Build vs Buy (AI)
Build vs buy in AI is the strategic decision between developing an AI capability internally — model, platform, data layer — and procuring it from a vendor, hyperscaler, or open-source ecosystem, weighed against differentiation, cost, time-to-value, and lock-in.
Full definition
A defensible framework asks four questions: (1) Is this capability core differentiation or shared infrastructure? (2) What is total cost of ownership over 3 years, not just year 1? (3) What is the buy option's rate of improvement? (4) What is our exit cost if the vendor changes terms? Build cases are strongest where domain data is the moat; buy cases are strongest for commodity capabilities (foundation models, vector DBs, model serving). AIDOLS structures the build-vs-buy decision in every ai-strategy-consulting engagement.
Why it matters
The two dominant AI failures are over-building (re-creating commodity infra) and over-buying (locking in on a vendor whose roadmap diverges). A repeatable framework prevents both and forces explicit choices a board can audit.
Example
A retailer builds a custom recommender (catalog data is differentiated), buys foundation-model APIs (commodity, fast-improving), and adopts open-source for model serving (medium differentiation, lock-in concern).
Related terms
- Vendor Lock-In (AI)AI vendor lock-in is the cost and difficulty of switching away from a chosen AI vendor — driven by proprietary APIs, fine-tuned weights, embedding incompatibilities, prompt portability gaps, and integrated platform features that have no clean equivalents elsewhere.
- AI Total Cost of Ownership (TCO)AI Total Cost of Ownership (TCO) is the total cost of an AI system over its full lifecycle — including model and inference costs, infrastructure, integration, data preparation, governance, monitoring, retraining, talent, and exit costs — usually expressed as 3-year fully loaded.
- AI StrategyAn AI strategy is a written, board-level plan for how an organization will use AI to create competitive advantage — naming the business goals, prioritized use cases, required capabilities, governance posture, partner choices, and a 12-36 month investment plan.
- RAG-as-a-ServiceRAG-as-a-Service is a managed offering that handles document ingestion, chunking, embedding generation, vector storage, retrieval, and LLM grounding behind a single API — letting teams ship retrieval-augmented features without building the underlying pipeline.
Source & further reading
Primary source: BCG — "Where's the Value in AI?" (2024).
Citation policy: this entry is part of the AIDOLS AI Implementation Glossary and may be quoted for research, journalism, and education with attribution to aidolsgroup.com/da/glossary/build-vs-buy-ai/.