Most data platforms we audit run 30–60% over what they need to. Move the sliders to estimate your own savings range — based on the patterns we see across real Azure & Databricks engagements.
Rough numbers are fine — this is an estimate, not an invoice.
Roughly 36% of your current spend looks recoverable through right-sizing, autoscaling, and Delta optimization.
Indicative estimate based on typical engagement outcomes. Actual savings depend on your architecture and workloads.
Always-on, oversized clusters are the single biggest source of waste. Matching compute to actual demand often reclaims a third of the bill on its own.
Proper partitioning, file compaction, Z-ordering, and caching mean the same workloads finish faster on less compute.
Incremental loads instead of full reloads, plus smarter scheduling, cut both runtime and the compute-hours you pay for.
Book a free 30-minute consultation and we'll pressure-test the estimate against your real setup — and tell you what's worth doing first.
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