To achieve optimal performance with MongoDB, it's essential to model your benchmark using representative data, queries, and deployment environment. This includes considering multiple parallel threads for sharded clusters and bulk writes to reduce network overhead. Pre-splitting chunks before loading data can also improve performance by allowing documents to be loaded in parallel into appropriate shards. Additionally, designing the data load such that different shard key values are inserted in parallel can help distribute writes across multiple shards. Other considerations include disabling the balancer during bulk loads, priming the system with representative queries for several minutes, using connection pools and configuring ulimits, and monitoring everything to locate bottlenecks. MongoDB Atlas provides features like charts, custom dashboards, automated alerting, and visualized metrics to help identify performance issues and optimize database cluster health.