On-premise

Infrastructure
About 1 min read

A model where an enterprise directly installs and operates hardware in its own data center or physical server room. It is an essential model for the financial and public sectors where data sovereignty and security are critical, and has recently gained renewed attention as key infrastructure due to the growing demand for 'private AI' using sensitive internal data and local inference of open-source LLMs.

Also known as
On-PremiseOn-PremIn-house deployment

Detailed explanation

On-premise is an infrastructure deployment model contrasted with cloud computing, where an enterprise physically hosts hardware and manages software within its own facilities. Providing full control and security over data, it is preferred by highly regulated industries like finance, healthcare, and the public sector. In the AI field, it forms the foundation of 'private AI' strategies, where open-source models like Llama 3 are deployed on self-hosted GPU servers to prevent corporate data leaks. Although the initial capital expenditure (CAPEX) is high, it offers better cost predictability than cloud operating expenses (OPEX) in the long run for consistent workloads, along with low-latency real-time processing. Recently, hybrid models combining cloud and on-premise have been expanding to achieve both security and flexibility.

Why it matters for AI tool selection

When AI models train on or process proprietary data—which is a core corporate asset—it completely eliminates the risk of data leaking to external networks. Especially when running continuous large-scale inference workloads, operating in-house GPU servers can be more economical in the long run than paying cloud API invocation fees.

What to check before adoption

  • Is transmitting data externally (to the cloud) prohibited by security regulations?
  • Are power and cooling facilities equipped to operate high-performance GPU servers like H100 or L40S?
  • Is there internal staff dedicated to infrastructure maintenance and AI model deployment (MLOps)?
  • Does the budget structure support initial high capital expenditures (CAPEX)?

Real-world example

To prevent leaks of customer consultation records and credit screening data, a major domestic bank deployed a Korean-tuned model based on 'Llama 3' in an air-gapped on-premise environment. This allowed them to automate tasks in a secure environment where customer information entered by employees is never sent to external servers.

Commonly confused terms

Public Cloud

A method of renting resources from service providers (AWS, Azure, etc.) over the internet, offering low initial costs and rapid scaling.

Edge AI

A method of performing immediate inference on devices (CCTV, smartphones, etc.) rather than in a data center, bringing computation even closer to the field than traditional on-premise systems.

Related terms

Air-gapped