3.1.2. Build, Buy, or Partner

💡 First Principle: Spend scarce talent and time only where they create differentiation. If a capability is the same for you as for everyone else, buy it; if it depends on your unique data, process, or customer insight and you can sustain it, consider building it; if you need speed or skills you lack, partner. The sourcing decision is about where advantage comes from, not about who owns the code.

The three options at a business level:

• Buy: adopt an existing product, for example a SaaS application with embedded AI, Amazon Quick for AI-assisted analysis and automation, or a third-party model or AI product purchased through AWS Marketplace (which consolidates billing through the AWS account and simplifies procurement). Fastest time to value, lowest build risk, least differentiation, vendor dependency.
• Build: create the solution internally, either on a managed platform (for example, building a generative AI application on Amazon Bedrock with your data and prompts) or by developing custom models (for example, on Amazon SageMaker AI). Highest control and potential differentiation; highest demand for talent, time, and ongoing ownership (monitoring, retraining, governance).
• Partner: work with a consultancy, systems integrator, AWS Partner, or specialist vendor to co-develop or deliver. Brings expertise and speed; requires careful scoping, knowledge transfer, and clarity on IP ownership and long-term support.

Note that "build" exists on a spectrum. Building an assistant on Bedrock (using existing foundation models, your documents, and your prompts) is a much smaller build than training a new model in SageMaker AI. Many strategic "builds" today are assemblies of managed services.

Factors the exam guide names, and the questions they raise:

• Budget: What is the full cost over three years, not just year one? Building carries ongoing costs (team, monitoring, retraining). Buying carries subscription and usage costs that scale with adoption.
• Timelines: When must value appear? A competitor launching in six months may rule out a twelve-month build.
• Capabilities: Do we have the data, AI talent, and operating maturity to build and run this? Many organizations can build a prototype but cannot sustain a production system.
• Vendor proposals: Do vendor claims hold up? Ask for evidence on your data, references, pricing at your projected volume, roadmap, data handling terms (is our data used for training?), exit terms, and IP indemnification.
• Regulatory compliance requirements: Can the option meet data residency, privacy, auditability, and sector rules? A product that cannot keep data in the required region or cannot explain decisions may be disqualified regardless of price.

Additional considerations include lock-in and portability (how hard is it to switch later?), integration with existing systems, and time to production versus time to prototype.

⚠️ Exam Trap: Building custom is not automatically the strategic answer. When a capability is common across the industry and products exist that meet compliance requirements, buying is usually best. Build is favored when the capability depends on proprietary data or processes that create differentiation and the organization can sustain it. Partner is favored when speed or expertise is the gap.

Reflection Question: A mid-sized insurer wants (a) meeting transcription and summarization for all staff and (b) a pricing model based on its 30 years of proprietary claims data. Apply the build-buy-partner logic to each, including at least two of the exam guide's factors.