The gap between planning and implementation of generative AI (GenAI) solutions is a significant challenge that many companies face. To overcome this, it's essential to create a solid foundation by laying down a comprehensive AI stack. This includes infrastructure for creating both traditional and GenAI applications, training, fine-tuning, and providing context to ML pipelines or AI models. A well-designed AI stack should include essential components such as high-quality data, large language models (LLMs), parametric memory, non-parametric memory, agents, tools for prototyping and productizing AI apps, and monitoring and observability systems. By identifying and commoditizing the common components of most AI app solutions, developers can save time and resources, enabling them to build more complex and accurate GenAI applications.