Generative AI projects often falter due to companies attempting to build their own infrastructure, resulting in wasted resources and delayed innovation. While DIY approaches promise control over models, GPUs, and costs, they often lead to engineering teams being bogged down with maintenance rather than innovation due to challenges like GPU scarcity, monitoring complexities, scaling issues, and compliance headaches. Real-world examples, such as Convirza's switch to a managed platform solution, demonstrate significant cost reductions and performance improvements, highlighting that managed platforms can offer a more efficient and cost-effective path for deploying AI projects. DIY infrastructure can incur substantial hidden costs, including high salaries for ML engineers and expenses related to downtime and crashes, whereas managed solutions provide predictable pricing and allow teams to focus on enhancing model quality and building innovative applications. Ultimately, unless a company's core product is GenAI infrastructure itself, managed platforms are often more beneficial, offering better ROI, reduced total cost of ownership, and accelerated product development.