Two years ago, the build-vs-buy decision for AI was straightforward: unless you were a tech giant, you bought. Today, the landscape has shifted so dramatically that the old heuristics no longer apply.
The New Landscape
The proliferation of open-source models, fine-tuning platforms, and managed inference services has collapsed the barrier to building custom AI capabilities. A team of three engineers can now deploy a production-quality AI feature in weeks, not months.
But "can build" doesn't mean "should build." The real question has become more nuanced: where does custom AI create defensible value, and where is it undifferentiated infrastructure?
A Decision Framework
We propose evaluating build-vs-buy decisions across three dimensions: differentiation potential, data advantage, and iteration speed.
Differentiation potential asks whether the AI capability will be a meaningful competitive advantage. If every competitor will have the same feature within six months, buying makes more sense—ship fast and invest your engineering effort elsewhere.
The Hidden Costs
The most common mistake we see is underestimating the ongoing costs of building. The initial development is often the easy part. The hard part is monitoring, retraining, handling edge cases, and keeping up with the rapidly evolving model landscape.
Teams that build should budget 3x their initial development estimate for year-one maintenance. If that math doesn't work, buying is the smarter play.