10.1 Build vs Buy Analysis¶
Decision Framework¶
graph TD
A{"Core competency<br>in biometric AI?"} -->|"Yes"| B{"Budget for<br>5+ person ML team<br>(ongoing)?"}
A -->|"No"| C["BUY (SDK/API)"]
B -->|"Yes"| D{"Regulatory need<br>for full control?"}
B -->|"No"| C
D -->|"Yes"| E["BUILD in-house"]
D -->|"No"| F["HYBRID<br>(Buy SDK +<br>customize)"]Comparison¶
| Factor | Build In-House | Buy (SDK/API) | Hybrid |
|---|---|---|---|
| Time to market | 12-24 months | 2-4 weeks | 2-6 months |
| Initial cost | $500K-2M | $50K-200K | $200K-500K |
| Annual cost | $300K-1M (team) | $100K-500K (license) | $200K-600K |
| Team required | 5-10 ML engineers | 1-2 integration engineers | 2-4 engineers |
| iBeta certification | Your responsibility ($30K-80K) | Vendor provides | Shared |
| Model updates | Your responsibility (continuous) | Vendor provides | Shared |
| Customization | Full control | Limited | Good |
| Data sovereignty | Full control | Depends on vendor architecture | Better control |
| Risk | High (technical + regulatory) | Low (vendor responsibility) | Medium |
When to Build¶
Build If:
- You have 100M+ annual verifications (cost efficiency at scale)
- Biometric AI is a core business differentiator
- Regulatory requirements demand full data and model control
- You have established ML infrastructure and team
When to Buy¶
Buy If:
- You need to launch within 3 months
- Annual verification volume < 10M
- You don't have ML engineering capacity
- You want proven iBeta-certified solution