Enterprises often begin their AI journey through public APIs, paying per "token" generated. But as AI moves into core production, this pay-as-you-go SaaS model creates runaway, unpredictable costs and latency bottlenecks. This highly practical panel tackles the critical pivot to "detokenization." It provides a blueprint for how enterprises can step away from variable token pricing by leveraging a fully sovereign, Canadian GPU-as-a-Service (GPUaaS) model. We will deconstruct how hosting models privately on dedicated, high-performance compute infrastructure caps enterprise costs while maximizing data sovereignty, latency performance, and operational control.
