms Are Calculated
Every workflow is scored across four dimensions of waste. Each dimension is weighted based on its impact on real-world execution costs and reliability.
Cost Waste
Weight: 35%
Overspend from unoptimized model selection, redundant calls, and uncached operations
Latency Overhead
Weight: 25%
Execution time lost to sequential processing that could be parallelized
Failure Risk
Weight: 25%
Probability of runtime failures from missing validation, retries, and governance gates
Structural Gaps
Weight: 15%
Architectural inefficiencies in workflow design, modularity, and clarity
Severity Levels
Well-optimized workflow
Room for improvement
Significant waste detected
Urgent optimization needed
Why Croms Matter
AI workflows are expensive to run and difficult to debug. A single poorly-configured model selection can cost 10x more than necessary. A missing retry handler can cascade into hours of lost execution time. Sequential processing where parallel execution is possible wastes both time and money.
Croms quantify all of this into a single, actionable number. When you see 847 Croms on a workflow, you know exactly how much preventable waste exists — and every optimization recommendation tells you precisely how many Croms it eliminates.
This is the difference between guessing and governing. Croms turn AI workflow optimization from an art into a measurable discipline.
Scan Your Workflow for Croms
Build a workflow in Cromus and run the optimizer to see your Croms count. Every recommendation shows exactly how many Croms it eliminates.