Too many models confusing choice

When teams have too many models to choose from, the decision process gets noisy very quickly. Every model looks attractive in isolation, but the lack of a clear selection framework makes it hard to know which one truly fits the job.

This usually happens after experimentation expands faster than operational discipline. Without clear criteria, teams compare models by habit, reputation, or a few demo results instead of by the actual needs of the product.

The fix is to narrow the decision around use case, latency, cost, reliability, and maintenance burden. A smaller, well-chosen model stack is often easier to run than a complicated menu of options.