On-Premise AI
On-premise AI is running AI models on servers you own, inside your building or your private data centre, instead of a cloud provider's machines. Your data stays on your hardware and you control the entire stack, from the GPU up to the model.
No jargon, no math
In plain words, what is it?
The models, the vector database, everything is installed on machines you bought. Inference requests never leave the building: no internet round trip, latency you can predict, and no third party ever touches the data. That is why regulated businesses choose it: patient records, defence contracts, industrial recipes. The trade is explicit. You buy the GPUs up front (capital expense), then pay power, cooling and your own staff forever. You also inherit the data centre's problems: firmware, failed drives, capacity planning, the 3am alarm. Scaling means ordering machines weeks or months ahead, not moving a slider. Where it breaks: your ceiling is the hardware you already own. A project that suddenly needs ten times the compute will wait for a purchase order, and a small team can spend more time babysitting servers than using the models.
Terms that live nearby.
Edge AI
Edge AI is running AI models on the device where the data is born: a camera, a sensor, a phone, a factory gateway, instead of shipping everything to a cloud data centre and waiting for the answer.
ReadCloud Compute
Cloud compute is renting processing power, storage and AI accelerators from a provider's data centre over the internet, paying per second, per gigabyte or per token instead of buying and running the machines yourself.
ReadGPU
A GPU is a processor built to run thousands of small calculations at the same time. Designed for graphics, it turned out to be the perfect engine for AI, because running a neural network is mostly one enormous pile of matrix multiplications.
Read§ 03
Want this running in your stack?
We design and ship AI systems with permissions, logs and human approval. It starts with a 30-minute scoping call.