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In short
On-premise AI runs on hardware that belongs to the organization, location or managed internal environment. Instead of sending all processing to an external AI service, key parts stay closer to your own infrastructure.
Infrastructure - 5 min
What is on-premise AI?
On-premise AI means AI runs on infrastructure managed by your own organization or location. It can help with privacy, compliance, continuity and control over systems.
5 sections Practical explanation For teams and organizations
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When do you choose it?
On-premise AI makes sense when data is sensitive, systems need to keep working locally or external platforms do not fit policy, cost or availability requirements. It is especially relevant for organizations with clear requirements around management and access.
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How is it different from cloud AI?
With cloud AI, processing mostly runs on a third-party provider's infrastructure. With on-premise AI, more responsibility sits with the organization or a partner managing the local environment. You gain control, but management needs to be arranged properly.
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Which parts are involved?
Beyond the model, you need hardware, software, access control, logging, updates, monitoring and support agreements. On-premise AI is not just installing a model; it is building a usable environment.
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Practical nuance
On-premise does not mean nothing may ever connect externally. Some organizations combine local processing with selected external integrations. The important part is making a conscious decision about what stays local and where external services do or do not make sense.
Related terms
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What is an LLM?
An LLM is a large language model that can understand, predict and generate text. It often feels smart, but the quality of the result still depends heavily on context, model choice and how you use it.
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What is edge AI?
Edge AI means AI runs close to the source of the data, for example on a device, server or local network. This can improve speed and often gives more control over privacy, continuity and cost.
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What is local AI?
Local AI means AI runs inside your own environment, for example on a local device, server or network. It is about more control over data, availability, cost and dependency on external AI platforms.
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