Control your data cloud, query by query.

Every credit, DBU and byte on Snowflake, Databricks and BigQuery — traced to the query that spent it, watched for spikes in near-real-time, and fixed under policy. Control before the bill, not after.

One bad
query
can drain a weekend of credits before anyone notices — on a bill with no query-level owner.
Consumption billing · Snowflake · Databricks · BigQuery
The problem

Nobody knows which query spent the credits.

Snowflake credits, Databricks DBUs and BigQuery bytes all land on one consumption bill. An unbounded query or an oversized warehouse can drain a month's budget in a weekend — and by the time it surfaces, the credits are gone and no one can say whose workload did it.

Idle warehousesauto-suspend off
+
Unbounded queriesfull-table scans
+
Oversized computeover-provisioned
The Value-Control loop

The same four steps, tuned for data

Plan the workload, attribute every credit to a query, act on waste under policy, and prove the savings — across Snowflake, Databricks and BigQuery. Each step takes action; it does not just report.

Plan
Estimate the workload before it runs
  • Model warehouse / cluster sizing & cost
  • Query cost preview before it ships
  • Budget guardrails by team & workspace
DATA ENGG · PRODUCT
Explain
Every credit traced to a query
  • Query-level attribution — credits, DBUs, bytes
  • By query → user → app → customer → LOB
  • Copilot Q&A — Ask FI in Teams & Copilot
FINANCE · DATA ENGG
Act
Stop the waste that burns credits
  • Near-real-time anomaly & bad-query detection
  • Copilot-driven, policy-governed automation
  • Warehouse right-sizing & auto-suspend
  • Query & job fixes → Jira / ServiceNow
DATA ENGG · IT EXEC · FINANCE
Prove
Credit savings you can take to the board
  • Realized credit / DBU savings, re-baselined
  • Chargeback by team & workload
  • Forecasts & budget variance
FINANCE
Inside the Explain pillar

Every credit traced to the query that spent it

The unit of data cost isn't a warehouse — it's a query. FinOpsly attributes every credit, DBU and byte scanned to the individual query, then rolls it up to the user, app, customer and LOB.

Query-level cost · ANALYTICS_WHlast 24h · top spend
SELECT … FROM events e JOIN sessions s …
Snowflake · WH ANALYTICS_L · user jsmith · 18m · no partition filter
42.1 cr
$126.30
MERGE INTO dim_customer USING stg_…
Snowflake · WH ETL_XL · user etl_svc · 9m
88.4 cr
$265.20
SELECT * FROM raw.clickstream
BigQuery · 2.4 TB scanned · user mchen · full scan
—
$12.00
dbt run — fct_orders
Databricks · job nightly_etl · 31 DBUs · svc account
31 DBU
$16.65
Rolls up → user → app → customer → LOB. The top query alone is $126/run × 48 runs/day — a partition filter cuts it 90%.
Explain · query-level attribution

See every credit, query, warehouse and role

FinOpsly reads the query history across Snowflake, Databricks and BigQuery and attributes cost to the individual query — apportioning warehouse credits, DBUs and bytes scanned to the statement, task and role that drove them. Then it rolls up to the owner.

This is the layer native tools skip: not “warehouse X cost $40k,” but “this query, run by this user, for this customer, cost $126 a run” — the detail you need to actually fix it.
Near-real-time anomaly detection

Flag the runaway query before it burns the month

One bad query or a runaway job can burn thousands in credits and DBUs in minutes. FinOpsly watches consumption as it happens and flags the spike — the query, the warehouse, the user, the projected burn.

Act · spike detection

A bad query shouldn't cost you a weekend

A missing partition filter, an accidental cross join, a job stuck in a retry loop — any one can silently torch a budget. FinOpsly detects the abnormal consumption in near-real-time, pins it to the exact query and owner, and projects the burn if it runs unchecked.

Then it alerts you and notifies the owner — with the query, the warehouse and the projected burn — so your team can stop it fast. FinOpsly gives you the signal and the context; you take the action.
Live credit consumption · ANALYTICS_XL
credits / min
⚠ Credit spike detected+$4,200 · 12 min
Runaway query on ANALYTICS_XL by etl_svc — full table scan, no partition filter. Query 01b2f…9a.
↗ Projected $18,400 if it runs unchecked
Owner + on-call auto-notified · policy: alert on breach — your team takes the action
Inside the Act pillar

Copilot-driven fixes, governed by policy

Ask FI diagnoses the cost driver and prescribes the fix. Policy decides whether it needs a human approval or runs automatically — with a full audit trail on every change.

Act · policy-governed automation

Right-size the warehouse, tighten the suspend

Ask FI spots the oversized warehouse, the never-suspending cluster, the query that needs a rewrite — and prescribes the fix with the saving attached. Because a warehouse resize and auto-suspend are reversible, FinOpsly can apply them under policy; deeper changes route to Jira or ServiceNow.

Agentic, with human approval. Policy decides per action: auto-apply the safe, reversible ones; hold the rest for approval. Every change is recorded end-to-end.
FIAsk FI · recommendationANALYTICS_L · 14-day analysis
ANALYTICS_L is oversized. Avg utilization 22% over 14 days, with idle time between batch windows. Recommend resize L → M and set auto-suspend 60s.
size: L → M · auto_suspend: 600s → 60s−$3,100/mo
🔒 Reversible change · recorded to the audit trail
Why teams run data on FinOpsly

Not another dashboard — a system that acts

Native tools show you the meter. FinOpsly reads it, explains it, and acts on it — with your approval or under your policy.

Thought partner
Ask FI interprets your context and prescribes the fixes that deliver the greatest impact — not a wall of alerts.
Planning with precision
Simulate a change, predict the credit impact, and map the smartest path to savings before a single credit is spent.
Automated execution
Act on approved fixes, or auto-execute the reversible ones under policy — every step recorded for a full audit trail.
Team-wide governance
One system where finance, data engineering and product all act on cost insight — not just observe it.
Trusted by enterprise data & platform teams

Take control of your data spend.

Plan it. Explain it. Act on it. Prove it.

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