FinOps & Cost (dw-cost)

The FinOps & Cost agent answers the two questions every warehouse bill raises: where is the money going, and what can we safely stop paying for? It profiles usage to attribute cost by team, pipeline, and dataset, surfaces the most expensive tables, and flags data nobody is using — tables with zero queries in 30 days or untouched for 90 or more.

Its recommendations are built to be safe to act on. Archival candidates come in three tiers — safe (long-unused, no dependencies, small), review (recently used or has dependencies), and risky (actively used but expensive) — with dependency verification before anything is suggested. And it never auto-archives: every archival goes through an approval workflow, because “the agent deleted a table someone needed” is not a cost saving.

Key capabilities

• Cost attribution dashboard. get_cost_dashboard shows total monthly cost by team, pipeline, and dataset, the ten most expensive tables, the unused-table count, and total potential savings.
• Real savings math. estimate_savings computes per-asset storage and compute costs using Snowflake credit pricing, and what archiving unused tables would save.
• Unused-data detection. find_unused_data profiles warehouse usage and returns stale tables sorted by staleness, with a configurable threshold.
• Tiered archival recommendations. recommend_archival classifies candidates into safe / review / risky tiers with dependency checks — and routes everything through approval rather than acting.
• Per-query cost estimates. estimate_query_cost applies a real estimation formula (credits per row with a complexity multiplier) before a query runs, with no external calls.

Example prompts

“What are our ten most expensive tables this month, and who owns them?”

“Find everything that hasn’t been queried in 90 days.”

“What would we save if we archived the safe-tier candidates?”

“Estimate what this backfill query will cost before I run it.”

“Break down warehouse spend by team for the last month.”

Connect it to your stack

• Snowflake — the primary target for usage profiling and credit-based cost math.
• BigQuery — query cost estimation through the connector gateway.
• AWS — Cost Explorer spend, forecasts, and recommendations through the connector gateway.

See the connector catalog for setup.

Works before you connect anything

The agent starts in 🟡 Evaluation on built-in sample usage data — the cost estimation and archival-tiering algorithms are the real thing, run on a sample estate. It earns 🟢 Connected per system through a passing live test. See Verify your setup.

Limits, honestly

• In 🟡 Evaluation, dashboards and savings figures describe the sample estate — use them to judge the workflow, not your bill.
• The built-in cost model is Snowflake credit pricing. BigQuery and AWS cost figures come through their connectors; other platforms’ native pricing models are not yet implemented.
• The agent recommends archival; it does not perform it unattended. Every archival action requires approval — that is enforced behavior, not a setting you forgot to flip.
• Usage-based staleness only sees the query history it has access to. A table read by a system outside your connected estate can look unused when it isn’t — the review tier exists for exactly this reason.