What is a key feature of pre-labeling in terms of resource usage?

The key feature of pre-labeling in terms of resource usage is that it does not consume AI units if the model is local. This is significant because when using an AI model that is hosted locally, the processing and labeling can be performed without the need to access external AI resources, which can significantly cut down on costs tied to AI unit consumption associated with cloud-based services.

By pre-labeling data locally, users can efficiently prepare datasets for training or validation without incurring additional AI unit expenses, allowing for increased flexibility and cost management in resources. This feature makes local models particularly advantageous for organizations seeking to optimize their AI usage and budget during the data preparation phase.

In contrast, the consumption of AI units typically becomes a concern with cloud-based models where every operation involving AI processing can incur charges, making the local option a more economically sensible choice for pre-labeling tasks.