The question you’re actually asking
“Build vs buy” sounds like a technology decision. It isn’t. It’s a question about where you want to create and capture competitive advantage.
The framework below forces that question explicitly.
Step 1: Classify the capability
Before evaluating build vs buy, classify the AI capability you’re considering:
Commodity — Available from multiple vendors, not differentiating. Example: OCR, speech-to-text, basic sentiment analysis. Default: buy.
Competitive parity — You need this to be competitive, but it’s table stakes — not a source of advantage. Example: recommendation engine for an e-commerce site. Default: buy and customize.
Competitive advantage — This is specific to your business, creates durable differentiation, and competitors can’t easily replicate it. Default: build.
Core IP — This is your business. Example: the pricing model for a market-making firm. Build, and protect it carefully.
Step 2: Evaluate data differentiation
Buying a vendor solution and building with your own data is often the best of both worlds. Ask:
- Does the vendor solution allow fine-tuning on your data?
- Is your proprietary data the source of differentiation, not the model architecture?
- Can you achieve competitive performance by starting with a vendor model and adapting it?
Step 3: Run the cost model
| Factor | Build | Buy |
|---|---|---|
| Initial cost | High (team, infra) | Low (subscription) |
| Ongoing cost | Engineering headcount + infra | Subscription + integration |
| Time to value | 6–18 months | Weeks |
| Customization | Full control | Constrained |
| Vendor dependency | None | High |
| Data control | Full | Shared or vendor-held |
Step 4: Assess team capability honestly
Build only if you can answer “yes” to all of these:
- Do you have engineers who have shipped ML in production before?
- Do you have (or can you hire) the specific expertise needed?
- Is your data infrastructure ready to support the initiative?
If you’re a Level 1–2 organization on the AI maturity scale, defaulting to buy is almost always correct.
Common mistakes
Building what you should buy: Spending 18 months building a generic recommendation engine instead of using an established vendor solution and investing that time in the proprietary data that makes recommendations unique to your business.
Buying what you should build: Licensing a vendor solution for something that is genuinely core IP, creating dependency and losing the ability to differentiate.
Ignoring the data question: Choosing build vs buy without thinking about where your proprietary data advantage lies.