AI Build vs Buy: Decision Framework for Leaders

Q: Should you build or buy AI solutions?

Build when AI is your core differentiator and you have unique data. Buy when AI is a utility function (chatbots, content moderation, transcription). The decision is strategic, not technical — it defines your competitive position.

Q: What is the true cost of building AI in-house?

In-house AI costs include: ML engineering salaries ($300K-500K/yr per senior engineer), infrastructure costs, data labeling, ongoing maintenance, and opportunity cost. Total Year 1 cost for a small AI team is typically $1.5-3M.

Q: When should you switch from vendor AI to in-house?

Switch when: vendor costs exceed in-house at scale, you need customization the vendor cannot provide, data privacy requirements prevent data sharing, or AI becomes your core product differentiator.

Q: What are the risks of buying AI from vendors?

Key risks include vendor lock-in, data privacy concerns (your data improving their models), API deprecation or pricing changes, limited customization, and dependency on vendor uptime and quality.

Q: How do you evaluate AI vendors?

Evaluate on: model performance on your data (not benchmarks), pricing transparency, data handling policies, SLA guarantees, integration complexity, roadmap alignment, and exit strategy (data portability).

Satyam Kumar

Volver al Blog

AI Strategy & Leadership

The AI Build vs Buy Decision Framework: Why the Biggest AI Mistake Is Not Technical

February 26, 202631 min read

AI build vs buy AI vendor selection AI decision framework buy vs build AI AI platform selection AI procurement

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Satyam

Arquitecto de IA y Cloud. Ayudando a equipos a construir sistemas que escalan a millones.

The AI Build vs Buy Decision Framework: Why the Biggest AI Mistake Is Not Technical

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