Cloud inference: shared service or dedicated GPU?
The workspace and the model do not necessarily run on the same infrastructure. Define both before comparing costs.
Editorial analysis and illustrative technical scenarios. They do not define the features or integrations included in a UTOMAI subscription. See Private AI On-prem pre-launch information.
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Choosing Cloud does not automatically mean renting a dedicated GPU. A hosted business workspace can call a managed inference service, while a dedicated environment can run a compatible open-weight model on reserved compute. These are different infrastructure and cost choices.
Separate the workspace from inference
Start with two questions: where are documents and conversations stored, and where is each model request processed? Include backup and management services in the same review. Hosting the application in Italy does not make a request to an external endpoint Italian.
UTOMAI private AI uses dedicated cloud servers in Italy or a dedicated on-premise machine. European cloud locations can be assessed on request. The proposal confirms the actual resources, storage and model execution locations, data flows and operating responsibilities for either mode.
Size the workload, then price the service
The model revision, available memory, context length, simultaneous requests and expected response time all affect sizing. Enabled users are a commercial segment, not a measure of concurrent GPU capacity. A small representative workload is more useful than a hardware specification selected in advance.
Compare the full service: inference or compute, application resources, storage, backup, updates and assistance. Dedicated GPU rental is only one cost component. The company should receive a configured platform and a clear operating agreement, without having to become its own inference operator.
