Amazon SageMaker Studio App
Does ZopNight manage Amazon SageMaker Studio App?
SageMaker Studio apps bill per instance-hour while running: each JupyterLab, Code Editor, or kernel session is its own metered instance, and closing the browser tab does not stop it. ZopNight discovers apps on the 6-hour cycle, attributes per-app cost from Cost Explorer or CUR 2.0, and flags apps left running after sessions end.
Rules that fire on Amazon SageMaker Studio App
No active rule family targets Amazon SageMaker Studio App today. Rules that used to are retired, and retired rules publish no pages and fire no findings. Scheduling and permissions coverage are unaffected.
At a glance
| Field | Value |
|---|---|
| Scheduling notes | discovery, cost tracking, and recommendations only. |
A SageMaker Studio app is a running application instance (JupyterLab, Code Editor, kernel gateway) inside Studio, billed per instance-hour while running. Studio apps left running after a work session keep billing exactly like forgotten notebooks.
Sessions that outlive their tabs
Studio’s architecture multiplies meters in a way classic notebooks never did. Each app (the JupyterLab server, each Code Editor instance, each kernel gateway backing a notebook’s compute) runs on its own instance and bills per instance-hour independently. One data scientist’s afternoon can leave three or four apps running: the JupyterLab app on one instance type, kernels on a GPU type chosen for an experiment, a Code Editor from a side task. Closing the browser ends none of them. The meters keep running in the space, invisible unless someone looks at the app list rather than the screen.
App-level cost accounting
ZopNight discovers Studio apps automatically on the 6-hour cycle and attributes per-app cost from Cost Explorer or CUR 2.0, with idle-app recommendations for the ones left running after their sessions ended. The per-app granularity is the point: Studio spend otherwise appears as an undifferentiated SageMaker charge, while the actual driver is a specific kernel gateway on a specific accelerated instance that a specific user forgot on a Friday. Naming the app and its instance type turns an amorphous bill into a fixable habit.
The kernel graveyard pattern
Studio’s convenience produces a distinctive leak: kernels accumulate. A user switches instance types for one cell’s workload, the old kernel app stays up; a shared space hosts apps from users who changed teams; auto-shutdown extensions exist but depend on being installed and configured per environment. GPU kernel apps are the costly tail: started for an experiment, orphaned at standup.
Auditing running apps
The SageMaker console’s Studio section lists domains, user profiles, and their running apps with instance types and creation times. Filtering for running apps with creation timestamps days in the past, especially on accelerated instance types, is the entire audit, and deleting an idle app loses nothing that a space’s storage does not preserve.