Building your own data stack sounds like the right move. You hire a data engineer, pick some modern tools, and start connecting systems. Six months in, the pipelines are running, dashboards exist, and leadership feels good about the investment.
Then the Salesforce API changes. Then your data engineer takes a two-week vacation and a pipeline breaks on day three. Then finance asks for a metric that requires joining data from four different source systems, and nobody remembers how the existing models were built.
This is the story of nearly every mid-market company that builds their data infrastructure internally. The initial build is the easy part. The ongoing maintenance is where costs quietly compound.
The Costs You Can See
The visible costs are straightforward: salaries, tool licenses, cloud compute. A typical mid-market data stack looks something like this:
- 1-2 data engineers: $150K-$200K each
- Analytics tooling (warehouse, ingestion, BI): $30K-$80K/year
- Cloud compute and storage: $20K-$50K/year
All in, you're at $350K-$530K annually. That's manageable for a company doing $100M+ in revenue. But it's the costs you can't see that break the model.
The Costs You Can't
Maintenance burden. Source APIs change constantly. Schemas evolve. New data sources get added. Your data team spends 60-70% of their time maintaining existing pipelines, not building new capabilities. You hired builders and turned them into plumbers.
Knowledge concentration. When your data engineer built the transformation logic for calculating gross margin across entities, they made dozens of small decisions about how to handle edge cases. Those decisions live in their head, not in documentation. When they leave — and at mid-market companies, turnover in technical roles averages 18-24 months — that knowledge walks out the door.
Opportunity cost. Every hour your data team spends fixing a broken connector or debugging a model is an hour they're not spending on the analysis that actually drives business decisions. The highest-value use of data talent is asking and answering business questions, not infrastructure maintenance.
Downtime impact. When a pipeline breaks on a Friday and the board deck is due Monday, the scramble costs more than just engineering time. It costs credibility. Leadership stops trusting the numbers, reverts to spreadsheets, and the data team's value proposition erodes.
The Managed Model
A managed data stack shifts these hidden costs off your balance sheet entirely. Maintenance, monitoring, incident response, source API changes, schema migrations — all handled by a team whose entire job is keeping data infrastructure running.
You don't carry the staffing risk. You don't absorb the maintenance burden. You don't lose institutional knowledge when someone quits. And you get an SLA instead of a best-effort commitment from an overloaded two-person team.
When DIY Makes Sense
To be fair, building internally makes sense in specific situations: when data is your core product, when you have genuinely unique infrastructure requirements, or when you're large enough to support a 10+ person data organization with proper management layers.
For everyone else — especially mid-market companies where data supports the business but isn't the business — the managed model is simply more efficient. You get better reliability, faster time-to-value, and lower total cost of ownership.
The question isn't whether you can build it yourself. You can. The question is whether you should.