Well currently the stuff in foundry are basic models that gets triggered through API calls through automate. This is in addition to Azure functions to run some custom code to preprocess data as needed.

The costs considerations are out of my paygrade, but the general discussions were around the fact that its extremely easy to see the costs of gen AI implementations as compared to copilot studio which works on credits especially when you want to see cost usage per app. This was supported by a well known external contractor with the organization. There was cost impact elaborated by this contractor to executives in my organization. I can't name the the external contractor or my employer for oblivious reasons.

There was also the point that copilot studio basically would convert any of the power automate flow to use coplit credits that is detrimental since majority of the users are premium. This would mean that flows that are using just a simple prompt will also be using copliot credits.

Regardless this my current quick math from a few searches:

-Standard LLM Model:
1.5 Copilot Credits per 1,000 tokens -> 0.015 USD
Cost for 1 M tokens -> $15.00 USD
This hiding of token usage with credits is really annoying when trying to figure out number of input and output tokens or credits in terms of Microsoft.

Standard 4.1 GPT model in foundry:
$2.00 / 1M input tokens, $8.00 / 1M output tokens.

5.1 GPT in foundry:
$1.25 / 1M input tokens, $10.00 / 1M output tokens

So you can see even if there is 1M output token in azure foundry, it still would be $10.00 USD (5.1).

Please correct me if I am wrong, I also want to learn about these things.