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Comment by faitswulff | Hacker News Reader

The article makes no sense. I can't use OpenRouter as a general purpose computing device. Why are we comparing a whole computer to a single purpose SaaS?

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They're responding to the people doing things like buying the most expensive Mac they can find specifically to do local inference for their AI agents.

Some do it to have control over their ability to use AI. Some do it because they think it will be cheaper to not have to pay a SaaS to generate tokens for them.

But for those interested in the latter case, it seems like it's not actually cheaper after all, at least at current prices. But then I don't expect prices to drastically jump because of how much competition there is in model development.

It's worth paying a premium for the privacy (assuming that llama.cpp and ollama aren't sending my sessions back to the cloud regardless...), and for the concerns about not getting a surprise bill.

> not getting a surprise bill.

Correct me if I'm wrong, but I believe this is a feature that only Google has figured out how to implement. All of the other pay-as-you-go token services have a cap you can set, some by monthly spending, some with API key resolution, others by how much you put into the account. I use many, and if configured with auto-purchase disabled, it's not possible to have a "surprise" bill (except for Google!)

You also have control over your costs. It is reasonable to assume that tokens will cost significantly more in the near to medium future as the market consolidates and subsidies decline.

Google, Microsoft, Meta, Anthropic, OpenAI, Oracle and others are going to be looking to recoup all the money that they’ve spent to date. Why would the price go down in the future?

> Why would the price go down in the future?

Because price is driven mainly by competition, not by a desire to recoup prior spending.

Investors aren't doing things out of the sheer goodness of their hearts, so if they could just bump the price up they'd have already forced it up. The very existence of workable local models puts a cap on how high the price can realistically go, but the high level of competition still extant makes the price floor ever closer to the actual cost to generate tokens.

The AI numbers are huge, but I remember similar arguments about residential high-speed internet. According to Gemini, the "price for internet" is down 12% in real terms (ugh, capitalism!), while speeds are staggeringly faster.

The providers have spent a fortune on wireless, pulled a lot of fiber/cable, and it's cheaper than it was when it started.

No, that’s not the point. I think this is to help people who are thinking about getting a beefier Mac so they can run their LLMs on it too. Some in particular want a dedicated Mac Mini or Studio for this purpose. The breakdown, even if slightly flawed, offers a good insight into the economics of it.

For most people, they might be better off with OpenRouter models and providers supporting Zero Data Retention. On the cloud, that’s as good as it gets for privacy - your data is never retained beyond the life of the request.

> your data is never retained beyond the life of the request.

Like with OpenAI for a year?

” In June 2025, the court ordered OpenAI to retain its consumer and API customer chat logs indefinitely, including any that had been deleted, so they could be investigated […]”

https://www.techspot.com/news/109839-openai-no-longer-requir...

I think it's because there are a lot of people writing articles about the benefits of running local models. I think it's fair to say that there are daily threads on HN singing the praises or local inference. I also see people buying new hardware where the main trigger is ability to run local models.

But the people who want to do local inference are putting some amount of value on privacy that’s not captured by the raw monetary value so just comparing the price is somewhat beside the point, it’s also true that, if you have eg a Mac and you use that as your main computing device then you would have spent money on it anyway, so you can’t even really compare its value to spend on something that’s not general purpose.

That's a lot of assumptions. I think there are also people buying new hardware specifically for this purpose, and their motivation to do it is thinking it will be cheaper in the long run. Privacy is not necessarily the motivation.

My overall opinion is that the smart thing is not to upgrade to the maximum memory for AI purposes. It's worth quantifying how much extra we pay for privacy.

I replied to a comment asking why the article exists.

As for privacy, I'm sure there are many people that are not so interested in that aspect.

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