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Databricks Consulting → Cost Optimization

Databricks Cost Optimization

Is your Databricks bill too high? We find the workloads, clusters, and storage patterns driving unnecessary spend — and fix them with measurable, reported savings.

Common causes

Where Databricks spend usually leaks

High DBU consumption

Oversized clusters

Poor autoscaling configuration

Idle clusters

Inefficient SQL burning compute

Excessive data scans

Repeated, redundant processing

Poor job scheduling

Inefficient storage

Non-optimal compute selection

Assessment

How we find the savings

01

Usage Analysis

Break down DBU and cloud spend by workspace, job, and team.

02

Workload Analysis

Identify which jobs and queries account for the bulk of consumption.

03

Compute Analysis

Review cluster sizing, autoscaling policy, and instance selection.

04

Storage Analysis

Review file layout, retention, and redundant storage.

05

Optimization

Implement right-sizing, scheduling, and configuration changes.

06

Savings Measurement

Report before/after spend so savings are measurable, not anecdotal.

Get started

Find your Databricks cost-saving opportunities