Not inherently, but how we're using it makes it cheaper. Our stack has ~50 different ML models being served live, and GCP makes it easy to treat each model as a micro-service, and give auto-scaling to each one.

This is in contrast to the easiest way we found to deploy the same architecture on AWS using Elastic Beanstalk, which involved one really big instance (that was constantly growing as we added more models), and the costs that come with that.

Because we were loading all the models startup time was long which meant that server would return 5xx errors which created more instability. We would had to do some engineering around it with a mix of config and code changes.

The bigger issue was that he had to use bigger machine as we added more custom ML models for our customers. New architecture gives us huge $$ saving and more visibility into performance of each model.