Machine learning (ML) and artificial intelligence (AI) platforms are essential for developing, deploying, and managing ML models and AI services, prompting organizations to decide whether to build or buy such platforms. Building an in-house platform offers customization, integration with existing systems, and alignment with business needs, although it can be resource-intensive and complex. On the other hand, purchasing a platform can expedite time-to-market and reduce development efforts but may involve vendor lock-in and limited customization. Most organizations opt for a hybrid approach, combining third-party components with custom solutions to balance flexibility, cost, and time-to-market. Open-source software often plays a role in these platforms, offering cost advantages but requiring integration and maintenance. A thorough evaluation of factors like technical expertise, costs, compliance, and vendor reputation is crucial for making an informed decision. The author recommends starting small and scaling up as needed, focusing on core features to quickly deliver value and adapt to evolving organizational requirements and the MLOps landscape.