Every time somebody questions why you might "trust" AWS (or Azure or GCP or whatever), or why you'd pay this premium, I realize they are not accustomed to working in enterprise environments.

In my case, I work at a large enterprise with strict data governance built into customer contracts, and (partly related, partly not) our own governance concerns. Using vendors where you not only have infosec permission, but they are also listed as data processors in our contracts with our customers is the way not to get fired and sued.

If I'm playing around at home, with my own code and data, I can do whatever I want. But with my employer and customer? Absolutely not. It's the same reason we don't use whatever is the flavor of the month frontier model is.

Side hustles and startups just have an entirely different set of constraints and considerations.

On top of this, there's a vast difference between "what do you mean that team spent $1000 on AI in their expense report, what did we get for that?" vs. "oh, the company-wide AWS bill went up by a few percent, let's look into that when we have time." The latter makes projects far more viable.

Or to put it simply, nobody ever got fired for buying IBM.

I have just moved from a free environment in which I was able to use any AI harnesses or models to a strict enterprise environment.

I was shocked to realize how difficult it has been to have a GitHub CoPilot license on Azure. I mean, they're both Microsoft products. But no, the IT now has to figure out how to set up a GitHub enterprise, link to Azure subscription, and all that.

while true, everyone signed this same data privacy agreement with anthropic / openai a long tiem ago