[derefr]

You can really tell, in the comments sections of changes like these, who is speaking from the perspective of having a professional/business vs. a personal use-case.

Individuals tend to be upset; while professionals are happy that individual free-riders will no longer be sucking up undue amounts of compute power, and so QoS on the system will improve for them.

[TigeriusKirk]

I'd think anyone trying to run a business off a $10 or $49 tier is the one sucking up undue compute power.

[derefr]

A business wants to pay (at least) what something costs, because 1. they’re making money themselves from the result, and 2. they don’t want the thing they depend on to stop being offered. You’re not a free rider per se if you want a cost-plus pricing model but it’s just not on offer. (In that case, it’s instead the provider’s fault for not capturing your value surplus.)l similar to how, in scalping, it’s the original seller’s fault for not charging what the market will bear.)

[ponow]

> similar to how, in scalping, it’s the original seller’s fault for not charging what the market will bear

Absolutely. Unfortunately, people hate scalpers, irrationally if you take a broad view.

[version_five]

Google's business model is exploiting people's data to sell advertising. If you use a google "product" that's not advertising, you're not paying what it costs, at least in money. In line with what you're saying, I want to compensate a cloud provider for their services in a way that makes me a customer instead of a vector for advertising or data exploitation.

[derefr]

Google Cloud, in fact, charges you what things cost (with markup, and economies of scale), just like AWS does. There are no loss-leader Google Cloud services. Google has separate b2b and b2c business models.

(I should know; we are a GCP customer, and we pay them quite a lot, in fact. They pinch every single penny, as they should. Nothing costs less than it would to do it on bare metal ourselves; we only save in CapEx in that we don’t have to pay ourselves for as many servers as are required to run something like e.g. reliable object storage, or BigQuery’s hot-idled compute.)

[XorNot]

The mistake you're making is thinking that paying them would mean they stop doing this. Why would they? You're not aware of how they're doing it now, why would money being involved change it unless there was a risk of you leaving the service?

The problem with advertising is that paying for a product just proves you have disposable cash to pay, which in turn makes you a more valuable advertising target.

Amazon, for example, aggressively monitor the types of services people are building that run on AWS, and then to launch competing products as "native" AWS services - knowing that to the rest of their customers, buying the "AWS native" thing is much more appealing then dealing with any 3rd party vendor.

[Jugurtha]

I've explored Colab users as a target audience for our product, especially given the fact that practically all the posts on the GoogleColab subreddit complain about how bad it is. Even those with the Pro+ or Pro tend to revert to the free Colab offer because there's no transparency.

What I understood from my interactions is that they complain but will not use a paid product because even though they're paying from nothing to $49, the actual resources used is in the $800/month ballpark (notebooks running 23 hours per day, seven days a week, using a GPU).

These are clearly hobbyists. The pros had different problems such as not being able to pay for it from certain countries.

In other words, there are people who need a notebook to run and not crash and willing to pay for that and there are others working on toy projects/individual pet projects or projects with no real stakes who'll complain about it but will not switch because another company will not really subsidize usage.

Yes, there are other companies that offer notebooks, but our product was for professionals in the ML field, and there's much more to ML project than running a notebook (real time collaborative notebooks, automatic experiment tracking, plugging compute from any cloud provider, one click model deployment, object storage like a filesystem, live monitoring dashboard for deployed models, and more).