Research · AI Strategy & Adoption

Build vs. Buy

AI can be bought as a finished product, assembled from platforms and model APIs, or built and trained in-house. The right choice depends on how differentiating the capability is, the data involved and the skills you have.

The main options

• Buy: AI features in SaaS products or specialized AI applications. Fastest to deploy, least control.
• Assemble: build applications on model APIs or cloud AI platforms, using your own data through retrieval or light customization.
• Build: train or substantially fine-tune your own models. Most control and cost, and requires specialized skills.

Questions that decide the answer

• Is this capability a source of competitive advantage or a commodity?
• Does it depend on proprietary data that a vendor cannot access?
• What are the data handling, residency and security requirements?
• Do we have the skills to build and operate it over time?
• How much lock-in are we willing to accept?

Why it matters

Model capabilities and prices change quickly. A capability that required custom work a year ago may now be a standard feature, and vice versa. Decisions that lock an organization into one approach can become expensive. Many organizations therefore buy for common needs, assemble for differentiated ones and build only where data or requirements are truly unique.

Common pitfalls

• Building what vendors already provide well.
• Buying tools whose data terms do not meet your obligations.
• Underestimating the ongoing cost of operating custom models.
• Designing around a single model provider with no exit path.

How to get started

• Classify each use case as commodity or differentiating.
• Review vendor data use, security and exit terms before buying.
• Keep a model abstraction layer so providers can be swapped.
• Revisit decisions periodically as the market changes.