AI is different.
Cloud computing genuinely is cheaper on average. It's better than paying for cisco servers, and at scale, it's cheaper than managed platforms (ala Heroku), and it's a coin toss for when you're in the middle ground and constantly approaching the point of rebuilding poor-man versions of existing products but with very very expensive engineering salaries.
In contrast, local models offer dramatic savings, and are magnitude of orders better in certain aspects: like stability - the performance is all over the place with traditional AI companies as they divert compute to their next big thing.
The benefits to maintaining your own infrastructure are pretty moderate to low, with very high risk.
And also, alternate models are pretty easy to use and easy to swap out unlike the vendor lock-in that exists with cloud services.
I agree. The other thing here is that, once you can run LLMs on a single piece of commodity hardware (whether that includes one GPU or several), the difference between cloud vs. on-premise LLMs will largely be about where your hardware is located. There will be very little software configuration involved (just an HTTP endpoint that talks to the GPU). This is decidedly different from cloud products where the moat of hyperscalers is largely in the software and services on top of the hardware, not the hardware itself. (Sure, GPUs will eventually break & need replacement, too, but there's no state to lose, so that's already orders of magnitude easier than replacing hard drives.)
Same reason people pay for things through the AWS marketplace (like Vanta) instead of having to go through their invoicing process.
But AI is just weights, you can run a reasonably intelligent model at home, or on a few GPUs if you're a small-medium sized company, and it doesn't require dedicated maintenance.
Same here. My job as a software dev does not require me to self-host services we need and use. Quite the opposite. But, I am reluctant to hand over all control to AWS or equivalent for several reasons that I will get into here.
I have found that Infrastructure as Code (IaC) and modern tools like opentofu, ansible, combined with frontier AI models and harnesses gives you superpowers in this space. Almost all of our self-hosted services are fully managed by these tools. e.g. We perform backups and test them more often now than we ever did before. Entirely because it is so much easier to do all of that now.
1. Individual dev machines
2. Shared local server
3. Shared server in corporate cloud
4. Third-party LLM SaaS provider
The OS needs updates, file systems get corrupted.
Fans get dirty.
All the things that you need to deal with in hosting your own server infrastructure you have to deal with when hosting your own AI infrastructure (which runs on servers...)
A lot of the reason people outsource normal software is its brittle security properties, not sure that even applies to an LLM - it can go and look up the latest security best practices just like an engineer can.
You know what gives me headaches? When I'm in the middle of a session and the model gets rug-pulled out from under me because somebody at the model provider didn't pay the Trump bill that month.
Or when someone at the model provider decides that the curve-fitting algorithm in my graphics package looks a little too much like Skynet for comfort.
Or when they do any number of other things to undermine my work for the sake of their business model, some of which I won't even notice until the damage is done.
The sad thing is, if you know how inference works, you know that it really is insanely wasteful for everybody to run it locally. If anything naturally belongs in the cloud, it's inference. But at the same time, what choice are we being given?