# What does a data warehouse cost for an SME? Honest numbers

> Google what a data warehouse costs and you'll mostly find 'it depends' and a button for a free consultation. Here's the real answer, with figures: what the market charges, where the cost actually sits, when you should not buy a warehouse — and what I charge for one.

_August 11, 2026 · Business, Data Warehouse, Costs_

Google what a data warehouse costs and you'll find two kinds of answers: "it
depends" and a form for a free consultation. Both true, both useless when you
simply want to know whether this is a ten-, twenty- or hundred-thousand euro
investment before you raise it internally.

I work with fixed prices that are simply published on my site, so let me
answer the question the same way. With numbers.

## What does a data warehouse cost for an SME?

For an SME with a handful of source systems, a serious data warehouse —
modelled, tested, with reporting on top, delivered so your own team can carry
it — costs roughly **€15,000 to €25,000** with a freelancer. An agency
typically adds 50 to 100% on top. If you see offers well under €10,000,
you're usually not buying a warehouse but a dashboard with some connectors —
which can be exactly the right choice; more on that below.

For comparison, from public Dutch market research:

| Scenario | Indicative price |
| --- | --- |
| Serious Power BI dashboard, *without* a warehouse underneath | around €5,000 |
| Two dashboards, three sources and a data warehouse | around €17,500 |
| Data warehouse with a freelancer (modelled, tested, transferable) | €15,000 – €25,000 |
| The same project with an agency | +50 to 100% |
| Freelance data engineer, hourly rate | €80 – €130 |
| BI consultant, day rate | €600 – €1,000+ |

## What you're paying for (hint: not the technology)

The software is not the problem. The engines I build warehouses on are open
source or cost tens of euros per month. What you're buying is work, and that
work sits in four places:

1. **Definitions.** What counts as a customer? When does an order count — at
   ordering, delivery or payment? Every figure your departments currently
   disagree about is a definition someone has to pin down. This is the
   hardest and most valuable part of the engagement, and it has nothing to do
   with technology.
2. **Modelling.** Translating those definitions into a data model that still
   holds next year, when a source gets added or the organisation changes.
3. **Testing.** The difference between "the report ran this morning" and "the
   report is *right* this morning". Checks at every step, so a problem in the
   source is caught before the monthly meeting finds it.
4. **Handover.** Documentation and a handover session, so your team can carry
   on without being chained to the builder.

> You don't buy a warehouse because you have data. You buy one because you
> have arguments — and you don't want to have them again every month.

## What pushes the price up or down

- **The number of sources.** Every connection is work: extracting, cleaning,
  testing. Three sources is a different project than ten.
- **The state of your definitions.** If they're settled, the build starts
  immediately. If they still have to be fought out, that's the first week.
- **History.** "How did this look last quarter?" sounds innocent, but
  reporting correctly across reorganisations is an explicit design choice
  that takes time.
- **The reporting layer.** One management dashboard is not fifteen reports
  for three audiences.
- **Who will maintain it.** Delivering it transferable to your own team is
  one-off work; ongoing operation is a subscription — mine is called
  [Reporting on autopilot](https://ruudjuffermans.nl/en/services/reporting-automation).

## What you can do yourself to keep it cheap

You can do this part without hiring anyone, and it genuinely saves money:

- **Pin down the definitions before the build starts.** One working session
  with the departments involved, writing down what each core figure means,
  saves a week of mid-project debate.
- **Start with the figures decisions actually run on.** Not "everything we
  have", but the five to ten KPIs the monthly conversation is really about.
  You can always extend; starting too big always backfires.
- **Don't buy tooling yet.** The licence choice follows from the model, not
  the other way around. Organisations that buy a platform first and then
  work out what goes on it pay twice.

## When you should *not* buy a warehouse

Honesty sells poorly but works well, so: there are situations where I advise
against it.

- **One source, one report.** Then a good
  [dashboard straight on that source](https://ruudjuffermans.nl/en/services/dashboards) is faster and
  a fraction of the price. A warehouse becomes worthwhile once figures from
  multiple systems have to agree with each other.
- **The organisation changes every quarter.** A warehouse anchors
  definitions; if those still shift weekly, it's too early.
- **Nobody owns reporting.** Without an owner, every warehouse decays into
  exactly the situation it was built to fix. Sort the ownership first, then
  the technology.

## What I charge

My package [Your data warehouse](https://ruudjuffermans.nl/en/services/single-source-of-truth) costs
**from €15,000** and takes three to four weeks. For that you get the smallest
honest version: pipelines on a bounded set of sources, a layered and tested
data model, one definition per figure, a Power BI dashboard on the KPIs, and
documentation plus handover so your team can carry it. More sources or more
complex history push the price up — you hear that up front, not afterwards.

Do the maths yourself: four weeks of senior work at the hourly rates above
quickly reaches €16,000 on an hourly basis — with no fixed scope and no fixed
price. The fixed price isn't a discount; it's the same money with the risk on
my side of the table instead of yours.

The cheapest way to start, by the way, is not the warehouse itself but the
[Data scan](https://ruudjuffermans.nl/en/services/data-scan): it delivers the target model that serves
as the blueprint for the build — the first build week gets shorter, and you
know where you stand before committing.

*What such a warehouse looks like when it's done — star schema, tests,
documentation — is something I show in [the open-data warehouse](https://ruudjuffermans.nl/en/blog/open-data-warehouse-met-dbt)
I built in public.*

*Recognise the arguments about whose number is right?
[Book an intro call](https://ruudjuffermans.nl/en/contact) — I'll gladly help you place your situation
on the ladder, no strings attached.*