Make shared data spend
allocable.
Trace warehouse and pipeline cost to the query, the pipeline, and the team that ran it, including the data spend your AI workloads now drive.
One unpruned query, times seventy-seven a month.
Warehouse spend is unpredictable and rarely maps to a team or product. A single unpruned query scans hundreds of gigabytes; run on a schedule, it compounds.
And now AI workloads are reading from your warehouses at scale, on budgets that were never sized for them, often from AI-assisted code with no obvious owner. DataX finds these automatically, with attribution down to the SQL.
What unattributed data spend costs you.
- Data platform spend that surprises you month to month.
- Warehouse and pipeline cost you can't cleanly attribute to a team or product.
- A rising share of the bill driven by AI workloads reading from warehouses.
- Queries from AI-assisted code showing up with no owner.
- No unit economics for data: cost per pipeline, per query, per dataset.
What DataX does for data teams.
Query attribution
Every query tied to a user, role, dashboard, dbt model, or job.
Pattern detection
Cost-amplifying patterns caught, with a rewrite suggested.
Predictive signals
Unit-cost regressions surfaced before monthly close.
Safe automation
Partition, cluster, and right-size fixes, reversible and audited.
How DataX controls
every warehouse dollar.
Read the docsDataX sits alongside your warehouses read-only, attributes every query's cost to an owner through the dbt DAG, and applies the fix once you approve it.
A cost explorer shows the spend. DataX shows who caused it, and what to do next.
Trigger when a new deal record is created.
Enrich the record with web research.
Score the lead and route to the right AE.
Route lead to Enterprise and draft outreach.
Route lead to SMB and draft outreach.
From invisible spend to accountable architecture.
A Southeast Asian digital and telecommunications group ran 129 applications across four clouds with no reliable owner. CloudVerse mapped spend to how the business works and surfaced Rp964.80M in savings before optimization began.
surfaced before optimization
applications mapped
Outcomes you can defend.
- Attribution. warehouse and pipeline cost traced to the query that committed it.
- Pattern detection. full scans and runaway queries caught before they compound.
- AI visibility. AI-driven warehouse traffic attributed cleanly.
- Engineering visibility. data cost committed by code surfaced before the bill.
- Predictive signals. unit-cost regressions before close, not in the post-mortem.
- Defendability. data economics that hold up in front of finance and the board.
The people who answer for the data bill.
Warehouse spend finally maps to the team and product that drove it, not a shared line item.
Full scans and runaway queries get caught before they compound, not at monthly close.
Dashboard and query cost traced back to the report that's actually driving the bill.
Attribution runs through the dbt DAG automatically, no manual tagging to maintain.
Data team questions, answered.
What data and analytics leads ask first.