Full-table scans.
Queries reading entire tables to answer questions about last week. No partitioning, no clustering — every question costs as much as the biggest one.
Data & Analytics · BigQuery FinOps
BigQuery cost optimization — cut your bill 30–60%, measured on your own billing
BigQuery doesn't get expensive by accident. It gets expensive through full-table scans, unmanaged slots, redundant PDTs, and scheduled queries nobody watches. We find where your money goes, fix the structure underneath, and measure the reduction against your baseline bill — not a benchmark slide.
30–60% typical cost reduction 27% avg waste, founder's past audits Free 15-min read-only check
Cost drivers · 01
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Where BigQuery bills actually grow.
We find the same five patterns in almost every environment we audit.
No slot strategy.
On-demand pricing for perfectly predictable workloads. No reservations, no autoscaling, no cost caps.
Redundant materialization.
Multiple PDTs computing the same data on different schedules. Every dashboard rebuilds the world from scratch.
SELECT * everywhere.
Bytes processed is the meter, and most queries read columns nobody uses.
Zero cost visibility.
No attribution by query, user, or dashboard. Finance sees one number going up; nobody can say why.
None of these require a migration to fix. They require someone who knows where to look.
The method · 02
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Four steps. Your warehouse, your billing, your baseline.
Cost audit
We map your most expensive queries, jobs, and Explores against your actual billing. Read-only. Output: one shared document with the waste, named.
Week 1Quick wins
Misconfigured queries fixed, dead scheduled jobs turned off, obvious SELECT * offenders rewritten. Visible in your next billing cycle.
Weeks 1–2Structural work
Partitioning and clustering on heavy tables, right-sized slot reservations, materialization strategy, BI Engine where it pays. This is where the 30–60% lives.
Weeks 2–6Monitoring & handoff
Cost attribution dashboards by project, user, and query. Alerts before a bad query becomes a bad month. Documentation and training so it stays fixed.
Weeks 6–8
What we optimize · 03
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Comprehensive BigQuery FinOps.
Partitioning & clustering
Tables restructured so queries scan what they need, not everything they could.
Slot strategy
Right-sized reservations for predictable load, on-demand or flex where it's cheaper. Often a hybrid.
Query tuning
Expensive queries rewritten, unnecessary joins removed, approximate functions where exactness isn't needed.
Materialization strategy
PDTs, materialized views, and scheduled queries — the right caching layer per use case, deduplicated.
BI Engine configuration
Sub-second dashboard queries and reduced compute where the workload fits.
Cost monitoring & attribution
Dashboards showing spend per project, user, query, and dashboard. Accountability built in.
The agent layer · 04
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Agents catch the cost before it reaches the bill.
BigQuery tells you what a query cost after it ran. That's the whole problem: the signal arrives on an invoice, weeks late and aggregated, long after whoever wrote the query moved on to something else. Agents move that signal to the two moments where acting on it is still cheap — the pull request, and the day a job changes behavior.
Cost estimate on every pull request
Before the merge, the agent dry-runs the query and posts what it will scan and what that costs at production volume. A missing partition filter becomes a review comment instead of a line item you find next month.
Anomaly watch on the jobs you already run
A scheduled job that scanned gigabytes yesterday and terabytes today is the most expensive event in a data platform, and nothing in BigQuery is going to call you about it. The agent reads INFORMATION_SCHEMA on a schedule and does.
Dead schedules and orphaned tables
Scheduled queries with no viewer, tables rebuilt nightly and read by nobody, PDTs persisted for a dashboard deleted two quarters ago. This is the waste that grows back after every audit, so the agent tracks it continuously instead of once.
Validation after the structural work
Partitioning, clustering, and rewritten queries change how a number is computed. The agent compares row counts and aggregates before and after, so "cheaper" never quietly turns into "different".
The agent doesn't change your warehouse. It puts the number in front of the person who's about to — the last moment where a cost decision is still free.
Pricing · 05
Your recoverable spend, before our invoice.
