COMPARISON
Building it yourself is a real option. It is just a bigger one than it looks.
Everything below is about the same destination: a warehouse you own, definitions everyone agrees on, and reports that refresh themselves. The difference is who assembles it and who keeps it running on the third Friday of every month.
Every figure about somebody else's tooling is sourced and dated at the bottom of this page.
Most teams that build in-house are not wrong about the architecture. They are usually right about it. What they underestimate is that the architecture was never the hard part.
The hard part is that a warehouse does not tell you what a qualified lead is. Somebody has to sit in a room and decide, then write it down, then hold the line when a department wants it defined differently this quarter. That work does not appear in any build plan, and it is the work that actually makes the numbers trustworthy.
So the honest comparison is not "buy versus build." It is whether the hire, the ramp, and the salary buy you something a managed system would not have given you in two to four weeks.
Side by side
| Building it in-house | The Data OS | |
|---|---|---|
| Time to the first number you trust | Six months before anyone starts building, on the standard three to hire and three to ramp | 2 to 4 weeks to the first live dashboard |
| Who you have to hire | An analytics or data engineer, plus three months to find them and three to ramp | Nobody |
| Who picks the stack | You do, and you live with the choice for years | Already chosen, already running |
| Modelling the CRM into something reportable | From scratch, and it is the longest part | Comes modelled, then tuned to your definitions |
| When a platform changes its API | Your engineer, that week, ahead of whatever they were doing | We fix it, usually before you notice |
| Who writes the metric definitions | Whoever gets to it, usually after the first argument | Week one, in a glossary, with your leadership in the room |
| What the AI reads | Whatever someone pipes into it | Your governed tables, definitions consulted before the query runs |
| If that person leaves | You inherit tables and transformations nobody left on the team understands | Nothing changes. The system is the deliverable, not the person |
| Ceiling on customization | None. It is your code | We customize to your business on our tooling, which is why it is faster |
| Where the data lives | Wherever you stand it up | BigQuery, wired to your billing account |
| What you own at the end | Everything, assuming it was documented | Everything, documented, still running |
What the build actually costs
A lean version: one person and the tools they would need, before a single report exists.
- One data engineerUS average base salary. Before benefits, equipment, or recruiter fees $123,053 a year1
- Six months before they start buildingThree to hire, three to ramp. The salary runs the whole time About half that again
- Warehouse (Snowflake)Standard edition, AWS US East, on demand. Storage is $23 per TB per month $2.00 per credit2
- Loader (Fivetran)Priced on rows moved, so it scales with your data. Their own worked example puts four connectors at $549 a month No published price3
- Transformation (dbt)Starter tier. One developer seat is free $100 per user, per month4
- BI tool (Looker)Call sales. Every edition, including Standard No published price5
- BI tool (Tableau), if you go that way insteadPer user per month, billed annually. Tableau does not sell a monthly plan $75 / $42 / $156
Salaries are US averages and move a lot by market. Tooling is list price, which is what you pay until you are large enough to negotiate. None of this includes the months of internal time spent agreeing on what the metrics mean, which is the line item nobody budgets and everybody pays.
When building in-house is the right call
We would rather tell you no than sell you a bad setup, so here is the honest case for the other column.
- You already have three or more data engineers. The coordination cost we absorb is one you are already paying for other reasons.
- Data infrastructure is the product you sell. Then it is not overhead, it is R&D, and it belongs in-house.
- You have a data problem no platform is shaped for. Genuinely unusual, not just detailed.
- You can sustain the team indefinitely, including the backfill when someone leaves. A build you cannot staff in year three is a build you are going to inherit.
- Regulation or contract requires the pipeline to run inside infrastructure only your employees may touch.
Where these numbers came from
Vendor pricing pages and named salary aggregators, nothing secondhand. Where a vendor does not publish a price, we say that instead of guessing.
- Salary.com, Data Engineer salary, United States (states its own date: August 01, 2026) checked 2026-08-14
- Snowflake Service Consumption Table (states its own effective date: August 10, 2026) checked 2026-08-14
- Fivetran pricing page checked 2026-08-14
- dbt pricing page checked 2026-08-14
- Google Cloud, Looker pricing page checked 2026-08-14
- Tableau Cloud pricing page checked 2026-08-14
Questions
We already started building. Is that wasted?
Usually not. The most common arrangement we end up in is that the warehouse stays exactly where it is and we take over the parts nobody enjoys: connectors, modelling, definitions, and the reporting layer.
If your team already built something that works, we would rather extend it than rip it out. The audit at the start tells you which pieces are load-bearing and which ones are costing you more to maintain than to replace.
Can we do both?
That is the normal end state for companies with a data team. They keep the core architecture and the board-level reporting. We take the long tail: tracking fixes, pipeline maintenance, new sources, and the self-serve requests that otherwise sit in their backlog for a quarter.
Your data team almost never wants the work we do. They want to build models, not chase a HubSpot field rename.
What if we want to bring it in-house later?
Then you do. The warehouse is already in your Google Cloud project, the definitions are written down, and the reports read from tables you own.
That is not generosity, it is the architecture. There is no proprietary database to migrate off, so "bringing it in-house" mostly means hiring someone and pointing them at documentation that already exists.
Isn't an employee cheaper over five years?
Sometimes, on salary alone. The comparison usually breaks somewhere else.
One person is one person. They take holiday, they get sick, they cover one skill set, and eventually they leave, which historically costs somewhere between six and twelve months of momentum while the next hire reads their code. We have watched two companies go through exactly that in the last year. If you want the honest version of this maths, bring your actual numbers to the call and we will do it with you, including the case where hiring wins.
Settle it on a call.
We map your sources and your tracking gaps, then tell you which column you actually belong in. Sometimes it is not ours.