What does local AI really cost? A calculation for decision-makers
Short answer: With 10 to 15 regular users, a local AI system costs around 8 to 12 euros per person and month. This includes hardware, power and ongoing maintenance. That can make local AI considerably cheaper than cloud licences, without usage-based model fees and without company data having to leave your own network for AI processing.
Anyone who wants to introduce AI in their company sooner or later faces the same question: licences from a cloud provider or a system of your own in the building? The common assumption is that local AI is expensive and cannot keep up with the large cloud models. Neither is true anymore.
Contents
- Why many cost comparisons are out of date
- What a local AI system actually costs
- What cloud AI costs in comparison
- The price risk of the cloud
- What other advantages local AI offers
- When local AI pays off
- The hardware is the easy part
1. Why many cost comparisons are out of date
Most comparisons between local AI and the cloud date from a time when local models were clearly weaker. Choosing local back then often meant paying for data protection with lower model quality.
That trade-off has become much smaller today.
An example: Alibaba's freely available Qwen3.8-27B model may be used commercially under the Apache 2.0 licence. In independent benchmarks it now reaches results on the level of capable cloud models. On the Agentic Index of Artificial Analysis, at its highest reasoning level it even ranks slightly ahead of Anthropic's Claude Opus 4.8 (Source).
The matching device is not a server rack. A system with NVIDIA's GB10 chip, such as the DGX Spark or comparable devices from ASUS and Lenovo, measures about 15 by 15 centimetres and offers 128 GB of memory.
For our calculation we assume a purchase price of 5,000 euros.
It can run current AI models with a freely available chat interface. Employees can use it to write texts, analyse documents or research information without paying a cloud provider for every request.
2. What a local AI system actually costs
The costs consist of three blocks: purchase, power and maintenance.
Our example assumes a company in which 10 to 15 employees work with AI regularly. In our experience, a suitably configured device can serve this number well for typical office tasks.
Purchase. 5,000 euros spread over an economic useful life of five years comes to around 83 euros a month.
Power. The DGX Spark's power supply delivers a maximum of 240 watts. In everyday use, consumption is often lower. We calculate with an average of 100 watts around the clock. At the electricity price of 17.2 cents per kilowatt hour reported by the German energy association BDEW, that is around 13 euros a month (Source).
Maintenance. A system that has been set up mainly needs security updates and occasionally a new model. We set five percent of the purchase price per year for this, around 21 euros a month.
| Cost item | Monthly |
|---|---|
| Hardware (5 years) | €83 |
| Power (100 watts) | €13 |
| Maintenance (5% per year) | €21 |
| Total | €117 |
With 10 to 15 users, that is 8 to 12 euros per person and month.
Calculation example from this article. Per-person costs rounded to whole euros.
Once the hardware has been fully paid off after five years and stays in use, around 2 to 3 euros per person remain for power and maintenance. A later replacement is not included.
Even under pessimistic assumptions, meaning only three years of use, 240 watts of continuous consumption and twice the maintenance effort, costs come to around 14 to 21 euros per person and month.
Even in this scenario, local AI is in the price range of ordinary cloud licences.
3. What cloud AI costs in comparison
At first glance, cloud licences look cheap.
Microsoft 365 Copilot Business regularly costs 18.20 euros per person and month with annual billing, currently with a promotional price from 15.60 euros. A qualifying Microsoft 365 plan is required in addition.
ChatGPT Business and Claude Team each start at 20 US dollars per person and month with annual billing.
These entry prices do not tell the whole story, though.
Both providers work with usage limits for certain features. Anyone who works intensively with AI may need additional capacity or a higher plan.
Premium seats for ChatGPT Business and Claude Team cost 100 to 125 US dollars per person and month, depending on the billing type. With Anthropic's Enterprise offering, usage-based model costs can be added to the base fee.
| Solution | Cost per person and month |
|---|---|
| Local AI, realistic (5 years) | €8–12 |
| Local AI, pessimistic assumptions | €14–21 |
| Microsoft 365 Copilot Business | regular €18.20 |
| ChatGPT Business | from $20, Premium from $100 |
| Claude Team | from $20, Premium from $100 |
Prices as stated by the providers, October 2026. Cloud prices with annual billing. The local calculation covers hardware, power and maintenance; individual integrations are not included.
