On-Premise
On-Premises
What is on-premise?
- Built and run on your own servers
- More control, higher upfront cost; the counterpart to cloud
- Making a comeback for private AI
Choosing on-premise is not a decision to buy equipment. It is a decision to take on accountability and headcount. In the cloud, when something fails you open the vendor console and file a ticket; on-premise, the person unlocking the server room at 2 a.m. is your own employee. Budgeting differs too. Cloud is booked as a monthly operating expense that business units can scale up as easily as swiping a card, whereas on-premise is a capital asset that has cleared investment review, so you have to plan to use it for its entire four-to-five-year depreciation period once you buy it.
In practice, on-premise and cloud are not an either/or choice. There are several steps in between: self-hosting in your own server room, colocation (placing your own hardware in a carrier's data center), and private cloud (renting dedicated hardware outright). When comparing costs, equipment price alone gives a misleading picture. You need to look at total cost of ownership (TCO) over three to five years, including power, cooling, rack space fees, software licenses, and staffing. As a rule, workloads with consistently high utilization favor on-premise, while spiky, event-driven traffic favors the cloud. Note that on-premise describes location (inside your own building), while network separation is a security measure that cuts off networks. They sit at different layers.
The most common misconception is the belief that 'keeping it in-house makes it safe'. A system is safe not because the data sits on site, but because someone patches it consistently and verifies the backups. The moment one overloaded administrator puts off security updates for six months, it becomes far more vulnerable than an external cloud. Many ransomware incidents start on neglected in-house servers. The same goes for generative AI: buy GPU servers without assigning anyone to handle model updates and operations, and hardware worth hundreds of millions of won sits idle within six months.
Seolyeong ParkGenerative AI and AX training expert with 17 years of hands-on experience and 200+ sessions, making AI usable right away even for non-specialists
Seungtae ParkPractical AX instructor who teaches by building AI business development and marketing automation hands-onThe term comes from installing software 'on the premises.' Before cloud computing spread, this was simply the norm and needed no special name. As cloud and SaaS became commonplace from the mid-2000s, it became the standard label for the traditional self-hosted approach.
Consider a hypothetical case. Company A, a precision-parts manufacturer with 1,200 employees and annual revenue of about KRW 400 (≈ US$1) billion, wanted to add generative AI to its design-drawing search but had been stuck for six months without a decision. The business units said, "Why not just use ChatGPT?" but the security team objected because client drawings could leak.
- The head of information security reviewed the 18 TB internal document repository by sensitivity grade and drew a line: "The 3.2 TB of design drawings and cost sheets can never leave the building." Everything else was classified as safe to move outside.
- When the legal director re-read the NDAs with three key clients, two contained clauses explicitly prohibiting storage of drawing data on overseas-region servers.
- An IT infrastructure team compared the purchase cost of four GPU servers (KRW 380 million (≈ US$284,300)) against a cloud API (KRW 120 million (≈ US$89,800) a year) over five years. Once they added power, rack space fees, and 1.5 full-time operators, the gap between the two options narrowed to about KRW 80 million (≈ US$59,900).
- The CTO settled on "drawings stay inside, everything else goes outside": the design-search AI runs on an open-source model on in-house GPU servers, while marketing copy and translation use commercial cloud APIs as they are.
- They assigned two dedicated infrastructure operators, fixed the third Wednesday of every month as Patch Day, and wrote into policy that backup recovery must be tested for real once a quarter.
- Six months after rollout, the time the design team spent searching past drawings fell from an average of 40 minutes per search to 7, uncontrolled use of external AI services dropped from about 300 cases a month to zero, and there were no security incidents.