The Short Answer
Private AI is priced on a flat license plus the compute (GPU) you run it on — not per user. That's the structural difference from per-seat SaaS, and it's why private AI gets cheaper per user as you scale, while per-seat gets more expensive. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing.
With ibl.ai you self-host the full source code and pay for a flat institutional license and the GPU your workloads actually use — there is no $30–60/user/month fee that grows with headcount. You also run any model (Claude, GPT, Gemini, or open-source), so you can route to a cheaper model per task.
Per-seat SaaS (ChatGPT Enterprise, Microsoft Copilot, Glean) scales linearly with employees regardless of use. Above ~100 users that shape is wrong: you pay the 100% to subsidize the 20% who actually use it daily.
What Drives Private AI Cost?
Private AI cost has three components, none of which is headcount:
Platform license — a flat fee for the software you deploy and own, independent of how many people use it.
Compute (GPU) — the dominant variable cost. You pay for the GPUs that run inference, sized to actual usage; idle capacity costs nothing extra.
Model usage — if you route to a commercial model (Claude, GPT, Gemini) via private deployment, you pay that model's token price; if you run an open-weight model (Llama, Nemotron, Qwen) on your own GPUs, there is no per-token fee at all.
Because the bill tracks software, compute, and tokens — not seats — the per-user cost falls as you add users.
Private AI vs. Per-Seat SaaS: The Math at Scale
The shape of the bill is the whole story. Per-seat SaaS is linear in headcount; private AI is flat-plus-usage. Here is the same workload at three organization sizes:
| Organization size | Per-seat SaaS @ ~$40/user/mo | Annual |
|---|---|---|
| 500 users | $20,000/mo | $240,000 |
| 2,500 users | $100,000/mo | $1,200,000 |
| 10,000 users | $400,000/mo | $4,800,000 |
| ibl.ai (self-hosted private) | flat license + GPU | does not scale per seat |
The flat-plus-GPU line stays roughly constant as headcount grows, so at 10,000 users private AI is typically 10–100× cheaper than per-seat for the same workload.
Self-Hosted vs. Managed Private AI Pricing
"Private AI" is sold two ways, and they price differently.
Managed private AI (a single-tenant or VPC deployment the vendor operates) removes the per-seat problem but you still rent access and don't own the code — pricing is a managed-service fee plus compute.
Self-hosted private AI (you deploy and own the platform) is a flat license plus your own GPU. It is the lowest long-run cost and the only option that gives full source-code and data ownership.
ibl.ai is the self-hosted ideal: you own the stack and pay only for the license and the GPU. For organizations weighing total cost over three years, owning the platform beats renting it once usage is steady.
Frequently Asked Questions
Is private AI cheaper than ChatGPT Enterprise or Copilot?
Above roughly 100 users, yes — usually by a large margin. Per-seat SaaS scales with headcount; private AI's flat-plus-GPU cost does not, so the gap widens as you grow.
What's the biggest cost in a private AI deployment?
GPU compute, sized to actual inference load. Running open-weight models on your own GPUs removes per-token model fees entirely.
Do I need to buy GPUs to run private AI?
No. You can run private AI in your own cloud tenant (VPC) on rented GPU instances, or on-premise on hardware you own — whichever fits your residency and cost profile.
Related: What Does AI Actually Cost in 2026? Latest LLM Pricing + Per-Seat Math · What Is Private AI? Models, Deployment & Ownership