Build vs. Buy: Making the right call on your AI stack

A framework for deciding when to roll your own AI infrastructure versus leveraging managed APIs.

Every ambitious business now faces the same fork in the road: do we build our own AI infrastructure, or do we pay for the managed APIs that abstract it away? The wrong answer costs months and hundreds of thousands of dollars.

Why This Decision Is Harder Than It Looks

The surface answer seems easy: managed APIs (OpenAI, Anthropic, Google Gemini) are faster to start with, while building your own stack gives you control. But the real question is never about technology — it's about where your competitive moat actually lives.

If your core product is the AI model, building makes sense. If AI is a feature that makes your product smarter, it almost never does.

The Build Case: When You Should Roll Your Own

1. Data Sovereignty Is Non-Negotiable

Regulated industries — healthcare, finance, government — often cannot send data to third-party APIs. If your data cannot leave your environment, you have no choice but to self-host. Open-weight models like Llama and Mistral have made this genuinely viable.

2. You Have Proprietary Training Data at Scale

If you've accumulated years of domain-specific data that a general model has never seen — clinical notes, legal precedents, engineering manuals — fine-tuning on that corpus creates a capability that money cannot buy from an API vendor.

3. Inference Cost at Extreme Scale

At 100M+ requests per month, the unit economics of managed APIs can flip. Run the math. If your marginal cost per inference exceeds $0.001 and volume is massive, owning the stack starts to pay back within 18 months.

The Buy Case: When Managed APIs Win

Speed to market matters more than marginal cost. Getting to product-market fit with a managed API takes weeks; building infrastructure takes quarters.
AI is a feature, not the core product. If your value proposition is your data, your workflow, or your network — not the model — then owning the model creates overhead with no strategic return.
The frontier moves fast. GPT-4 class models from 18 months ago are now commodities. If you had trained your own equivalent, you'd be maintaining legacy infrastructure while vendors leapfrog you quarterly.

A Decision Framework in Practice

Ask these four questions:

1. Does your data legally need to stay on-premises?
   YES → Build (or self-host open weights)

2. Do you have unique training data others can't access?
   YES → Fine-tune. Consider whether full training is needed.

3. Is inference cost the #1 operational expense at your scale?
   YES → Model the economics. Threshold is usually >50M req/mo.

4. Is the AI model itself your core differentiator?
   NO → Buy. Integrate. Ship faster.

The best AI infrastructure decision is the one that keeps your team focused on the problems only you can solve.