Manually review each Databricks cluster and notebook usage, making ad-hoc adjustments to cluster sizes and instance types based on workload requirements without a systematic analysis.

Utilize the Databricks Cost Management UI to systematically analyze usage patterns, identify underutilized clusters and idle resources, and optimize cluster configurations and auto-scaling settings based on data-driven insights.

Develop and implement a custom monitoring solution to track Databricks clusters and notebooks usage, then use this data to manually adjust cluster sizing, instance types, and auto-scaling settings.

Adopt a third-party cost optimization tool to analyze usage patterns and identify savings, relying solely on the tool's recommendations for implementing changes without further validation.