Inside the Cost Savings & Data Cleanup Agent
You're Paying to Store Data Nobody Has Queried in Months.
Cost dashboards tell you the warehouse is expensive. Meet the agent that finds the waste, checks it's safe to remove, and reclaims it.
Nobody can say where the money went
Every few weeks r/dataengineering produces another bill-shock thread: a $22,000 invoice from just firing some queries, a $50,000 BigQuery surprise after a weekend of testing, a Snowflake bill that nearly got me fired. The numbers differ; the story is the same. The warehouse is wonderful right up until the invoice arrives - and then nobody can say exactly where the money went.
So a human starts the audit: query the information schema, cross-reference the query logs, check which dashboards still depend on which tables, build a spreadsheet - and by the time it's done, it's already stale, while the waste keeps compounding. The painful truth across most warehouses is that a large share of the bill isn't the work you're doing at all - it's idle compute billing around the clock and stale tables nobody has touched in months, quietly racking up storage.
What our Cost Savings & Data Cleanup Agent actually does
The Cost Savings & Data Cleanup Agent treats your bill as something to act on, not just a chart to read.
It continuously analyzes spend and shows you exactly where the budget goes - by table, by query, by team, by pipeline - in minutes, not a three-week manual audit. It finds the waste: the stale tables nobody has queried in months, the duplicate copies and redundant materializations you're paying for twice, and the oversized or idle compute quietly billing around the clock. When a bill spikes, it attributes the jump to the exact query, team, or job that caused it - and proposes a concrete fix. And it cleans up safely: every archival candidate is dependency-checked against downstream consumers and sorted into safe, review, and risky tiers, so reclaiming storage never breaks a dashboard. The hygiene runs continuously, so waste stops re-accumulating the week after a one-time cleanup.
The shape of the win is a bill you actually understand. The agent is built to reclaim the slice that's pure waste - the stale tables, redundant copies, and idle compute that across a typical stack run roughly 30 to 40% of warehouse spend - targeting a 25 to 40% reduction in spend and around 30% less storage. Those are design targets we're engineering toward, not averages we've billed; what you actually recover depends on how much waste you're carrying today.
Here's the reframe: cost optimization isn't a dashboard you read - it's waste you remove. The chart was never the hard part. And the agent doesn't work alone: the usage-intelligence agent tells it what's actually being used, the catalog agent supplies the lineage that makes archival safe, and the governance agent keeps every action inside policy. Cost control sits inside a team that shares one picture of your stack - not a console that hands you a worklist nobody has time to work.
A few of the agent's capabilities
The Cost Savings & Data Cleanup Agent ships with a deep toolkit. A sampling of what it can do:
| Capability | What it does |
|---|---|
| Unused-data detection | Surfaces the tables and views nobody has queried in months. |
| Spend attribution | Breaks the bill down by table, query, team, and pipeline. |
| Cost-spike root cause | Traces a sudden increase to the exact query or team behind it. |
| Redundancy detection | Flags duplicate datasets and redundant materializations you're paying for twice. |
| Inefficient-query analysis | Identifies the expensive query patterns quietly driving compute cost. |
| Compute right-sizing | Proposes sizing and idle fixes for compute that bills while doing nothing. |
| Dependency-checked archival | Verifies downstream consumers before recommending anything for removal. |
| Risk-tiered cleanup | Sorts archival candidates into safe, review, and risky so cleanup stays reversible-minded. |
| Lifecycle automation | Archives cold data continuously under your retention rules. |
| Swarm hand-off | Loops in usage, catalog, and governance so a cleanup never breaks downstream or breaks policy. |
…and these are just a few of many - the agent carries dozens more autonomy skills, with new ones added continuously.
How this is different from a cost dashboard
The warehouse-FinOps space is real and genuinely capable - and most of it stops one step short of removing the waste.
Keebo autonomously tunes Snowflake's compute knobs for measured, attributed savings - but by design it stays data-blind, so it shaves the bill without ever cleaning up the stale data underneath. SELECT and similar tools give deep, CFO-legible credit attribution by user, role, and BI tool - excellent visibility, but acting on it is still your job. Unravel and Revefi go further into autonomous remediation with deep warehouse telemetry and validate-then-revert safety - genuinely strong, but each is scoped to one or two warehouses and the cost vertical alone. And Snowflake's own optimizers only ever look inside Snowflake.
Where they stop is breadth and cleanup: they tune or report spend on a single platform. Our agent attributes the cost and removes the unused-data root cause - dependency-checked - across Snowflake, BigQuery, and Databricks, as one member of a swarm that shares context with usage, catalog, and governance. The dashboards tell you it's expensive; this closes the loop to cheaper.
The takeaway
Warehouse bills became mysteries because the tooling stopped at the chart: it could tell you spend was up, but not safely turn the waste off. So the audit stayed manual, went stale, and the idle compute and stale tables kept billing. The way out isn't another dashboard - it's an agent that finds the waste, proves it's safe to remove, and reclaims it across every platform you run, continuously. You shouldn't be renting storage for tables nobody has opened since last quarter.
See it on your own bill
Point the agent at the warehouse that surprised you last month - and watch it show you exactly where the money goes, attribute the spike, and reclaim the waste it can prove is safe to remove. Book a demo to see it on your stack.