Databases serve as the crucial backbone for web applications, ensuring the efficient management and storage of user data and application-specific information. This article explores how feature flags can be used to conduct performance testing between different databases, specifically MariaDB and PostgreSQL, by distributing users between them and analyzing query execution times. A simulated environment is set up using Docker and Python, where sample data is loaded into both databases, and a series of queries is executed to compare performance. The experiment uses the Harness FME platform to implement a feature flag that determines which database a user interacts with, and tracks query execution events. The findings reveal that PostgreSQL resulted in a significant 84% reduction in query time overall, with specific queries involving grouping and limits performing much faster, while others like self-joins and SQL date functions were slower. This approach allows for a granular analysis of database performance, aiding in informed decision-making regarding database engine selection and infrastructure optimization for improved user experience.