On-premise AI
On-premise AI means running AI models and the systems around them on hardware in your own facilities or private data center, rather than calling a vendor's cloud service. Data is processed entirely inside infrastructure you own. It is typically chosen for strict data residency, air-gapped environments, regulatory requirements, or very high, steady workloads.
01why it matters for a business
Some organizations cannot send certain data to any outside service: defense suppliers, some government work, facilities without reliable internet, or businesses whose contracts forbid it. For them, on-premise is the only path to using AI on that data. For others it is a cost and control decision: steady, high-volume workloads on owned hardware can become cheaper over time than per-token fees.
The tradeoffs are significant. You buy and maintain GPU hardware, which carries a large upfront cost and a refresh cycle. You run your own security, monitoring, and updates. You are limited to models you can legally and practically run, which generally means open-weight models. Many companies find that a private deployment in their own cloud account meets the same requirements with less burden.
02what it looks like in practice
A manufacturer wants to use computer vision on its production lines to spot defects. Its plants have unreliable connections and strict rules about images leaving the site. It runs vision models on servers at each plant, with model updates pushed centrally during maintenance windows. A cloud service would add latency and a dependency on the network; on-premise inference fits the physical reality of the operation.
03common mistakes
- Choosing on-premise for comfort when the data could legally and safely live in a private cloud deployment.
- Underestimating staffing. Someone has to run, patch, and monitor the stack.
- Buying hardware before measuring the workload, then sizing it wrong.
- Forgetting the rest of the system. Retrieval, logs, and applications need the same controls as the model.
04related terms
- Private LLMA private LLM is a language model deployment in which your data and prompts stay inside an environment you control or have contractually isolated, rather than a shared consumer service.
- Open-weight modelAn open-weight model is an AI model whose trained parameters, called weights, are published so anyone can download and run it on their own hardware or cloud, subject to its license.
- Computer visionComputer vision is the field of AI that lets software interpret images and video: detecting objects, reading text, measuring, classifying, and spotting anomalies.
- Total cost of ownership (TCO)Total cost of ownership (TCO) is the full cost of a technology decision over its useful life, not just the purchase price or build quote.
- Inference costInference cost is what it costs to run an AI model to produce outputs, as opposed to the cost of training it.
05where insomnia club fits
Insomnia Club scopes where your AI should run (public cloud, private cloud, or your own hardware) against your data rules and workload, then builds the system to match.
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Scoped by the people who ship it. Priced before we start.
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