What on-premise means: a complete guide to using AI without sending documents outside

On-premise deployment means installing and operating the model and system on servers the customer manages directly, rather than an external provider's cloud, so that the data being processed never crosses the organization's boundary.

Why on-premise becomes a requirement

Some organizations cannot send the document out at all.

With cloud services now the default, most software discussions begin with which service to use. In finance and the public sector, a different question comes first: can this document leave the building? A credit application carries an individual's income and account details. A relief claim carries a victim's identity and the substance of a criminal case. Sending those files to an external server runs into both internal policy and supervisory regulation.

For these organizations, on-premise is not one option among several but a gate that has to be passed. However strong the performance, a solution that cannot be installed internally drops off the list first. That is why the opening step in an evaluation is confirming the deployment model rather than assessing the technology.

What on-premise actually is: how it differs from cloud

Cloud and on-premise across three axes

First, the scope differs. In a cloud arrangement, the customer manages the request and response contract and nothing else. On-premise brings servers, accelerators, the container environment, storage, and access control into the customer's scope. There is more to manage, and correspondingly more that can be controlled.

Second, the objective differs. The cloud objective is to connect quickly and start using it. The on-premise objective is to sustain production volume without data leaving the organization. One prioritizes speed of adoption; the other prioritizes control.

Third, the metrics differ. Cloud is judged on response time and throughput limits. On-premise adds installation in an air-gapped network, required accelerator specification, the model update procedure, audit logging requirements, and incident response. In practice, inference and document processing run entirely inside the internal network, with permission management, processing history, and separation between production and review environments provided alongside.

The two are not alternatives. Validating in the cloud and moving to an internal installation for production is a common path. What matters at evaluation time is therefore not which model you use today but whether the same engine is offered both ways.

Five requirements to confirm before deciding

Five requirements that hold in a real build

First, installation has to complete inside an air-gapped network. If the installer reaches out to an external repository to pull components, the work stops where the internet is cut. Confirm that the required images and model weights can be transferred on physical media and installed from there.

Second, the accelerator specification has to be explicit. Vision language model processing is computationally heavy, and practical throughput is not achievable without accelerators. Size the requirement against monthly volume and peak-hour concentration, and confirm the path for expansion at the same time.

Third, the model update procedure has to be controllable. External models are updated at the provider's discretion, and performance changes without notice. An internal installation should validate a new version, promote it through an approval step, and allow a rollback to the previous version when needed.

Fourth, audit requirements have to be met. Who processed which document, when, and which values were corrected all need to exist as records. Role-based access control and retained processing history come up repeatedly in financial and public-sector audits.

Fifth, the operational burden has to be counted. An internal installation means the customer carries the servers and container environment as well. Settle before contract whether monitoring tools, incident alerting, and a regular inspection procedure are included, and how far the vendor's remote support extends.

How on-premise is applied in practice

Separate the deployment into layers

Design begins by splitting functions into layers: an application layer that receives documents, a model layer that performs recognition and structuring, a data layer that holds results and originals, and an integration layer that connects to business systems. Keeping these distinct makes later expansion and replacement straightforward.

In practice each layer is deployed on containers, with accelerator resources allocated to the model layer alone. A relational database holds processing history and result metadata, original files sit in object storage, and a job queue and cache run separately. That shape is widely used.

Keep the review environment separate from production

The benefit of an internal installation is not only that data stays inside. Being able to hold a review environment apart from production matters just as much day to day. When a new form arrives, the extraction criteria can be tried there first, and only after the result is confirmed does it move to production through an approval step.

With that structure in place, the risk of a change causing an incident in production drops. Without it, every added form leaves room for production performance to wobble.

On-premise in the Korean environment

The most direct source of on-premise requirements in Korea is network separation. Where the business network and the internet are separated, outbound API calls are blocked outright, which leaves internal installation as the only option.

In finance, the configuration has to align with the electronic financial supervision regulations and the Financial Security Institute's guidance. In the public sector, each agency's security policy and personal data handling standards apply on top. Both share one condition: cross-border transfer of personal data is effectively impossible.

One further consideration is the domestic document format. Converting Korean word processor files inside an air-gapped network requires the relevant processing components to be installed internally as well. Miss this and the installation completes while the actual intake documents still cannot be opened.

Frequently asked questions