Private AI
What is a private LLM? Self-hosted or private cloud endpoint, and the trade-offs
A plain guide for business and IT leaders who want a language model that works with company data without that data leaving a controlled environment. It covers the two main ways to build one, what each gives you, and what each asks of your team.
A private LLM is a large language model set up so that your prompts, documents and outputs stay inside an environment you control, and are not shared with other customers. There are two common ways to get there. You can self-host an open-weights model on your own servers or cloud account. Or you can use a hosted model through a private cloud endpoint, where a cloud provider runs the model and your traffic reaches it over a private network. Self-hosting gives the most control and the most work to run. A private endpoint is quicker to start and leaves the running of the model to the provider.
definition
What does private mean for a language model?
A private LLM is not a special kind of model. It is a way of deploying one. The word private describes where the model runs, who can see what you send it, and whether your data can be used for anything beyond answering you.
Three questions sort most setups. Where does the model run? Who can read your prompts and the answers? Can your data be used to train a model that other people use? A setup is private to the degree that you control the answers to all three.
two routes
Self-hosted model or private cloud endpoint: which one is it?
Self-hosted means you run the model yourself, on servers you own or in a cloud account you manage. You choose the model, the hardware, the network rules and the logging. Nothing leaves unless you send it out.
A private cloud endpoint means a cloud provider hosts the model and you reach it over a private connection instead of the open internet. Microsoft says it hosts the models it sells through Azure in its own Azure environment, and that they do not interact with services run by the model's original provider (Microsoft Learn, read 10 November 2026). On AWS, Amazon Bedrock lets you set up a VPC interface endpoint with AWS PrivateLink so the connection to your data is private. In both cases the provider, not you, runs the model.
comparison
How do the two compare?
Qualitative comparison only. No cost, speed or accuracy claims; those depend on the model, the workload and the contract, and were not verified for this page.
| Question | Self-hosted model | Private cloud endpoint |
|---|---|---|
| Who runs the model? | Your team, or a partner you hire | The cloud provider |
| Control over model and settings | Most: you pick the model and every setting | Less: you pick from what the provider offers |
| Choice of model | Open-weights models you are allowed to use | The provider's catalogue |
| Work to start | More: capacity, serving software, monitoring | Less: create a deployment and set up access |
| Ongoing work | Updates, scaling, uptime and security fixes are yours | The provider handles the platform; you handle access, data and use |
| Where your data sits | Where you put it | In the provider's environment; where exactly depends on the deployment type you choose |
| Our view: best fit | Strict data-location rules, or a need to control the model itself | Teams that want a private setup without running model infrastructure |
provider terms
What do cloud providers say about your data?
Read the provider's own terms for the exact service and region you plan to use. These are two examples, read on 2026-11-10.
Microsoft says that prompts, completions, embeddings and training data sent to its Foundry models sold by Azure are not available to other customers, are not available to OpenAI or other providers of those models, and are not used to train any generative AI foundation models without your permission or instruction. It also says its models store and process data to provide the service and to monitor for uses that break its terms, so a sample of prompts and completions may be selected for review, by automated means and, where needed, by human reviewers. If modified abuse monitoring has been approved for you, the human review and data storage do not apply, though automated review may still run. Global and DataZone deployment types may process prompts outside your resource's geography, and other types may process them between regions inside it. (Source: Microsoft Learn, Data, privacy, and security for Foundry Models sold by Azure, page dated 2026-05-18.)
AWS says that model providers do not have access to Amazon Bedrock logs or to customer prompts and completions, and describes using a VPC and an AWS PrivateLink interface endpoint so your data is not available over the internet. (Sources: Amazon Bedrock User Guide, Data protection, and Protect your data using Amazon VPC and AWS PrivateLink.)
tradeoffs
What are the trade-offs to weigh?
Control against effort. Self-hosting gives you the most say over the model, and also makes you responsible for running it.
