Maximizing Elasticsearch performance when adding nodes to a cluster involves understanding the impact on key hardware resources such as compute, storage, memory, and network bandwidth. Adding nodes can enhance a cluster's capacity to handle larger workloads and improve request handling, but it can also introduce performance bottlenecks if not planned carefully. Common bottlenecks occur in compute and storage resources, especially when nodes share hardware on virtual machines or containers. To mitigate these issues, it is crucial to configure shard allocation properly and monitor resource utilization, particularly CPU and storage throughput. Effective capacity planning, including choosing an appropriate shard count, can help ensure that adding nodes improves performance rather than causing instability. Whether using dedicated hardware or virtualized environments, careful resource allocation and monitoring are essential for optimizing Elasticsearch's scalability and efficiency.