Load testing is essential for determining the capacity of a current cluster and planning necessary adaptations to accommodate user growth without compromising service levels. It involves a series of tests considering various factors such as access patterns, data structures, and network configurations, applicable to both self-hosted and cloud environments. For cloud-hosted Couchbase clusters, the Kubernetes Autonomous Operator simplifies configuration management. Before testing, clearly defined SLAs are crucial for evaluating performance, ensuring that processes meet required time frames. Testing involves using mock objects to isolate database interactions and calculate time allocations for these interactions, followed by gradual load increases to identify performance degradation points. This process helps identify which services or nodes might need scaling and provides actionable information for cluster configuration. Continual testing under various configurations, including during operations like rebalancing, aids in planning for growth by establishing milepost triggers to signal when scaling is necessary. This approach ensures that cluster scaling is timely and efficient, accommodating both predictable and organic growth patterns.