What Is the Difference Between Serverless Inference and Traditional Model Deployment?

The main difference between serverless inference and traditional model deployment lies in how infrastructure and scaling are managed. Traditional deployments rely on pre-provisioned servers or containers, which require constant monitoring, scaling logic, and DevOps resources. In contrast, Serverless Inference abstracts infrastructure management and provisions compute automatically based on demand.

On Vultr, Cloud GPU fits traditional deployments where you need dedicated infrastructure and full control, while Serverless Inference is suited for on-demand inference without infrastructure management.