The strategic decision between training/fine-tuning proprietary models, deploying open-weights variants, and leasing access to commercial APIs. By 2026, most en
Build-vs-buy for AI weighs assembling your own system (models, retrieval, orchestration on your data) against vendor products, across capability fit, differentiation, total cost (including integration and ops), data control, and vendor risk. 2026 reality is mostly buy-the-platform, build-the-differentiator: commodity capabilities from vendors, proprietary-data and workflow-deep systems in-house.
These decisions compound: buying everything leaves you undifferentiated and lock-in-exposed; building everything burns scarce talent on commodity plumbing. A disciplined framework, anchored in where your data and workflows create advantage, is among the highest-leverage strategic tools in enterprise AI.
Anything touching proprietary data advantages, differentiating workflows, or margins at scale, plus integrations too specific for vendors. If it's core to how you win, owning it usually pays.
Underestimating non-license costs both ways: buyers ignore integration and change management; builders ignore ongoing ops, evals, and model churn. Honest TCO over 3 years changes many decisions.
Abstraction layers over model APIs, exportable data and prompts, eval suites that make switching testable, and contract terms on data use and portability. Lock-in is a design choice, not a fate.