There are three ways to put AI to work in your business. Here is an honest look at how managed private AI compares to cloud tools like ChatGPT and to rolling your own — on the things that actually matter: data, cost, compliance, and who keeps it running.
| Cloud AIChatGPT, Claude, Gemini | DIY self-hostedPrivateGPT, AnythingLLM, Ollama | StavrynManaged private AI | |
|---|---|---|---|
| Where your data lives | Their servers | Your hardware | Your hardware |
| Cost model | Per token — climbs with every use | Hardware + your team's time | Hardware you own + flat monthly fee |
| Compliance (HIPAA, CMMC) | BAA and their controls; data still leaves | Entirely your responsibility | Built into the design and managed for you |
| Who builds & runs it | Nobody — it's a finished product | You and your engineers | Stavryn, under contract |
| Setup effort | Sign up and go | High — GPUs, the stack, tuning, hardening | None — we handle the whole build |
| Customization & fine-tuning | Limited to their API | Full, if you have the expertise | Full, fine-tuned on your data for you |
| Works offline / air-gapped | No | Yes | Yes |
| Shutdown / lock-in risk | They own the switch | You own it | You own it |
The easiest to start with, and the hardest to live with once your data is sensitive.
Cloud AI is a finished product: sign up and go. But every prompt and document leaves your network to be processed on servers you do not control, the bill grows with every token your team spends, and you inherit the provider's risk — prices change, terms change, and a model can be restricted or retired overnight. A business-associate agreement documents the exposure; it does not remove it.
The right instinct — keep it in-house — with the entire engineering burden on you.
Self-hosting an open model fixes the privacy problem: nothing leaves the building. But you become the AI infrastructure team. You size and buy the GPUs, assemble and harden the stack, fine-tune on your data, lock it down for your compliance posture, and keep it patched and running. For a company without a dedicated ML and DevOps bench, that is where most private-AI projects stall.
Stavryn sits exactly between the other two: the privacy and ownership of self-hosting, delivered as a service so you never touch the hard parts.
We size the hardware, assemble and harden the open stack, fine-tune it on your documents, build the compliance posture in, and run it under contract. The model and your data stay on hardware you own; the marginal cost of a query is basically electricity; and nobody else holds the switch. You get the result of a self-hosted system without becoming an AI infrastructure company to get there.
