StavrynSTAVRYN
Comparison

Private AI vs. ChatGPT, cloud AI, and DIY.

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.

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Cloud AIyour data leaves YOUR NETWORK Your teamprompts & files Cloud AItheir servers Private AI · Stavrynstays in your building YOUR BUILDING Your teamprompts & files Private modelyour GPUs
Cloud vs. private — the same work; one path leaves your network, one stays inside.
Side by side

The three options at a glance.

The short version
  • Cloud AI (ChatGPT, Claude, Gemini): easiest to start, but your data leaves your network and you pay per token.
  • DIY self-hosting (PrivateGPT, Ollama, AnythingLLM): private, but you own the entire engineering and maintenance burden.
  • Managed private AI (Stavryn): the privacy and ownership of self-hosting, built and maintained for you, at a flat cost.
Cloud AIChatGPT, Claude, GeminiDIY self-hostedPrivateGPT, AnythingLLM, OllamaStavrynManaged private AI
Where your data livesTheir serversYour hardwareYour hardware
Cost modelPer token — climbs with every useHardware + your team's timeHardware you own + flat monthly fee
Compliance (HIPAA, CMMC)BAA and their controls; data still leavesEntirely your responsibilityBuilt into the design and managed for you
Who builds & runs itNobody — it's a finished productYou and your engineersStavryn, under contract
Setup effortSign up and goHigh — GPUs, the stack, tuning, hardeningNone — we handle the whole build
Customization & fine-tuningLimited to their APIFull, if you have the expertiseFull, fine-tuned on your data for you
Works offline / air-gappedNoYesYes
Shutdown / lock-in riskThey own the switchYou own itYou own it
Option 1 · Cloud AI

ChatGPT, Claude, Gemini.

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.

Option 2 · DIY self-hosted

PrivateGPT, AnythingLLM, Ollama.

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.

Option 3 · Managed private AI

Self-hosting, without owning the headache.

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.

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Common questions

Private AI vs. the cloud, answered.

Is private AI cheaper than ChatGPT?
It depends on usage, but the shapes differ. ChatGPT and other cloud tools bill per token, so the cost rises every time your team uses them. Private AI on hardware you own has an up-front hardware cost and a flat monthly fee, after which the marginal cost of a query is basically electricity. For teams that use AI heavily, the flat model usually wins over a few years, and you own the hardware at the end.
Can't I just self-host an open-source LLM myself?
You can, with tools like Ollama, PrivateGPT, or AnythingLLM. The catch is that you take on everything: sizing and buying GPUs, assembling and hardening the stack, fine-tuning, securing it for compliance, and keeping it patched and running. That is a real engineering project. Stavryn does the same thing as a managed service, so you get the privacy of self-hosting without owning the headache.
Is a private model as good as ChatGPT?
For most business work, yes. Around 80% of typical tasks run fine on open models you can host yourself, and a smaller model fine-tuned on your own data often beats a generic frontier model on your specific work. You reach for a frontier-scale model only for the small slice of work that truly needs it.
Why not just use ChatGPT Enterprise or a cloud BAA?
Those reduce the paperwork but not the exposure: your data still leaves your network to be processed on infrastructure you do not control, you still pay per use, and you are still subject to the provider changing terms or restricting a model. Private AI removes all three by keeping everything inside your building.
Does ChatGPT train on my data?
By default, the consumer tiers of ChatGPT and Claude can use your conversations to train their models, and those terms change over time. Opt-outs and enterprise plans help, but the default, and who controls it, belongs to the vendor. A private model on hardware you own never sends a prompt off your network, so there is nothing to train on and no setting to forget.
What if the vendor changes its terms, raises prices, or pulls the model?
Then you move on their schedule, not yours. Cloud providers have deprecated popular models, changed pricing, and even been ordered by courts to retain user data, all outside your control. An open model you run yourself keeps working, at a fixed cost, until you decide to change it.
How fast can a private model be up and running?
Faster than most expect. Once the hardware is specced and in place, deployment, fine-tuning on your data, and standing up the stack takes weeks, not quarters, and we run it for you afterward so it stays fast and patched.
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