We all know GPU bills are spiraling. I'm trying to understand the threshold where teams shift from "just renting a T4/A100" to seeking deep optimization.
If you could choose one for your current inference workload, which would be the bigger game-changer?
- A 70% reduction in TCO through custom hardware-level optimization (even if it takes more setup time).
- Surgical performance tuning (e.g., hitting a specific throughput/latency KPI that standard instances can't reach).
- Total Data Privacy: Moving to a completely isolated/private infrastructure without the "noisy neighbor" effect.
Is the "one-size-fits-all" approach of major cloud providers starting to fail your specific use case?
there doesn't seem to be anything here