The Executive's Dilemma

In Module 4, we introduced the concept of Managed AI vs. Custom ML. Now, as an AI Practitioner, you must be able to justify this decision to a CFO or a CTO. Success isn't just about "Making it work"; it's about making it work efficiently.

Usually, the decision to "Build" or "Buy" is defined by four major factors: Time, Scale, Domain, and Talent.

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1. The "Buy" Strategy (Managed Services)

The Buy Logic: "I want to solve this common problem as fast and cheaply as possible so I can focus on my core business."

Strategic Indicators to BUY:

• Low Differentiation: If you want to translate a manual, having a "Better translation engine" doesn't actually help you sell more products. Just use Amazon Translate.
• Urgency: You need to launch a feature by next Friday. You don't have time to train a model.
• Limited Talent: You have 3 web developers but no one knows what a "Hyperparameter" is.

Total Cost of Ownership (TCO): High per-unit cost (pay per API call), but Zero maintenance cost and Zero R&D cost.

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2. The "Build" Strategy (SageMaker / Custom ML)

The Build Logic: "This specific AI model is our 'Secret Sauce'. If we don't own it, we don't have a competitive advantage."

Strategic Indicators to BUILD:

• High Differentiation: You are a healthcare company and your "Cancer detection algorithm" is why people choose you over a competitor. You MUST build that.
• Unique Data Domain: You are analyzing data that doesn't exist on the public internet (e.g., secret satellite frequencies).
• Extreme Scale Savings: If you are at a scale of 10 billion events a day, the margin you save by running your own optimized model on AWS Inferentia might pay for your entire data science team.

Total Cost of Ownership (TCO): High Upfront cost (salary of data scientists + GPU time), but Lower per-unit cost at extreme volumes.

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3. The "Build-on-Top" Strategy (The Bedrock Middle Ground)

Amazon Bedrock represents a new "Third Way."

• You Buy access to the model (The FM).
• You Build the context around it (RAG). This is often the "Goldilocks" solution for 80% of enterprise GenAI projects.

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4. Visualizing the Decision Framework

graph TD
    A[AI Requirement] --> B{Does it differentiate our BRAND?}
    B -->|No: It's just a feature| C[BUY: Managed AI Service]
    B -->|Yes: It is our CORE value| D{Do we have clean data?}
    D -->|No| E[Buy/Partner for data first]
    D -->|Yes| F{Do we have ML Engineers?}
    F -->|No| G{Use SageMaker Canvas or Autopilot}
    F -->|Yes| H[BUILD: SageMaker Custom Model]
    
    C --> I[Low Risk / High Speed]
    H --> J[High Risk / High Reward]

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5. Summary: Follow the Focus

• Managed Service: Buy "Speed."
• Custom Model: Build "Value."
• Bedrock (RAG): Combine both.

A Practitioner always favors the Managed option first. You should only "Build" when you can prove that the managed option is technically incapable or economically foolish.

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Exercise: Identify the TCO Factor

A startup wants to use AI to summarize legal documents. They have a budget of $10,000 and 2 weeks to launch a MVP (Minimum Viable Product). They found that building a custom model would cost $100,000 and take 4 months. Why is "Buying" (using Bedrock/Claude) the correct choice here?

• A. Data Privacy.
• B. Opportunity Cost (Time to Market).
• C. High Differentiation.
• D. Domain Specificity.

The Answer is B! The "Opportunity Cost" of waiting 4 months would be fatal for a startup. Buying the managed service allows them to launch instantly and start learning from customers.

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Knowledge Check

What's Next?

Deciding how to build is easy. Deciding what to build is hard. In the next lesson, we see how to spot the "Dumb" AI ideas. Find out in Lesson 2: Identifying High-Impact AI Opportunities.