Build-vs-buy for AI isn't a binary — it's five questions. Most teams default to building because building is more fun than buying, and the default costs them runway.
The build-vs-buy decision for AI is loaded: engineers want to build, vendors want you to buy, and the honest answer is usually somewhere specific. The framework is five questions that force the specific answer.
The Five Questions
- **Is it differentiated?** If yes, build. If no, buy — don't reinvent a commodity.
- **What's the build cost — honestly?** Time, runway, maintenance, opportunity.
- **What's the buy cost — honestly?** License, integration, lock-in, exit.
- **What's the integration cost either way?** The hidden tax on both paths.
- **What's the exit cost?** What happens when you want to change?
The Cashflow Lens
Each path has a cashflow profile, not a price tag. Building is cheap in license, expensive in time and maintenance, and gives you control. Buying is cheap in time, expensive in license and lock-in, and gives you speed. The decision is which profile fits your runway and your stage — not which is "better."
- Build: low license, high time, high maintenance, high control.
- Buy: high license, low time, low maintenance, low control.
- The decision is fit, not rank.
The Issue Tree: When Each Wins
- **Build wins** when it's differentiated, the build cost is justified by the runway, and control matters.
- **Buy wins** when it's commodity, speed matters more than control, and the exit is clean.
- **Hybrid wins** when the core is differentiated (build) and the periphery is commodity (buy).
The Bias to Watch
The bias is toward building — because building is more fun, more visible, and more resume-relevant than buying. The discipline is to ask the five questions honestly, including the build cost, and to refuse to build what isn't differentiated. Most AI infrastructure isn't — and the teams that build it anyway pay for the bias in runway.
Conclusion
Build-vs-buy for AI is five questions: differentiated, build cost, buy cost, integration, exit. The bias is toward building; the discipline is to answer honestly and refuse to build the undifferentiated. The framework produces the specific answer, not the default one.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Run your next AI decision through the five questions.
Tell us what they say, and we'll pressure-test the answer. See build vs buy for startups for the startup version.
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