Local vs cloud LLM cost in 2026
Cloud token prices have fallen hard since 2023, and most AI bills have still gone up. This guide separates the three effects and shows where local hardware actually wins.
Who this is for
Teams spending four figures a month on AI who want to know whether owning hardware is a real option or a distraction.
Three effects people conflate
- Unit price per token is falling. The frontier token price index sits about 84 percent below its March 2023 base, and the same quality keeps getting cheaper. Anyone claiming cloud AI gets more expensive per token is wrong.
- Effective spend per seat is rising anyway. Premium tiers at 100 to 200 euros a month appeared in 2025 and 2026, one major coding assistant moved to usage credits in June 2026, and another raised its business seat from 10 to 19 dollars. Same subscription, less included.
- Usage growth dominates both. Surveyed CIOs expected LLM budgets to grow about 75 percent year over year, and most companies miss their own AI infrastructure forecast by more than a quarter.
- Our default assumes these cancel out: cloud unit cost changes 0% per year unless you move the slider. Falling list prices against rising tiers is a wash, and a neutral default is the only one we can defend.
What local actually costs
- Capital, paid once: the machine, plus installation. This is the number that has to be earned back.
- Electricity, every month. At 0.25 euro per kWh net, a machine running around the clock costs 3.4 times what the same machine costs during business hours. Running hours matter more than most people expect.
- Maintenance, every month: a yearly share of the hardware price, from 8 to 15 percent depending on how many machines you run.
- The line nobody budgets: someone has to own the thing. Updates, monitoring, a model upgrade when better open weights land. That cost is real even when it never appears on an invoice.
When local wins
- High steady volume. Hardware is a fixed cost, so the more tokens you push through it, the cheaper each one gets. Cloud bills scale with usage; a machine does not.
- Usage based plans with heavy users. Coding agents consume tokens at a rate seat pricing was never designed for, which is exactly why providers moved to credits.
- Predictable load you control. A product serving customers at all hours keeps the hardware busy; an office tool that idles overnight does not.
When it does not make sense
- Small teams on standard seat plans. Five people on a twenty euro plan spend 1,200 euros a year. No machine we sell pays that back inside 36 months, and we will tell you so in the first line of the report.
- Work that needs frontier quality on every request. An open model on your hardware is not the model you use today. If the gap costs you more than the hardware saves, the hardware is the wrong purchase.
- Bursty, unpredictable load. If your peak is ten times your average, you buy hardware for the peak and pay for it while it idles.
- No one to own it. If nobody on the team wants the pager, a hybrid setup or staying on cloud is the honest answer.
A worked example
- A team of 25 on coding assistants and chat, paying around 2,000 euros a month net. Sizing lands on a multi GPU workstation. The capital cost is earned back somewhere in the second year, and the machine keeps running after that. Change the electricity price or the growth assumption and that month moves; that is the point of the slider.
- Your numbers will differ. Ours are defaults; yours are on your invoice.
Sources
Cloud price trends from published provider pricing and industry indices, reviewed September 2026. Electricity from German commercial tariff analyses, February 2026. Hardware prices from EU vendor list prices, dated per catalogue entry.