Build vs Buy in AI: Where the Line Is in 2026

Default: buy

For 80% of use cases, a configured SaaS now beats a custom build on cost, time, and reliability. The bar to build has gone up, not down, despite cheaper models.

Build when

• The workflow is your moat (rare)
• You have a proprietary data asset competitors cannot replicate
• Your scale makes per-seat SaaS pricing irrational
• Regulation or data residency forces it

A useful hybrid

• Buy the application layer (UI, workflows, integrations)
• Own the data layer (your warehouse, your access controls)
• Swap the model layer (use a gateway so you're not locked to one provider)

What this means for headcount

You do not need a 20-person ML team. You need 1-2 ML/AI engineers paired with 2-3 strong product engineers and a domain expert. Most "AI teams" are over-engineered for the work.