What does a private LLM cost?
There is no fixed price tag: costs depend on your goal, your data volume and the desired infrastructure. A lean model for one department requires a different investment than an organisation-wide system. Against the startup costs stand lower ongoing costs and independence from expensive licences.
On this page you will read which factors determine the price, so you can make a grounded estimate instead of a finger-in-the-air figure.
Which factors determine the costs?
Scope. One process or department is cheaper than an organisation-wide rollout. We often start small with a measurable result and scale from there.
Data volume and complexity. How much data the model must unlock and how structured it is determines the work on the RAG pipeline and the compute required.
Infrastructure. On-premise on your own hardware requires a different investment than a private cloud. Each has its own cost profile.
Model choice. A lean open model needs less compute than a large model. The choice affects both startup and usage costs.
Integrations and management. Connections with your existing systems and ongoing technical management count towards the total cost.
Read more: On-premise vs. private cloud
One-off versus ongoing costs
View costs as total cost of ownership over time, not as a single figure.
One-off: the AI-readiness scan, setting up the environment, building the pipeline and the initial fine-tuning or integration.
Ongoing: hosting or hardware, maintenance, management and further development.
The startup requires an investment, but afterwards ongoing costs are often lower than recurring per-user subscriptions on commercial AI tools.
How do you keep the budget under control?
The fastest route to a reliable estimate is the free AI-readiness scan. In it we map your scope, data and infrastructure and determine where AI delivers the most value. Based on that you get a grounded indication, instead of a generic figure that does not match your situation anyway.
Read more: Private LLM for enterprises
