Implement auto-scaling to dynamically adjust the number of executors based on the workload, and set up alerts for high latency thresholds without any additional cost monitoring tools.

Use Databricks' built-in cost and latency monitoring tools to track performance and costs, requiring manual intervention to adjust the cluster size and configuration as workload changes.

Design a system with a fixed number of executors and a static cluster configuration, relying solely on manual monitoring to ensure SLAs are met, ignoring the potential for cost savings through scalability.

Utilize Databricks' autoscaling feature to dynamically adjust resources based on workload, implement a budget alert to monitor costs, and deploy a custom monitoring solution to track latency in real-time, triggering alerts when predefined thresholds are exceeded.