Build vs Buy in Data: True Cost, TCO Framework, and Opportunity Cost
Most firms think they save money by building internal data tools. Learn why hidden costs make builds 3–5x more expensive — and how to calculate true TCO.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
The $150k Illusion
On paper, building an internal data tool looks cheap:
Two engineers.
Three months of work.
$150k in salaries.
You think you “own” the tool. You think you avoided vendor fees.
Here’s the truth: your $150k tool is costing you millions.
Not in the build — but in hidden costs of maintenance, turnover, and lost opportunity.
This is why the build vs buy debate isn’t a technical decision.
It’s a financial and strategic decision.
The True Cost of Building Internal Tools
When execs calculate build cost, they only add up salaries and time.
That’s just the tip of the iceberg.
Here’s what they miss:
Maintenance Overhead → Every tool requires fixes, patches, and support.
Turnover Risk → When the engineer leaves, new hires spend months learning undocumented systems.
Feature Backlogs → Requests pile up, and the tool becomes outdated.
Opportunity Cost → While your team babysits pipelines, competitors build products that win revenue.
What looks like $150k on paper often becomes millions in hidden cost over 3 years.
How to Calculate TCO for Build vs Buy
CFOs and COOs should run a Total Cost of Ownership (TCO) framework before green-lighting any internal build.
Step 1: Build Cost
Engineer salaries × project time.
Initial project management overhead.
Step 2: Maintenance Cost
% of engineering hours per month spent fixing or updating.
On-call firefighting costs.
Step 3: Turnover Cost
Retraining and ramp-up for replacements.
Lost productivity during handoff.
Step 4: Opportunity Cost
Revenue lost because engineers were fixing tools instead of enabling growth.
Market share lost to faster-moving competitors.
Add these up. Compare against vendor subscription costs.
In most cases, buying beats building by 3–5x over a 3-year horizon.
(Related: Why Most Data Teams Fail in Year One)
When Does It Actually Make Sense to Build?
There are rare cases where building is the right choice:
Differentiation → The tool creates a competitive edge (e.g., proprietary ML).
Industry-Specific Needs → No vendor solution fits strict compliance/regulatory requirements.
Strategic IP → The tool itself is the product (e.g., SaaS).
If your reason is “we don’t want to pay vendor fees” → you’re not building strategically.
You’re building a liability.
The Opportunity Cost of Plumbing
Your engineers are your most expensive asset.
So ask: what are they working on?
Competitors are building predictive models to grow LTV.
Your team is building a custom Airflow operator.
Every hour spent on plumbing is an hour not spent on revenue.
That’s the real opportunity cost of building.
Checklist: Build or Buy?
Ask these before building:
Does it create direct revenue impact? (Yes → maybe build. No → buy.)
Can a vendor do it 80% as well? (Yes → buy.)
Will engineers spend >20% of time maintaining it? (Yes → buy.)
Can the CFO explain ROI to the board? (No → buy.)
If you can’t answer clearly → you’re building a liability, not an asset.
The Bottom Line
Your $150k internal tool is not an asset.
It’s a liability.
It doesn’t just cost you money to run — it prevents your best people from driving growth.
The build vs buy debate is not an engineering choice.
It’s a CFO-level decision about ROI and survival.
Stop calculating build cost.
Start calculating the business cost.
Schedule a Data Strategy Assessment and audit your internal tools. You’ll find at least 2–3 “zombie tools” quietly draining millions.
