Cut database cost with evidence, not guesses
Estates accumulate: tables nobody reads, jobs feeding dashboards nobody opens, resources sized for a load that moved on. Datapace puts usage and freshness evidence on the same validated map as meaning, so dead weight stops being anecdotal: you see what is alive, what has been untouched and for how long, and what it costs to keep. Cuts follow the workload evidence, demand-side first (dead tables reveal dead jobs, dead jobs free oversized resources), and because lineage is on the map, removing something is a reviewed decision, not a gamble.
How cost evidence flows
From workload evidence to a smaller bill.
Usage evidence
Usage and freshness, measured
Reads, writes, and last-touched evidence per table, next to the meaning, so cold data is visible instead of suspected.
Dead weight surfaced with its footprint
Tables and pipelines untouched for months are flagged with their size, and their downstream dependencies checked against lineage.
Demand-side first
Workload evidence grounds cleanup and resizing in that order: dead tables reveal dead jobs, dead jobs free oversized resources, before any supply-side tuning.
Decisions your experts own
Every removal or resize is reviewed on validated context, with lineage showing exactly what depends on what before it happens.
Related reading
Related use cases
See this on your own data, safely
Bring a use case. We will show you what Datapace resolves on your live database and how the policy gate governs what AI may do.