Tell us about your data stack

Six questions, about a minute. Nothing is stored until you ask for the report.

0 of 6 answered
Databases, SaaS tools, APIs, file drops, and anything else that has to land in the warehouse.
Roughly how much data you expect to hold. This drives engineering difficulty, not just storage.
Near real-time is a different architecture, not a faster schedule, so it costs accordingly.
Metrics that need to be agreed, modelled once and trusted everywhere they appear.
The team you would need to stand this up and keep it running.
This is the engineer's annual base salary
$
Enter an amount between $60,000 and $500,000.
Advanced: cloud and tooling spend
Paid to your cloud and tool vendors either way, so it is excluded from the saving. Leave blank and we will estimate it from your answers.
$
What we assume. A fully loaded engineer costs 1.30× base salary. Hiring one takes about 3 months, and a new hire runs at half output for their first 3 months. We assume 120 productive hours a month on this project per engineer, once ramped.
Estimated year-one saving
$0
Answer the questions on the left and your estimate will build here.
0
engineering hours your team never spends, year one
0
sooner to a working stack

Year one, side by side

People cost only. Cloud and tooling spend is excluded, because you pay it either way.

In-house team$0
Fully loaded payroll, less the months spent hiring.
Datum Labs$0
Build plus twelve months of maintenance.

Your comparison will appear here once every question is answered.

Time to a working stack

From decision to first trusted dashboard

In-house
0
Hiring, then building, at reduced output while they ramp.
With Datum Labs
0
A team that has built this before, starting the week you sign.
$0
saved over three years
0%
of the in-house cost
0
cheaper than hiring from
0
engineer-months freed, year one
0
hours a month we absorb
0
working days to go live