Model choice against convenience. Open-weights models give you freedom to adapt them, but their licences differ and you must read each one. A provider's catalogue is easier to use but narrower.
Data location against provider reach. Some hosted deployment types let you pick the region or zone, but you rely on the provider's terms and its monitoring rules.
Private is not the same as safe. A private model can still give wrong answers, leak data through a badly set permission, or act on a malicious instruction hidden in a document. Access control, logging and testing matter in either setup.
checklist
What should you check before you choose?
Use this list in a review with your security and legal teams. It is a set of questions, not a legal opinion.
What data will the model see, and does any of it carry rules about where it may be stored or processed?
Where will prompts and answers be processed and stored, and can the provider or a deployment type move processing to another region?
Is anyone at the provider able to read your prompts, for example for abuse monitoring, and can that be changed?
Can your data be used to train a model other customers use? Get the answer in writing from the provider's terms.
Who can call the model, how is that access controlled, and are prompts and responses logged for review?
If you self-host, who owns updates, scaling and security fixes, and what is the plan if that person leaves?
How will you test answer quality and safety before and after launch?
retrieval or tuning
Do you need to fine-tune, or can the model use your documents at query time?
A private LLM can use your data in two ways. Fine-tuning adjusts the model on your examples. Retrieval looks up your documents when a question is asked and gives them to the model as context. Kriv's fine-tuning page notes that for retrieval, data typically stays in your vector stores. We recommend starting with retrieval for most first projects, because it is simpler to change and to control. Our fine-tuning page covers how to decide.
how kriv helps
How Kriv AI helps
Kriv AI is a consultancy. We help teams design and deliver language model setups where sensitive data stays in a governed environment. As our fine-tuning page states, we can architect solutions where data never leaves your governed cloud, including private endpoints, on-premises inference and self-hosted models. We never use your data for training models for other clients.
We design for your security requirements, with encryption, role-based access, audit logging and, where possible, de-identification. Our delivery aligns with frameworks like the NIST AI RMF at a practical level. This page describes our delivery practices, not certifications.
To talk through which setup fits your data and your team, book a discovery call. Scope and price are set after that call.
Sources
Cited sources
- Microsoft Learn, Data, privacy, and security for Foundry Models sold by Azure in Microsoft Foundry (read 2026-11-10)
- Amazon Bedrock User Guide, Data protection (read 2026-11-10)
- Amazon Bedrock User Guide, Protect your data using Amazon VPC and AWS PrivateLink (read 2026-11-10)
- Kriv AI, Fine tune a language model: when it pays off and what it takes (read 2026-11-10)
Straight answers
Frequently asked questions about What is a private LLM? Self-hosted or private cloud endpoint, and the trade-offs
What is a private LLM?
A private LLM is a language model deployed so that your prompts, documents and outputs stay in an environment you control and are not shared with other customers. It is a way of deploying a model, not a different kind of model.
What is the difference between a self-hosted LLM and a private cloud endpoint?
With a self-hosted LLM you run the model yourself, on your servers or your cloud account. With a private cloud endpoint a cloud provider runs the model and you reach it over a private network connection. Self-hosting gives more control and more work.
Does a private LLM mean my data is never used for training?
Not automatically. It depends on the terms of the service you use. Microsoft and AWS each publish statements on this for their services, and you should read them for your exact service and region. If you self-host, you decide.
Is a private LLM more accurate than a public one?
No. Privacy describes where the model runs and who can see your data, not how good its answers are. Accuracy depends on the model, the data you give it and how you test it.
Do I need to fine-tune a private LLM?
Often not at first. We recommend starting with retrieval, where the model looks up your documents at query time, because it is easier to change and control. Fine-tuning is worth considering later for a specific, repeated task.
How does Kriv AI help with a private LLM?
We design and deliver the setup: where the model runs, how access is controlled, and how prompts and answers are logged. We never use your data to train models for other clients. Book a discovery call to scope it.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
Book a Discovery Call