Before RavencoreX, our founder's engagements cut 30–60% of the monthly bill, measured on the client's billing before and after, and his audits found 27% waste on average. Run the math on your own number: a $10,000/month bill carries $3,000–$6,000/month of recoverable spend — $36,000 to $72,000 a year. The free check exists so you see your number before spending a dollar.
Free cost check
Read-only look at your billing and query patterns. We name the estimated recoverable spend. No findings, no charge.
$0 · 15 min
BigQuery cost audit (scoped)
Complete FinOps review: queries, jobs, slots, storage. Prioritized savings plan with impact per finding.
Fixed scope · 1 week
Fractional data engineering
Ongoing optimization and monitoring, measured monthly against your actual billing.
Monthly retainer · flexible scope
Structural projects are scoped after the audit. The committed reduction goes in the SOW as a number — not an estimate.
The proof · 06
Measured on the client's billing.
Deep tuning of a high-volume Looker and BigQuery platform: 30%+ lower BigQuery cost and 40%+ faster dashboards — cost and performance optimization are the same work seen from two sides. What we didn't do: migrate the warehouse.
FAQ · 07
What prospects usually ask before the first call.
How much can BigQuery cost optimization reduce our bill?
The typical range is 30 to 60 percent of the monthly bill, measured on actual billing before and after the work. The exact number depends on the current state of your stack: unpartitioned tables, unmanaged slots, redundant scheduled jobs, and unused Explores are the patterns that account for most waste. Before RavencoreX, our founder's audits found 27% recoverable spend on average. Your specific range comes out of the free check.
How much does BigQuery cost optimization cost?
It depends on scope. The 15-minute read-only check is free — if we find nothing worth fixing, we tell you and you owe nothing. From there you get a fixed-scope proposal: a one-week cost audit, a structural optimization project, or ongoing FinOps support — sized to your billing and query patterns. In every case you see the recoverable number before committing. Book the free check and we'll scope it.
How long until we see savings?
Quick wins — dead scheduled jobs, misconfigured queries — show up in your next billing cycle. Structural changes like partitioning and slot reservations land their full impact within 6 to 8 weeks. That timeline goes in the SOW as a commitment: if the reduction isn't on your dashboard by week 8, we don't bill the next phase.
Will cost optimization hurt query performance?
No — it usually improves it. Properly partitioned tables, tuned queries, and BI Engine make queries faster and cheaper at the same time. Cost and performance optimization are the same discipline; we measure both.
Does the cost agent need access to our data?
No. It reads metadata: INFORMATION_SCHEMA job history, billing exports, and the query text in your pull requests. That is enough to know what a query scans, what it costs, and how that changed — without reading a single row of your tables. Access is read-only and scoped to the projects in the engagement, nothing is used to train models, and every action lands in your own audit trail.
Should we be on on-demand or capacity (slot) pricing?
It depends on your workload pattern. Predictable, high-volume workloads usually benefit from reserved capacity; variable or low-volume workloads are often cheaper on-demand. Most environments we audit end up on a hybrid. We analyze your actual usage history and recommend the model with the receipts to back it.
Do you work with environments that don't use Looker?
Yes. We specialize in Looker + BigQuery, but the warehouse-side work — partitioning, clustering, slots, query tuning, cost attribution — applies to any BI tool on top: Tableau, Power BI, or custom applications.
What size of BigQuery bill makes this worth it?
Opportunities typically become material from around $3,000/month in BigQuery spend. Below that, the audit may still find waste, but the savings won't always justify a project — and if that's your case, we tell you on the first call.
Let's talk · 09
See your number before you decide anything.
Fifteen minutes, read-only access to your billing. We tell you what's recoverable — and if there's nothing there, we tell you that too.
Related services · 08
More ways we work on Looker and BigQuery.
LookML development, embedded analytics, dashboards, and team training — knowledge your team keeps.
Performance optimization for Looker and the BigQuery bill behind it — measured against your baseline.
Migrate to Looker from Tableau or Power BI
Zero-downtime BI migrations: parallel run, validated data, trained team, fallback plan.