I remember early in my career working for the Retail group of a large company and I started learning about what was the "Best" price for a product. And the answer was always, "it depends, what is your goal? Sell more? Sell more of a different product (many products are linked together)? Help out your supplier?" And there are many, many other trade offs. IoT has the same issue with Real Time. Do you need the temperature once a minute? Once a second? What about speed? You can get the speed of a vehicle at 2000 times a second. But do you need it? Can the mobile network handle that much data? And do you want to pay for it?

I think of all of this when I see all of the hype in AI/ML that is currently happening. "We need supper fast GPUs to do AI" Do you need it for everything? They are a lot more expensive and use a lot more energy.

Tony Rigoni wrote a blog post about this and he gives you three things to think about when deploying AI:

Deploy only the amount of compute you need to meet the performance requirements of your application and use general purpose rather than specialized processors as broadly as possible to maintain flexibility for future compute needs.
Switch AI CPU-only inferencing from legacy x86 processors to Cloud Native Processors. With the performance, you may be able to deploy as CPU-only for a wider range of AI workloads than with legacy x86 processors.
Combine GPU with the power efficient processors for heavier AI training or LLM inferencing workloads.
Basically, use the more efficient processors where you can and save specialized processors for when you need them.

Check out his full post here.

And BTW while there might not be a "best price", there is a worse price. $1.06, followed by anything ending in .06 . You will sell a lot more at 99 cents and you won't sell less at $1.09.

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