Apache Kafka is a powerful tool for moving large amounts of data in a fast and reliable manner, but optimizing its performance and reducing cloud costs can be challenging due to its complexity. To overcome this, it's essential to understand the costs involved and identify areas where improvements can be made. Monitoring Apache Kafka's performance using tools like New Relic is crucial to optimize network, storage, server hardware, and client configurations. By implementing a showbacks/chargebacks strategy, teams can ensure that users are using Kafka resources efficiently, reducing cloud costs in the long run. The key to success lies in understanding how different components of Kafka interact and affect each other, and leveraging monitoring data to make informed decisions about cluster configuration, application performance, and resource allocation.