Build on Badgr / Use case
GPU AI Application
Add GPU execution — generate, train, process, render, run inference — to your product without operating a GPU fleet.
How it works
1.Your customer starts a GPU-backed action in your product (generate, train, process, render, run inference).
2.Your backend submits the workload and its limits to Badgr as a GPU Run job.
3.Badgr finds compatible GPU capacity and provisions execution.
4.Badgr runs the workload, monitors execution, recovers where permitted, preserves required artifacts, verifies the required result, and confirms teardown.
5.Your product receives the outputs and displays them under its own branding.
Badgr owns
- GPU capacity matching and provisioning
- Execution monitoring and recovery
- Cost cap, runtime cap, cancel
- Artifact preservation and result verification
- Teardown and billing settlement
You own
- Which action your customer triggers
- Script/command and GPU budget
- Your product UI and branding
- How outputs are stored and shown