Data & Analytics
Explain warehouse cost changes before the bill becomes the incident.
ZeroTwo compares approved BigQuery and MotherDuck usage evidence with a reviewed baseline, identifies likely workload drivers, and routes safe next actions to data owners.
What changes
Baseline
Teams compare bills with different periods, pricing modes, or workload mix
The brief records time grain, pricing basis, currency, exclusions, and a reviewed comparison window
Driver analysis
A cost spike is blamed on the largest query or newest dashboard
ZeroTwo separates price, volume, mix, retries, storage, and workload changes with source evidence
Ownership
Alerts go to a shared channel with no accountable responder
Each material driver links to the workload, owner, confidence, and next review action
Remediation
Teams make emergency changes from a cost chart alone
The agent proposes bounded options while production query, reservation, and pipeline changes require approval
Why use an agent for warehouse cost monitoring
Compare costs at the right grain
The workflow keeps period, pricing mode, region, currency, workload, and data-quality caveats visible.
Find likely drivers faster
ZeroTwo can organize query and usage evidence into price, volume, mix, retry, and storage explanations.
Route evidence to owners
The cost brief identifies the responsible workload and reviewer instead of broadcasting an unowned alert.
Keep remediation controlled
Optimization options remain proposals until a data owner reviews performance, reliability, and customer impact.
A warehouse bill shows what changed, not why it changed safely.
Spend can move because of scanned bytes, slot commitments, concurrency, repeated queries, new dashboards, backfills, storage growth, pricing changes, region, or an intentional product launch. A simple percentage alert cannot distinguish waste from valuable usage.
A useful workflow preserves measurement grain and produces a ranked driver analysis with evidence and confidence. It should never convert an uncertain attribution into an automatic production change.
How ZeroTwo prepares a warehouse cost driver brief
Collects approved BigQuery usage and pricing context
BigqueryZeroTwo gathers bounded billing, job, reservation, storage, and workload evidence with the period, project, region, pricing mode, and known exclusions.
Collects MotherDuck usage and cost evidence
MotherduckThe brief reads approved usage and billing context, preserves plan and time grain, and avoids claiming cross-platform equivalence where pricing models differ.
Builds a metric diagnostic
GPT-5The agent separates price, volume, mix, retry, storage, and workload drivers, links each conclusion to evidence, and identifies unanswered questions.
Routes material drivers to owners
SlackA Slack brief shows baseline, variance, likely driver, confidence, affected workload, owner, and approve, investigate, or dismiss options.
Tracks reviewed outcomes
BigqueryThe next run records whether the explanation was confirmed, which action was approved, and whether cost or performance changed as expected.
Treat cost monitoring as metric diagnosis, not automatic optimization
Start with a stable metric definition: cost type, currency, time zone, pricing mode, credits, exclusions, and denominator. Comparing gross list cost with net invoiced spend or a partial day with a full day creates false anomalies.
Driver analysis should separate price, volume, and mix. More spend can be expected when a valuable workload scales; lower spend can hide failed pipelines. The brief needs product and reliability context before recommending action.
Optimization is a production change. Query rewrites, schedule changes, reservation adjustments, cache policy, and pipeline cancellation require accountable data owners and performance validation.
Get started in under 10 minutes
Connect your tools
One-click OAuth for each integration. No API keys, no engineering.
Describe what you need
“Each weekday, compare approved BigQuery and MotherDuck usage and cost evidence with the prior four-week same-day baseline. Preserve pricing mode, currency, region, period, exclusions, and workload owner. Explain material changes as price, volume, mix, retry, storage, or unknown, include source links and confidence, and post a review brief to #data-platform-costs. Do not cancel queries, change reservations, rewrite pipelines, or notify customers.”
It runs on schedule
Runs on a reviewed cadence and when a material threshold is crossed; remediation remains approval-gated.
Frequently asked questions
It is an agent that compares bounded warehouse usage and billing evidence with a reviewed baseline, identifies likely cost drivers, and routes an evidence-linked brief to owners. In ZeroTwo, it can organize BigQuery and MotherDuck context without pretending their pricing models are identical or automatically changing production workloads.
The workflow can examine approved job, billing, reservation, storage, user, and workload evidence at a consistent grain. It separates possible price, volume, mix, retry, and storage drivers and records confidence. A data owner should verify attribution because incomplete labels, shared service accounts, credits, and reporting delays can change the explanation.
It can report each platform under its own pricing and usage contract and assemble one operational brief. It should not claim one unit is equivalent to another without a valid normalization. Keep pricing mode, time period, region, plan, credits, scanned data, compute time, and workload purpose visible.
Require approval for cancelling queries, changing reservations or capacity, modifying schedules, rewriting production SQL, changing retention, disabling pipelines, altering service accounts, or accepting performance regressions. The agent can recommend a bounded investigation or optimization, but the accountable data owner should validate reliability, latency, and customer impact.
Track confirmed driver accuracy, false alerts, unknown-driver rate, time to owner response, reviewer corrections, approved savings, performance regressions, and whether the next run verified the expected outcome. Do not optimize only for lower spend; the workflow should preserve required data freshness, reliability, and product value.
Related workflows
- Data & AnalyticsMetric Root Cause Analysis
- FinanceBudget vs Actual Variance
- Engineering & DevOpsDatabase Migration Readiness
Turn warehouse spend changes into owned decisions.
Compare the right evidence, identify likely drivers, and keep production changes behind data-owner approval.