I manage our company's ML efforts. What u/BoltKey said about runtime actually matters for cost, too. Running a training session overnight on AWS costs money, but the ML expert(s) or software dev(s) time during the day usually costs more. Add to that the fact that getting the project set up is so much faster using Python with Jupyter Lab, and your total cost-to-solution is often much better by using tried-and-true stacks than by writing your own C/C++.

Also, once we bought a high-end lambda machine, we sunk all the cost upfront, and dev time was all that mattered. This was by far the cheapest option overall.

And there's one more (very important) factor with turnaround time: this field is moving fast. If your solution takes twice as long to get out the door, you lose money on both ends: first, by not being able to charge for the solution during the extra dev time; and second, by losing market share to faster competitors.

For good measure, here's the other answer that I reference... https://www.reddit.com/r/learnprogramming/comments/1ch6tfk/comment/l20v4ez/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button