Feature flags can significantly enhance logging strategies by allowing dynamic management of log levels and sampling rates, which helps reduce costs and maintain consistency without requiring code redeployments. The approach involves wrapping logging in an internal library for consistent formatting and using feature flags to control sampling rates and log levels at the class level. By employing tools like Split, teams can set up default logging levels with the flexibility to override them, enabling real-time adjustments and more efficient incident response. This method allows for runtime log level management without interrupting program execution and supports dynamic configurations through key-value pairs or custom JSON. Split further aids product development teams by offering a comprehensive feature management and experimentation platform, providing data-driven insights, risk reduction, and improved team visibility, thereby facilitating faster and more confident feature releases.