Why Your AI Model Costs More on a Different Machine

Preprint available on arXiv.

The 30-Second Summary

TL;DR: Developers often worry that moving an AI model from Linux to macOS (or changing Python versions) will break its accuracy. We found that while accuracy can drop (in ~23% of cases), the real danger is Instability in Time and Cost. Changing your environment configuration can silently double your processing time or cloud costs in nearly 100% of cases, without you realizing it until the bill arrives.

The Problem

“It works on my machine” is the classic developer excuse. In traditional software, if the environment changes (e.g., different OS), the code usually crashes immediately.

But AI systems are “non-deterministic.” If you change the underlying hardware (CPU architecture) or the software stack (Python version, OS), the system might not crash. Instead, it might:

Give slightly different predictions (Model Instability).
Take twice as long to run (Time Instability).
Cost significantly more to compute (Expense Instability).

We wanted to know: Which of these actually happens, and how bad is it?

Our Approach

We conducted an extensive empirical study using Travis CI.

Subjects: 30 Open Source AI-enabled systems.
Variables: We permuted 8 different environment configurations, changing the Operating System (Linux vs. macOS), Python Version, and CPU Architecture.
Metrics: We measured shifts in Model Performance (accuracy), Processing Time, and Total Expense.

Key Results

Our findings were surprising. Most people expect the model to “break” (lose accuracy), but that wasn’t the biggest issue.

Accuracy is (Mostly) Safe: When switching from Linux to macOS, only 23% of projects saw a significant change in model performance.
Time is Volatile: 96.67% of projects experienced significant instability in processing time. A model that runs fast on Linux might crawl on macOS.
Cost is Guaranteed to Change: 100% of the studied projects saw expense instability.

Why This Matters

For DevOps Engineers and ML Practitioners, this is a wake-up call.

Don’t just check accuracy: Passing your unit tests isn’t enough. You need to benchmark time and cost across your deployment environments.
Standardize early: Since configurations drastically impact cost, finding the “Optimal Configuration” isn’t just about performance—it’s about financial efficiency.
Containerize: To avoid these “silent” cost spikes, use Docker to ensure your production environment matches your testing environment exactly.

Resources

Cite this post

@article{rahman2024impact,

  title={The Impact of Environment Configurations on the Stability of AI-Enabled Systems},

  author={Rahman, Musfiqur and Khatoonabadi, SayedHassan and Abdellatif, Ahmad and Samaana, Haya and Shihab, Emad},

  journal={arXiv preprint arXiv:2408.02825},

  year={2024}
}