Custom vs. Off-the-Shelf AI: A Hard Look at Total Cost of Ownership

Custom vs. Off-the-Shelf AI: A Hard Look at Total Cost of Ownership

K
Kaprin Team
Jan 12, 202610 min read

The easiest way to get AI is to click "Enable" in your existing software. Salesforce Einstein, Microsoft Copilot, Notion AI. It takes 5 seconds.

But convenience has a cost. Usually, that cost is a per-user upcharge. $30 here. $50 there. Suddenly, your software bill has doubled.

The TCO (Total Cost of Ownership) Trap

Let's do the math for a 500-person company:

  • Microsoft Copilot: $30/mo * 500 = $180,000/year.
  • Salesforce AI: $50/mo * 100 sales reps = $60,000/year.
  • Notion AI: $10/mo * 500 = $60,000/year.

Total: $300,000/year. And this price will never go down. It is a rent that scales linearly with your headcount.

The "Build" Alternative

What if you took that $300,000 and built a custom "Company Intelligence Layer"?

You could build a unified RAG system that connects to Sharepoint, Salesforce, and Notion simultaneously. You pay for the inference (tokens) or the GPU, not the seats. If you use open-source models (Llama 3), your running costs might be $20,000/year.

Yes, the upfront build cost is higher ($100k-$150k). But the "Payback Period" is often less than 9 months. After that, you own the asset.

The "Data Moat" Argument

When you use off-the-shelf AI, you are training their generic model. When you build custom AI, you are fine-tuning your model. You are capturing the unique IP of how your company works. This becomes a defensive moat that competitors cannot buy.

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