Comparison of serverless inference vs traditional model deployment approaches, highlighting infrastructure management and scaling differences
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.
| Aspect | Traditional Model Deployment | Serverless Inference |
|---|---|---|
| Infrastructure | Requires pre-allocated GPU servers or containerized environments. | No pre-provisioned infrastructure. Compute is provisioned on demand. |
| Scaling | Manual or auto-scaling rules must be configured. | Automatically scales up or down based on traffic or usage. |
| Operational Overhead | High. Requires monitoring, patching, and capacity plans. | Low. Provider manages infrastructure, scaling, and availability. |
| Resource Utilization | Often underutilized due to over-provisioning for peak loads. | Optimized usage. Resources are active only during inference execution. |
| Cost Model | Pay for allocated infrastructure, even when idle. | Pay only for actual inference executions (event-driven). |
| Deployment Complexity | Needs DevOps pipelines, load balancing, and capacity plans. | Simple API-driven deployment with minimal setup. |
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.