As organizations scale generative AI (gen AI) across business units, a familiar tension appears—bigger models can often deliver better results, but they also require significantly more compute, cost, and operational complexity. This creates a production paradox— while enterprises want higher-quality reasoning, domain specialization, and agentic autonomy, they struggle to deploy monolithic trillion-parameter models that run continuously across clusters.As a result, the industry is shifting strategies, moving from single, massive models toward more efficient architectures. One of those techn