Bento's article highlights the increasing trend of enterprises moving Large Language Model (LLM) workloads to on-premises environments due to data privacy, performance consistency, and cost efficiency. However, the complexities of setting up an on-prem LLM stack extend beyond initial hardware investments, emphasizing the need for a robust inference platform layer to handle tasks like workload scaling, GPU utilization, and production reliability. The article identifies key challenges such as slow time to market, poor cost visibility, performance bottlenecks, and observability issues, which can hinder an organization's ability to leverage LLMs effectively. Bento On-Prem is presented as a solution to these challenges, offering a platform that integrates seamlessly with existing infrastructure to provide standardized workflows, fast autoscaling, distributed serving, and inference-specific observability, ultimately enabling AI teams to efficiently manage and optimize LLM deployments.