In this podcast, Shareedhar Gade discusses the complexities of scaling Generative AI (GenAI) for production, focusing on capacity planning, continuous integration, and cost optimization. During the bootstrapping phase, businesses identify use cases and determine which hyper skills to engage with; however, in production, they must focus on continuous scaling, monetization, and staying competitive by adapting to new models. Sreedhar addresses the challenges of implementing Continuous Integration, Continuous Delivery (CI/CD), and Continuous Training for GenAI models, emphasizing the need for custom solutions to handle functionality and performance issues. In this podcast, Sreedhar also discusses strategies for optimizing large language model usage, such as adjusting token usage based on modelling success and categorizing use cases for specialized models. The section identifies capacity planning, model performance, and cost as the major challenges in scaling GenAI for production. To address these challenges, Sreedhar suggests strategies like switching between models, dual deployment methods, and iterative model pipelines. Sreedhyar Gade recommends monitoring various metrics, such as several tokens, cost, performance, system governance, acceptance rate, and time taken for model responses to effectively manage and deploy public and private models together. They will continue discussing scaling and governance in the next episode.