When does it make sense to run AI models on-premise versus in the cloud?

Running AI models on-premise or at the edge makes sense when enterprises need to process data close to its source for real-time applications, such as in factories, ships, or stores, where distributed infrastructure provides strategic flexibility and reduces latency compared to centralized cloud setups [1][2]. This approach is particularly advantageous as AI outgrows traditional data centers, enabling multi-agent and multi-model environments without over-reliance on cloud providers, and can lower ongoing costs by leveraging local hardware like PCs for free, offline execution after initial setup [12]. However, on-premise may face challenges with power and infrastructure demands, as AI's real bottleneck is energy rather than just compute [7][8]. In contrast, the cloud is preferable for production-scale AI, offering the right kind of compute for inference—especially when existing on-premise systems lack suitable hardware—and enabling secure, affordable operations through cloud-native ecosystems [3][10][11]. High compute and OpEx costs dominate AI expenses, making cloud viable for scaled operations where companies balance innovation with profitability, though skyrocketing infrastructure needs highlight the need for economic sustainability [4][9].