AI Apps Should Define Workloads, Not Pick GPUs, Argue Developers

A growing concern among AI developers highlights how building GPU-backed inference features often devolves into complex infrastructure orchestration code. When applications directly select specific GPU instances, they inherit dozens of implicit assumptions around availability, provider APIs, driver compatibility, and billing models. This tight coupling leads to provider lock-in embedded not just in contracts but in the application code itself, making it costly to switch providers or regions. Developers argue that AI applications should instead declare workload requirements—such as memory, latency, and cost constraints—and let a dedicated infrastructure layer decide how to fulfill them. This separation of concerns would allow applications to remain focused on business logic while infrastructure systems handle capacity, provisioning, and fallback decisions dynamically.