Hosting your own LLM: the volume you need before it pays off
In brief
According to Journal du Net, many executives consider hosting an open model on their own server to keep data in-house and control bills. A server with a graphics card rented in Europe for about 200 euros a month undercuts Anthropic's August 2026 public prices only beyond about 54 million tokens monthly versus Claude Sonnet, 108 million versus Haiku and 21 million versus Opus. Many firms remain far below those volumes.
Source : Journal du Net - « Héberger son propre LLM pour réduire la facture IA : faire le calcul »
What it changes for an SME
For an SME of 10 to 250 people, tens of millions of tokens a month is still a high volume. The break-even point depends on which model you compare against and on the mix of reading and writing: according to Journal du Net, Anthropic charges five times more for what the model writes than for what it reads, which can raise the required volume sharply. Human time to run the server often costs more than the machine, a medium open model may fall short on complex work, and provider prices tend to fall. Self-hosting mainly makes sense when data cannot leave the company for legal, contractual or secrecy reasons, or for simple, high-volume, repetitive tasks.
What you can do
- Measure one or two months of real usage, splitting tokens the model read from tokens it wrote, using the provider dashboard.
- Run the break-even calculation against the model you actually use, not the most expensive one in the catalogue.
- Add staff or contractor time to operate the server, and test whether a medium open model is good enough on your own tasks.
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