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Tenants

Create tenants, edit names, KPI targets and feature toggles, deactivate, delete permanently and follow deletion progress

Required permission: Global admin

A tenant is an isolated workspace used by one team (or one customer). Datasets, experiments, models and deployments are all separated per tenant, and each tenant gets one Kubernetes namespace and one MLflow workspace. You create, change, suspend and delete tenants only on this screen.

Tenant list

Each row shows the tenant code and name, a status badge, and links to the next setup steps (Users (사용자), Shared images (공유 이미지), GPU wiring (GPU 배선)). If any kind–framework combination has no default image, the row gets an "N defaults missing (기본 누락 N개)" badge; pressing it takes you to Shared images. Training submissions and builds for that combination are rejected.

Create a tenant

  1. Press Create tenant (테넌트 생성) at the top right.
  2. Enter the Code (코드) and Name (이름). The code may contain only letters, digits and hyphens between them, is 2–16 characters long, and is stored in upper case. MLOPS is reserved for the shared image namespace and cannot be used. As you type, the names derived from the code (namespace tenant-acme, MLflow workspace acme) appear right below.
  3. Under Features (기능), turn off the modules this tenant will not use (all are on by default).
  4. ① Under Default shared images (기본 공유 이미지), choose the defaults for the training runtime (one per framework), the dataset stager and the serving builder. Usually you keep the preselected values.
    Create tenant — code, name, features, ① default shared images
  5. Press Create (생성). Press Assign first admin (첫 관리자 지정) in the completion notice to assign the tenant's first ADMIN on the Users screen.

Change the name, KPI targets and features

  1. Press Edit (편집) on the row.
  2. Change the name, the KPI targets (KPI 목표) and the Features (기능) switches, then press Save (저장).
    Edit tenant — name, KPI targets, feature toggles
  • KPI targets: the dashboard's KPI cards compare against these values and flag shortfalls. If left empty, the card shows "Target not set (목표 미설정)". Enter % items as a value from 0 to 100.
  • Features: turning one off removes that module from the menus of the tenant's members, and the server also blocks the related API. Global administrators pass the feature toggles on the server.

Deactivate — suspend for a while

Deactivation only cuts off people's access and leaves the tenant's data as it is. Members of an inactive tenant cannot enter its workspace, and the tenant disappears from their tenant selector. They cannot reach it through MLflow, the container registry or MLflow tokens either. Global administrators can still enter. Training and serving that are already running are not stopped, and edge devices keep sending data. Undo it with Activate (활성화), which has no confirmation dialog.

  1. Press Deactivate (비활성화) on the row.
  2. Press Deactivate (비활성화) once more in the confirmation dialog.
    Confirm tenant deactivation — it can be reactivated later

Delete permanently

Deletion removes the tenant and every resource in it. It cannot be undone; to only suspend the tenant, use deactivation.

  1. Press Delete (삭제) on the row. The dialog shows the resources to be deleted (삭제될 리소스) as reported by the server: serving endpoints, running PODs, datasets and their size, collected data and its size, training runs, MLflow experiments/runs, registered models, the namespace and the MLflow workspace. Items at 0 are still shown so you can confirm there is "none". If the counts cannot be loaded, you can still delete; the progress screen shows the real result of each step.
  2. The Delete permanently (영구 삭제) button unlocks only after you type the tenant code (case-insensitive) in the confirmation field.
    Delete tenant — lists what will be removed and requires typing the code
  3. Pressing Delete permanently (영구 삭제) takes you to the deletion progress screen. Cleanup can take a few minutes; you can close the window.

The deletion progress screen runs the steps below in order and shows the result and log of each step.

  1. Stop serving endpoints
  2. Stop training processes and delete working directories
  3. Stop pipeline Jobs
  4. Delete the Kubernetes namespace
  5. Delete the MLflow workspace
  6. Delete object storage
  7. Delete database records

Every deletion that did not finish cleanly (in progress, partially failed, interrupted), plus the 5 most recent clean ones, stays in the In progress · recent deletions (진행 중·최근 삭제) panel above the tenant list, and you reopen them with View progress (진행 보기) / View result (결과 보기). On the row of a tenant being deleted, the edit, deactivate and delete buttons are locked.

Written for the platform as of 2026-10-02.

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