Private AI vs public AI APIs: how to keep company data safe

Every team adopting AI eventually asks the same question: is it safe to put our data into this? The answer depends on what the data is and which kind of AI service you use.

What is the difference between public and private AI?

A public AI API is a model hosted by a provider. You send a prompt over the internet, the provider's servers process it, and you get a response back.

A private AI deployment runs a model on infrastructure you control. Prompts and documents stay inside your environment, under your access rules and logging.

When is a public AI API fine?

Hosted AI is a reasonable choice when:

The data is not sensitive, such as public marketing content or general research.
You use a business plan whose terms state your data is not used for training.
Your contracts, regulators and customers do not restrict where data is processed.

For many companies this covers a large share of everyday AI use.

When do you need private AI?

Private AI is worth considering when you work with:

Health information covered by HIPAA.
Financial or customer records with contractual confidentiality requirements.
Legal documents and privileged communications.
Proprietary source code or trade secrets.
Public sector data with residency or handling rules.

In these cases the question is not only whether a provider is trustworthy, but whether you can prove to auditors and customers exactly where data went.

What does a private AI deployment involve?

A practical private AI setup has four parts:

The model. An open model sized to your workload, from small models that run cheaply to very large ones that need dedicated GPUs.
The infrastructure. GPU servers in your cloud account or data center, containerized so they are repeatable and maintainable.
The interface. A chat interface or API your team can use without learning anything new.
Access control. Role-based permissions and logging, so each person sees only what they should.

We built exactly this for a 15-person engineering team: a 120-billion-parameter model behind a familiar chat interface, with role-based access and zero data leaving the environment. See the case study.

Can you combine both?

Yes, and many teams should. Route sensitive work to the private model and general tasks to a hosted service. The key is making that routing a deliberate policy rather than leaving it to each employee.

Next step

If you are unsure which category your data falls into, that is a good first conversation. Talk to us or read about our AI services.

Frequently asked questions

What is private AI?

Private AI means running a large language model inside infrastructure you control, such as your own cloud account or data center, so prompts, documents and outputs never pass through a third-party AI provider.

Are open models good enough compared to commercial AI services?

For many business tasks, yes. Large open models handle summarization, drafting, question answering and coding assistance well. For the most demanding reasoning tasks, top commercial models may still perform better, so some teams use both.

Is private AI more expensive?

Setup costs more because you run the infrastructure, often including GPUs. For steady, high-volume use the running cost can be competitive with per-request API pricing, and it removes data exposure risk.