Build vs. Buy: Off-the-Shelf AI vs. Custom Solutions

This is one of the most expensive questions to get wrong — so let me give you a framework that’ll save your team months of debate.

Default to buy. Seriously. For most mid-market companies, off-the-shelf AI tools cover 80%+ of your use cases right now. ChatGPT/Claude for writing and analysis. Copilot for coding. Fireflies for meeting notes. These tools have thousands of engineers behind them and they’re getting better every month. Building your own version of something that already exists is almost never the right call.

When buying makes sense:

• The problem is common (many companies have it)
• An established tool already solves it well
• You need it working this quarter, not next year
• Your team doesn’t have ML/AI engineering expertise
• The data isn’t highly proprietary or regulated

When building makes sense:

• The problem is unique to your business and no tool addresses it
• You have proprietary data that creates a real competitive advantage when combined with AI
• You need deep integration with internal systems that no vendor supports
• You have (or can hire) the engineering talent to build and maintain it
• You’ve already tried buying and hit hard limitations

The middle path most teams miss: customize a bought tool. Use an off-the-shelf LLM via API but wire it into your own data (RAG — we’ll cover this tomorrow). This gives you 90% of the value of a custom build at 10% of the cost. Most companies that think they need to “build AI” actually need to “connect AI to our data.”

The real trap: Building AI for the resume value instead of the business value. Custom AI sounds impressive in board meetings. It’s also how you end up with a team of 3 expensive engineers maintaining a system that works worse than what you could buy for $50/month.