- Local AICalculation example, 10–15 users, 5 years€8–12
- Microsoft 365 Copilot BusinessRegular price, plus a Microsoft 365 plan€18.20
- ChatGPT BusinessPremium seats from $100from $20
- Claude TeamPremium seats from $100from $20
Prices as stated by the providers, October 2026, annual billing. Euro and US dollar are not converted.
In everyday work, this is more than a question of cost.
A usage limit that kicks in in the middle of a contract review interrupts the work. A local system, by contrast, has no monthly token quotas from a model provider. The limit is the available computing power of your own hardware.
4. The price risk that is in no table
The large AI providers are investing billions in data centers, chips and infrastructure.
According to financial reports, OpenAI still expects considerable losses or negative cash flows for the coming years. Anthropic has long-term infrastructure commitments worth hundreds of billions.
These investments have to pay off in the long run.
That software prices can rise is already reality. Microsoft raised the prices of various commercial Microsoft 365 licences as of July 2026.
Our assessment: with growing pressure to become profitable, AI licences and usage quotas could also become more expensive. That is not certain. Competition and technical progress can just as well lower prices.
Anyone who builds their workflows entirely on a cloud licence carries this risk.
Hardware that has already been paid for, on the other hand, cannot be made more expensive afterwards. Power, maintenance and later replacement devices of course remain ongoing or future costs.
5. What advantages local AI offers beyond price
Three further points hardly appear in classic cost comparisons but can be decisive for companies.
Data sovereignty. When an AI model runs entirely on your own infrastructure, company data does not have to be transferred to an external AI provider for processing. That can be especially important for client files, trade secrets and confidential documents.
Independence. A cloud provider can change terms of use, replace models or restrict access. With a locally operated open-weight model, the installed version basically remains available on your own hardware.
Control over the model. Companies decide for themselves which model they use and when they update it. A model that works reliably today does not automatically have to be replaced by a new version.
Especially in regulated industries, these advantages can be worth more than the pure difference in monthly licence costs.
Further reading: Where does your data end up when employees use ChatGPT?
6. When local AI pays off
Not in every case.
For a small team that occasionally has a text drafted, a cloud licence is often enough. Nobody has to buy their own hardware for that.
The calculation turns as soon as several employees work with AI regularly.
Local AI becomes particularly interesting when documents are reviewed, information is researched or recurring tasks are handled every day.
If the number of users grows, an existing device can initially serve additional employees as long as its capacity is sufficient. With higher load, further hardware can be added.
Costs then rise in steps, not automatically with every single user.
On top of that come the advantages in handling sensitive data and less dependence on external providers.
7. The hardware is the easy part
Installing a local AI model and making it usable through a chat interface is no longer an unusually complicated task today.
Employees can already use many typical AI applications with it.
Anyone who also wants to connect internal company knowledge or automate entire workflows needs additional software and integration.
That is exactly what we work on at Alae. With Cortex, Imago and Acta, we connect local AI with a company's knowledge and processes, entirely on its own infrastructure.
This integration can cause additional costs. But it is not a prerequisite for using local AI productively in principle.
Frequently asked questions
Can a local model keep up with ChatGPT?
For many typical business tasks, yes. Current open-weight models can analyse documents, write texts and answer complex questions. For particularly demanding tasks, the largest cloud models still have advantages in some cases.
How many employees can one system serve?
In our experience, 10 to 15 regular users is a realistic figure for typical office tasks. What matters is the type of tasks and simultaneous use.
Can local AI be written off for tax purposes?
In Germany, the tax authorities allow a useful life of one year for computer hardware. The tax treatment should be clarified with your tax advisor.
Conclusion: local AI is no longer an expensive alternative
A capable local AI system often costs less today than many companies expect. With just 10 to 15 regular users, it can be economically more attractive than comparable cloud licences. Anyone who also values data sovereignty and independence should seriously consider the local alternative.
Free check: does local AI make sense for your company?
