The adoption of generative AI and Large Language Models (LLMs) is growing, but the costs associated with running these models are causing sticker shock for many organizations. Costs can be driven by factors such as token-based pricing or hosting your own model on infrastructure that requires compute resources like GPUs. Automation strategies can help reduce these expenses and run cost-efficient models. Some tactics include autoscaling using node templates, leveraging spot instances, automating inference, selecting the right LLM model, and deploying the model on ultra-optimized Kubernetes clusters. These strategies can help organizations balance the benefits of generative AI with the costs associated with running these models at scale.