Private AI is the approach of running AI models, typically large language models and the systems around them, in an environment your organization controls, so that prompts, documents and outputs are handled on infrastructure and terms you set. That environment might be your own data center, colocation space, dedicated bare metal or GPU servers from a provider, or an isolated private environment in a public cloud. It contrasts with sending data to a shared, multi-tenant AI service run entirely by a third party.
There is no single industry definition of private AI. This entry uses it to mean a private or self-hosted deployment under the buyer’s control. Vendors also market hosted offerings as “private AI” when they add tenant isolation, private network connections or commitments not to train on customer data. Those can be reasonable choices, but whatever the label, buyers should verify isolation, tenancy, data use, access and where processing happens.
At a glance
- The term has no single definition: here it means a deployment under your control, though some vendors apply it to isolated hosted services, so check what is behind the label.
- Deployment options range from on-premises servers to colocation, dedicated hosted GPUs and isolated cloud environments.
- It gives more say over data location, retention, access and model choice, but you take on more operational and security work.
- Cost trade-offs depend on volume: higher fixed costs, potentially lower cost per request at steady high use.
- Model availability varies; not every model can be run outside its provider’s service.
What problem it solves
Public AI services are quick to start with, but they mean sending data to an outside provider under its terms. For organizations handling regulated, confidential or contractually restricted data, that can be hard to approve. Legal and security teams want to know where data is processed, how long it is kept, who can access it and whether it is used to train models, and customers increasingly ask the same.
Private AI addresses this by bringing the model to the data instead of sending the data out. It can also give predictable costs at high volume, the freedom to choose and tune models, and independence from a single AI provider’s roadmap and pricing. For some organizations, it is also a way to offer staff an approved alternative that reduces shadow AI.
How it works
Models. You select a model, often a large language model (LLM) available under an open or commercial license that permits self-hosting, and may adapt it to your needs. Many deployments connect the model to internal documents so it can answer from your own information.
Compute. Running models at useful speed usually needs GPUs or other AI accelerators. Options include buying servers for your data center or colocation, renting dedicated bare metal GPU servers, using GPU as a Service (GPUaaS), or using isolated capacity in a public cloud.
Software stack. Around the model sit serving software, a retrieval layer for your documents, access controls tied to your identity system, logging, monitoring and user-facing applications. Some vendors package this as a private AI platform.
Operations. Someone has to patch, secure, monitor and update the models and infrastructure, either your team or a managed service provider.
Buyers should verify in detail where inference runs, where prompts and logs are stored, whether any data reaches outside services (for example for telemetry or support), and who at the provider can access the environment. Private is a matter of configuration and contract, not a label.
When it matters for buyers
- When data can’t go to a public AI service. Regulated, highly confidential or contractually restricted data is the most common driver.
- When data residency or data sovereignty applies. Private AI lets you choose location and operator, though meeting a specific rule depends on more than location.
- When AI usage is high and steady. Fixed infrastructure can become cheaper than paying per use.
- When planning data center or cloud capacity. AI hardware can need far more power and cooling per rack than typical servers; check what your facility or provider supports. Our private cloud and bare metal overviews cover hosting options.
- When setting AI governance. Private deployments still need policy, access control and output review.
Questions to ask vendors
- Where exactly do inference, storage, logs and backups run, and does any data leave that environment?
- Who at your company can access the environment, the models or our data, and under what conditions?
- Which models can we run, under what licenses, and can we bring our own?
- What GPU or accelerator capacity is dedicated to us, and what happens when we need more?
- Who is responsible for patching, securing and updating models and infrastructure?
- What are the power, cooling and space requirements if we host the hardware?
- How does total cost compare with pay-per-use services at our expected volume?
How it differs from GPU as a Service (GPUaaS)
GPU as a Service (GPUaaS) is rented GPU computing capacity, a type of infrastructure. Private AI is an approach to deploying AI so you control the data and environment. GPUaaS can be one building block of private AI, if the capacity is dedicated or isolated enough and the contract gives you the control you need, but renting GPUs doesn’t by itself make an AI deployment private, and private AI can also run on hardware you own.