MgntUtils Cold Filtering Lets Teams Verify AI Token Savings on Their Own Log Data

A follow-up guide from the author of MgntUtils explains how technical and managerial decision-makers can independently verify AI token cost savings before making any production changes. The tool supports 'cold' stacktrace filtering, meaning it can process stacktraces already captured as text from existing logs, rather than only live exceptions. Users can run a small standalone Java program — isolated from their main systems — by adding the MgntUtils jar to a classpath and applying filters using their own company-specific package prefixes. The filtered and original stacktraces can then be compared by line count, byte size, or token count using a model's tokenizer to estimate real cost reductions. The approach is designed to let teams validate the tool's claims on their own data without relying solely on the author's published benchmarks.