🎯 The 3 Main Options (Plain English)

1) Buy (SaaS / Off-the-Shelf)

You pay a monthly subscription for a finished product. (e.g., ChatGPT Team, Jasper, Microsoft 365 Copilot).

• Pros: Instant access, no maintenance, usually cheaper upfront.
• Cons: Generic features, data privacy depends on their policy, less control.

2) Build “Light” (API Wrapper)

You build a custom interface that connects to a major model (OpenAI, Anthropic) via API.

• Pros: Custom workflow, your own branding, you control the system prompt.
• Cons: Requires developers to maintain, pay-per-use costs can spike.

3) Build “Heavy” (Custom Model / Hosting)

You train your own open-source model (Llama, Mistral) and host it on your own servers.

• Pros: Total data privacy, total control, no API dependency.
• Cons: Very expensive, requires ML engineers and GPUs. (Rarely needed for beginners).

🧭 The Decision Framework: 4 Questions to Ask

Before you hire a developer, ask these four questions.

1) Is the problem unique to us?

• No (Standard): Writing emails, summarizing meetings, coding help.
  → BUY. Don’t build your own email writer; Google and Microsoft already did.
• Yes (Unique): Generating quotes using your proprietary pricing logic and obscure industry codes.
  → BUILD. No SaaS tool knows your secret sauce.

2) Do we have “Builder” talent?

• No: We have no engineers.
  → BUY. Building AI isn’t just writing code; it’s fixing it when the model drifts or the API changes.
• Yes: We have a dev team.
  → BUILD (Light). Use APIs to solve specific problems.

3) How sensitive is the data?

• Low/Medium: Marketing drafts, public data.
  → BUY (with a good privacy policy).
• High/Regulated: Patient records, defense secrets.
  → BUILD (Private hosting or Enterprise agreements).

4) Is AI the product, or just a feature?

• Feature: “We want to add a chatbot to our HR portal.”
  → BUY (or use a low-code builder).
• Product: “We are selling an AI legal analyst.”
  → BUILD. You can’t rely entirely on a third-party UI for your core value.

⚠️ The “Commodity Trap” (Don’t Build This!)

The biggest mistake companies make is building tools that are about to become free features.

Example: In early 2023, many startups built “PDF summarizers.” Six months later, ChatGPT and Claude added file uploads for free. Those startups died.

Rule: If a general-purpose model (GPT-5, etc.) is likely to do it natively in 12 months, do not build it. Just wait or buy.

✅ A Practical Strategy Roadmap

Phase 1: Buy to Learn (Month 1-3)

Subscribe to ChatGPT Team or Claude. Let your team experiment. Find out what they actually use AI for.

Phase 2: Identification (Month 4)

Identify the one workflow that is high-value but frustrating in the chat interface. (e.g., “We paste the same prompt 50 times a day for customer support”).

Phase 3: Build a “Thin Wrapper” (Month 5+)

Build a simple internal tool using the API for that specific workflow. Lock down the prompt so users can’t mess it up. Connect it to your data (RAG).

🔗 Keep exploring on AI Buzz

• AI Vendor Due Diligence (If You Buy)
• Fine-Tuning vs RAG (If You Build)
• AI Change Management (How to Roll Out)
• Preventing Runaway Costs (If You Use APIs)

🏁 Conclusion

Building AI is exciting, but buying AI is often smarter.

Buy for standard tasks (email, coding, chat). Build for unique data workflows that give you a competitive edge. Don’t reinvent the wheel—invent the engine.