Owned GPUs or GPU as a Service? Start with the workload
The lowest price per GPU-hour can conceal a poor operational fit. Define what the application must deliver before comparing proposals.
IN ONE MINUTE
- Training and inference can have very different usage profiles.
- Idle capacity, data and support belong in total cost.
- Pilots measure useful performance beyond hardware specifications.
Start with actual usage
Define the model, data volume, memory, latency and availability required. Compare capacity using a useful unit: training time, inferences served or jobs completed.
Consider the complete lifecycle
Include environment preparation, storage, data transfer, networking, orchestration and operations. Owned equipment also requires power, cooling, staffing and a utilization assumption over time.
Test before committing capacity
Run a representative workload, monitor stability and support and compare utilization scenarios. Commercial flexibility matters when demand is still uncertain.
Good infrastructure delivers the workload predictably.
Keep the conversation going
Is your main constraint capacity, cost or predictability?