Reddit user AndreVallestero posted a cost comparison this week on r/LocalLLaMA: $10,000 spent on a Mac Studio M5 Max for running local LLMs, if redirected to cloud APIs, could buy approximately 6.2 billion tokens on the Qwen Pro plan (tokens are the smallest unit of text processed by LLMs, with 1 token roughly corresponding to 1–2 Chinese characters), 5.7 billion on DeepSeek V4 Pro, and up to 100 billion on DeepSeek V4 Flash. His conclusion is blunt: unless there are hard data compliance requirements, local inference has been outpaced by cloud on pure cost.
What This Is
Essentially, this is a classic "buy vs. rent" showdown upgraded to the AI compute arena. Buying servers and GPUs used to be the default corporate move, since software had to run on-prem; what's special about LLMs is that cloud APIs have compressed unit prices to extremely low levels, making the payback period for "building your own data center" much longer. The Mac Studio M5 Max is Apple's high-end workstation model this year, positioned between consumer and professional, with $10,000 as its top-spec price.
Industry View
The on-prem camp fires back: this Redditor only counted token costs, without factoring in data-exit compliance expenses. Financial, medical, and government clients sending conversation data to third-party APIs may directly hit regulatory red lines—which is why Huawei and Alibaba's private deployment solutions still win orders in the government and enterprise market.
But the counter-counter-argument has teeth too: the M5 Max was never an enterprise-grade solution in the first place. A real "on-prem vs. cloud" comparison should pit H100 clusters against self-built data centers, and in that matchup, the cloud vendors usually still win—because LLM inference marginal costs are still dropping quarter by quarter.
The trend we're tracking: pay-per-token cloud pricing will keep growing more attractive to SMBs. "Buying a Mac to run models" is looking more like a geek toy than a serious enterprise IT option.
Impact on Regular People
For enterprise IT: The ROI model for on-prem AI servers needs a full rewrite. In 2026, unless it's a hard-compliance scenario, paying per API call will likely be more cost-effective.
For working professionals: Freelancers and small teams no longer need to agonize over "should I buy a GPU"—opening an OpenRouter (an aggregator platform for multiple LLM APIs) account in a browser gives them access to mainstream models.
For the consumer market: The Mac Studio product line is in an even more awkward position—pros find it underpowered for serious workloads, regular users find it too expensive, and it will likely need price cuts or a refreshed pitch going forward.