In the quest for cost-efficient database infrastructure, organizations often fall prey to common mistakes such as lack of experience, suboptimal technology stacks, over-reliance on cloud services, and ignoring infrastructure costs, which can lead to costly surprises down the line. Engineers without high-load/big-data experience may struggle to design scalable systems, while relying too heavily on managed services can hinder long-term cost savings. A suboptimal technology stack can result in higher operational costs as data volumes increase, whereas self-serve solutions paired with DevOps expertise can offer faster data processing speeds and substantial cost savings. Relying on cloud services for heavy lifting can be beneficial at first but may become expensive when scaling, while ignoring infrastructure costs can lead to a "free-money trap" where startups underestimate the true cost of their operations as they grow. By understanding these pitfalls and taking a proactive approach to database architecture design, organizations can avoid costly mistakes and ensure a more efficient and scalable infrastructure.