Kubernetes costs can be broken down into three main categories: compute costs, storage and network costs, and operational overheads. Compute costs include the expenses associated with the resources needed to run applications, such as CPU and memory. Storage and network costs arise from persistent volumes that provide long-term storage and I/O operations, which are charged based on capacity usage and input/output operations. Operational overhead includes costs for monitoring, logging, and running CI/CD pipelines, which can be reduced by implementing best practices such as right-sizing and autoscaling strategies. Frequent updates in Kubernetes can lead to increased resource usage during deployments and system instability if not managed properly. Factors influencing Kubernetes costs include resource misallocation, frequent updates and short release cycles, and dynamic workloads. By monitoring key metrics and following best practices such as optimizing resource requests and limits, streamlining logging and monitoring, and leveraging API development platforms like Blackbird, businesses can achieve Kubernetes cost optimization while ensuring high performance and reliability.