For a campus of any size, the difference between per-seat “retail AI” and an institution-owned application layer is the difference between a line item and a strategy. Per-seat tools such as those offered by OpenAI look convenient at $20/user/month—until you multiply. An owned layer (your assistants, your sources, your policies) uses developer-rate APIs under the hood, so you pay for actual usage, not for every person who might click “New Chat.” Add code and data ownership, and the total cost of ownership (TCO) tilts even further in your favor.

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The Simple Math

• Retail, per-seat pricing: $20/user/month × headcount × 12 months.

• Owned application layer with mentorAI: Low six-figure campus license (e.g., ≈$200k–$300k) + developer-rate LLM usage (billed on tokens, at API prices) + infrastructure of your choice (we can host or you can) + unlimited users.

Two Quick Breakevens

• If your campus license is $300k, retail parity is ~1,250 users (because $20 × 12 = $240 per user-year; $300,000 ÷ $240 ≈ 1,250). Above that, a per-seat model is more expensive—before you add any growth.

• If your license is $200k, retail parity is ~834 users.

Either way, once you serve more than a small sub-program, the owned model wins—and keeps winning as adoption grows.

A Real-World Scale Example (From Our Conversations)

• Headcount: ~50,000 learners

• Retail per-seat: $20 × 50,000 × 12 = $12,000,000/year

• Campus-owned layer: ≈$300,000/year license (plus API usage at developer rates and infra)

That’s the difference between a multi-million annual outlay and a budget that leaves room for enablement, content, and research. Key nuance: developer-rate APIs are model-dependent and billed per-token. Institutions can bring their own keys and pay cloud/model vendors directly. You can also route to cost-efficient models for routine tasks and reserve higher-end models for specialized work—all without changing your campus app layer.

Why CFOs Prefer The Owned Model

• Predictable TCO. A single campus license avoids per-seat growth penalties. Usage is right-sized via API routing and token budgets.

• Capex/opex flexibility. Host with us or in your cloud (AWS, GCP, Azure, on-prem). Either way, you’re not forced into a single vendor’s stack.

• No vendor lock-in. You own the application layer, codebase, and data. If pricing or policy shifts elsewhere, you’re insulated.

• Multi-LLM leverage. One abstraction over OpenAI, Gemini, Anthropic, and others lets you price-shop and swap models as markets change—without refactoring your campus apps.

• Operational focus. Your spend funds faculty enablement and student impact (workshops, office hours, one-on-ones), not shelfware licenses.

What The Owned Layer Buys You (Beyond Dollars)

• Institutional control. Factory defaults that “just work,” with deep prompt/pedagogy settings when instructors want them.

• Scoped safety. Domain-bound assistants that refuse out-of-scope questions, on top of base-model alignment.

• Granularity that matters. Per-course and per-student mentors—so the experience matches the class, the level, and the learner.

• Cited answers by design. Assistants point back to your slides and readings to build trust and drive deeper study.

• Adoption that compounds. Our hands-on model (group workshops, drop-in office hours, one-on-ones) moves faculty from “trying a chatbot” to shipping course-ready assistants.

Procurement Patterns That Work

• Five-figure pilots scoped to priority courses or services (often co-funded with cloud partners), to gather data through action rather than endless bake-offs.

• University-wide licensing in the low six figures when you’re ready to expand—covering tutoring, content creation, advising, and operations across schools and programs.

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In Conclusion

If you’re weighing $20/user/month against an owned campus layer, let’s run your numbers. Reach us at ibl.ai/contact—we’ll model your breakeven and stand up a pilot that proves impact before you scale.