VECTORIO“For data teams whose warehouse bill grows faster than their data”
Stop paying a cluster for laptop-sized queries.
Move dev, test, CI and mid-size jobs off the warehouse bill and onto a laptop or edge box.
- I can't explain next month's warehouse bill, and it keeps going over budget.
- We pay for a cluster to scan data that would fit on my laptop.
- Compute is only the visible part; the people cost around it is bigger.
- Move dev, test, CI and mid-size jobs off the warehouse bill and onto a laptop or edge box.
- The same pipeline runs locally in development and scales out for production; no rewrite.
- No more glue scripts and no separate scheduler for those jobs.
From today to the first result.
a cloud warehouse billed per query
Dev, test and CI runs scan the warehouse at cluster rates. In one analysis of warehouse workloads, 90 % of queries touched less than 100 MB.
1 billion rows grouped and summed in 32.1 ms on one notebook, CPU only. The nightly job runs off the warehouse bill.
Move one expensive nightly job from the warehouse onto a notebook and keep the warehouse for everything else.
one jobRevenue by town over 100 million sales. Step by step.
100 million property sales are loaded once; the revenue-by-town query then answers in milliseconds.
- CSV Subscriber · SQL QueryCSV.Subscriber · ETL.SQLQuery
- CLI ProcessorETL.CLIProcessor
src → CSV.Subscriber
sql_cold → ETL.SQLQuery
out → ETL.CLIProcessor- CSV SubscriberReads the sales CSV in 1M-row batches with a fixed schema.
- SQL QueryRuns the query as soon as the data is loaded.
- CLI ProcessorPrints the town table.
A real workflow from our end-to-end test suite; the designer view is illustrated.
What you get. From the first pilot.
Move dev, test, CI and mid-size jobs off the warehouse bill and onto a laptop or edge box.
The same pipeline runs locally in development and scales out for production; no rewrite.
No more glue scripts and no separate scheduler for those jobs.
Why your tools fall short.
Warehouse pricing is consumption-based, so more usage means a higher bill and FinOps tooling only observes it.
DuckDB-style single-node tools are fast but have no orchestration, audit trail or multi-user governance.
Spark/Databricks have cluster start-up and tuning overhead that is wasted on small and medium queries.
- Budget overruns and FinOps pressure
- AI workloads adding data volume and spend
- EU data-sovereignty and exit pressure
- Engineer time spent on plumbing
Your pilot. Three steps.
Your data shape
Tell us what data you have, where it lives and what the result must be.
A scoped pilot
On your data and your hardware, with the success measure written down first.
A clear decision
You see the result against that measure — and keep every workflow and record.
Related roles in Cost & reproducibility.
Finance & trading risk analytics
Risk numbers you can reproduce on demand.
Retail, e-commerce and quick-commerce operations
City-scale inventory analytics on one box.
System integrators and consultancies building data platforms for clients
One engine to deliver, audit and hand over.
ISVs and SaaS vendors needing an embedded analytics / ETL engine
Built into your product.
AI/ML teams
Reproducible data prep at memory speed.
Insurance analytics
Reproducible reserves, run in minutes.
Mid-size companies without a data team
Replace the spreadsheet chain, keep the data.
Bring us your hardest data problem. Let's solve it together.
A short pilot on your data and your hardware. We agree on what success looks like before we start.