Reducing Databricks Costs by 40%: A Practical Guide

Cost optimization in Databricks requires a multi-faceted approach. Here’s how we achieved 40% cost reduction in production.

Cluster Configuration

Right-Sizing Workers

Don’t over-provision. Use these guidelines:

• Memory-intensive jobs: Memory-optimized instances
• CPU-intensive jobs: Compute-optimized instances
• Balanced workloads: General-purpose instances

Autoscaling Configuration

cluster_config = {
    "autoscale": {
        "min_workers": 2,
        "max_workers": 10
    },
    "autotermination_minutes": 15
}

Spot Instances

Spot instances can reduce compute costs by 60-90%:

1. Use for fault-tolerant workloads
2. Mix spot and on-demand for critical jobs
3. Set appropriate max price

Delta Lake Optimization

Optimized Delta tables = fewer scans = lower costs:

• Enable auto-optimize
• Regular VACUUM operations
• Partition pruning

Monitoring and Alerting

Set up cost monitoring:

• Daily spend alerts
• Job-level cost attribution
• Cluster utilization dashboards

Results

Our optimization strategy:

• 40% reduction in monthly costs
• Maintained SLA performance
• Improved query response times