How much does your data privacy cost?

Will this cost structure always be this way and are there other benefits to not running your LLM on the cloud?

E.g.

Privacy

Uptime

Future cost structure controls

This is a field that has moved very quickly. And it has moved in a direction to try to trap users into certain habits. But these habits might not best align with what best benefits end users today or some time in the future.

Open router doesn't cost money per say, it depends on the providers pricing

Unless I'm misunderstanding, this is counting the entire laptop in the cost of generating tokens. The calculation seems to omit that, in addition to receiving LLM output, you have also received a laptop in exchange for your money. If you intend to put this machine in a dark corner and run it solely as a token-munching server, a laptop would be an exceptionally poor choice of technology for this purpose. But if you intend to use the laptop as a laptop, having a laptop is a pretty big benefit over not having a laptop.

You also get the benefit of privacy, freedom from censorship, and control over the model used (i.e. it will not be rugpulled on you in three months after you've built a workflow around a specific model's idiosyncrasies).

OpenRouter and other LLM platforms are being subsidized by VC investment to less than it costs them to run inference, the MacBook Pro is not

The author only compared output token costs -- but for typical agentic workloads, input tokens dominate the costs by a large margin. Running inference locally, input tokens are, to first order, free. (They only generate implicit costs through higher time-to-first-token, higher power use, and lower token output speed).

I don't hear people debating which is cheaper, local or cloud run models. The conversation, at least what I hear, is a lot of the time users are not utilizing an awful lot of tickets all the time, those providers will be paid if you never use them. If 80% - 90% of the work I and my team are doing with Ai is grunt work, write tests for this, implement a FFT here, write the dB query for X. Nothing exhausting. Those who are using AI for whole cloth "vibe coded" applications and services are definitely better suited to cloud. If a work laptop can run my local models and get my works needed performance for development, why wouldn't I as a company prefer that?

Add to that the privacy improvements and data protection and potentially further specific inferance if needed it's a no brainer.

Again, Ai is a tool, and the right tool for the job, I would wager with no evidence looked up, is that the majority of Devs would be happy with 10-30 per second locally.

I simply can't go back to cloud AI. Privacy and full control are more important to me than speed and SOTA models.

Wouldn’t a Mac Mini be a better comparison?

This isn't a good analysis, and it's because it keeps rounding everything up. He rounds up the cost of electricity by 10%. He has a range of power use, takes the high end (which is 2x the low end) and multiplies it by the inflated electricity cost.

But then they talk about using a newly purchased Mac to do the inference, running at full capacity, 24/7. Why would you do that? Apple silicon is fast but the author points out: you're only getting 10-40 tokens per second. It's not bad, but it's not meant for this!

It's comparing apples to oranges. Yeah, data centers don't pay residential electricity rates. Data centers use chips that are power efficient. Data centers use chips that aren't designed to be a Mac.

Apple silicon works out pretty good if you're not burning tokens 24/7/365 and you're not buying hardware specifically to do it. I use my Mac Studio a few times a week for things that I need it for, but I can run ollama on it over the tailnet "for free". The economics work when I'm not trying to make my Mac Studio behave like a H100 cluster with liquid cooling. Which should come as no surprise to anyone: more tokens per watt on hardware that's multi tenant with cheap electricity will pretty much always win.

"Accelerated depreciation (if any) from shortening the lifespan of the device will be more expensive than the electricity"

Shortening the lifespan?

Slightly different slice into this a very similar situation (local vs OpenRouter AI inference).

But in _every_ metric other than privacy it was better to run via OpenRouter than a local model, and not by a small amount.

Direct link to the comparison charts:

https://sendcheckit.com/blog/ai-powered-subject-line-alterna...

I like that the numbers were crunched, but the answer to these is always a bit of a foregone conclusion.

* Industrial power pricing

* Wholesale hardware pricing

* Utilization density of shared API

means API always wins a cost shootout.

Privacy & tinkering is cool too though

Your laptop AI costs too much? Speculative investors can help!

So I did the India-specific analysis for a tier-3 city. Here, electricity costs 1/3rd of the US version, and you also get solar subsidy up to a certain amount.

https://shorturl.at/q6gRE

tldr;

Hardware deprecation costs are the major factor.

But, if we assume ZERO hardware deprecation (not realistic), then local inference becomes super cheap.. roughly, 90%+ cheaper.

Third case: the break-even happens only if we can get at the very very very least, 8.7 years of useful hardware life. A more realistic number, however, when working 8 hrs/day and not of 24 hrs/day, is around 25 years.

So, for now, local inference is preferable if you deeply care about privacy. From cost perspective, it's still not there.