Loading...
Taking longer than expected.
Reload the pageTaking longer than expected.
Reload the pageA private LLM is a large language model that runs inside infrastructure you control - your data centre, your private cloud, or a fully offline network - so prompts, documents and outputs never leave your environment. This guide explains how private AI works, the four ways to deploy it, why enterprises are moving to it, and what a production deployment actually requires.
A precise definition first, because the term gets stretched to cover everything from a laptop demo to a sovereign cloud.
Definition
A private LLM is a large language model deployed inside infrastructure an organisation controls - on-premises servers, a private cloud (VPC), or an air-gapped network - rather than accessed as a shared public service. Prompts, retrieved documents and model outputs stay within the organisation’s security boundary, under its own access controls, audit logging and compliance policies, and are never used to train third-party models.
The term covers more than self-hosting. A private LLM can be an open-weight model such as Llama or Mistral running on your own GPUs, a commercial model consumed through a dedicated endpoint inside your cloud tenancy, or a small local model running entirely on a single workstation. What makes it private is the boundary: the organisation, not the model vendor, decides where data flows, who can access the system, and what records exist.
That boundary is the whole point. Public AI assistants are operated as shared, multi-tenant services: your staff’s prompts travel to someone else’s infrastructure, are processed under someone else’s terms, and on consumer plans may be retained or used to improve future models unless someone remembers to opt out. A private LLM delivers the same capability - chat, document analysis, retrieval-augmented answers, agents - behind your SSO, on your network, with your audit trail. Related terms such as private AI and private GPT describe the same architecture from different angles: private AI is the broad strategy, a private GPT is the ChatGPT-style assistant your staff see, and the private LLM is the model underneath it.
Public LLM services are the fastest way to start. Private LLMs are how enterprises stay in control. Here is the honest comparison.
| Dimension | Public LLMConsumer tools and shared SaaS APIs | Private LLMDeployed inside your boundary |
|---|---|---|
| Data control | Prompts are processed on shared third-party infrastructure; consumer tiers may use inputs to train future models | Prompts, documents and outputs stay inside your security boundary and are never used for training |
| Data residency | The provider chooses processing regions; residency guarantees vary by plan and provider | You choose exactly where the model runs - a specific country, your own data centre, or a fully offline network |
| Compliance | You inherit the provider's controls; audit evidence is limited to what the provider chooses to expose | Controls, logging and evidence live in your environment and map directly to HIPAA, GDPR, SOC 2 and the EU AI Act |
| Cost model | Per-seat or per-token pricing that scales linearly with usage, forever | Infrastructure plus platform costs; heavy usage gets cheaper per query as utilisation rises |
| Model choice | Locked to one vendor's models, behaviour changes and deprecation schedule | Any open-weight or commercial model - Llama, Mistral, Qwen, Gemma, DeepSeek and more - swappable as the field moves |
| Latency & operations | Nothing to operate, but latency and uptime depend on the provider and the public internet | You operate the stack (or deploy a managed golden image); local inference removes internet round-trips |
Neither column wins universally. A public LLM service is the right answer for low-stakes experimentation with non-sensitive data, and enterprise tiers of public services do offer contractual protections that consumer tiers lack. The private column wins as soon as the inputs include customer records, source code, financial data, health information or anything a regulator, court or competitor should never see - which, in practice, is most of what an enterprise wants AI to work on.
Private does not mean one thing. There are four distinct deployment models, with different cost, control and operational profiles.
The model and the full serving stack run on hardware you own, inside your own data centre and behind your firewall. Maximum control over data, hardware and network paths, with capacity you size yourself.
Best for: regulated enterprises with existing data centre and GPU capacity
The stack deploys into your AWS, Azure or GCP virtual private cloud, optionally alongside dedicated model endpoints such as Azure OpenAI or AWS Bedrock. Cloud elasticity, but inside your own tenancy and compliance boundary.
Best for: cloud-first teams that need control without buying hardware
Zero external network dependencies. Model weights, inference, retrieval and the user interface all operate inside an isolated network that never touches the internet. Updates arrive by controlled media transfer.
Best for: defence, government and critical infrastructure
Open-weight models run on individual workstations or a small local server via Ollama or LM Studio. The fastest, cheapest way to pilot private AI - and a genuine production option for small teams with modest workloads.
Best for: pilots, developers and small teams proving the use case
Areebi supports all four deployment models
One golden image deploys on-premises, into your VPC, fully air-gapped, or alongside local models served through Ollama - so the deployment model is a choice, not a migration.
The shift to private AI is not theoretical caution. It is a response to documented incidents, government bans and measurable data leakage.
In May 2023, Samsung banned generative AI tools on company devices after engineers pasted internal source code into ChatGPT. Once sensitive data enters a public service, the organisation cannot retrieve it, audit it or prove what happened to it.
In February 2025, the Australian Government banned DeepSeek from all government systems and devices on national security grounds. When governments treat foreign-hosted models as an unacceptable risk for their own data, enterprises handling comparable data draw the same conclusion.
Cyberhaven’s analysis of workplace usage found that 4.2% of workers had pasted company data into ChatGPT, including client records and source code. Blocking the tools does not remove the demand; it pushes usage onto personal devices where no control applies.
Underneath the incidents sits a structural driver: regulation. Frameworks such as the EU AI Act, HIPAA, GDPR and the Australian Privacy Act expect organisations to know where regulated data is processed, to control who accesses it, and to produce records when asked. Those obligations are difficult to evidence when prompts disappear into a shared public service, and straightforward when the entire AI stack runs inside infrastructure you already govern.
The pattern across these drivers is consistent. Companies do not go private because public models are bad - they go private because ungoverned AI usage creates risk they cannot measure, and a private LLM turns that unmeasurable risk into an auditable system.
Running a model is the easy part. A production deployment is the model plus six other capabilities, and most DIY projects stall on the other six.
A way to run open-weight models (vLLM, Ollama) and reach commercial models through private endpoints, with the freedom to switch as models improve.
Areebi routes across 30+ LLM providers, including fully local models via Ollama, behind one consistent interface.
See it on the platformDocument ingestion, chunking, embeddings and vector search, so the model answers from your knowledge base rather than guessing.
Built-in RAG and document processing on a hardened AnythingLLM workspace, connected to SharePoint, Google Drive, Confluence and S3.
See it on the platformSAML/OIDC single sign-on, multi-factor authentication, role-based access and workspace isolation by data classification.
Enterprise SSO (SAML/OIDC), MFA and per-workspace RBAC are part of the golden image, not an add-on project.
See it on the platformReal-time scanning of prompts and responses for PII, PHI, credentials and custom patterns, applied before anything reaches a model.
Real-time DLP with under-10ms latency, covering PII, PHI, financial data, API keys and custom detection patterns.
See it on the platformAn immutable record of every prompt and response, attributable to an identity, that survives investigations and regulator review.
Immutable audit trails with cryptographic integrity verification and one-click evidence export for auditors.
See it on the platformMachine-enforced rules about who may use which model, for which data, under which conditions - not a PDF policy nobody reads.
A no-code visual policy builder with pre-built compliance templates and automatic, real-time enforcement.
See it on the platformUsage metrics, cost attribution by team and model, risk scoring and anomaly detection across the whole estate.
Real-time dashboards for usage, cost attribution, risk scoring and anomaly detection across every workspace.
See it on the platformThe checklist is also a useful evaluation lens: whatever you deploy, ask which of the seven capabilities it covers and who is responsible for the rest. An inference server alone covers one. A workspace tool covers two or three. A platform should cover all seven on day one.
Both paths end at the same architecture. The difference is how long it takes to get there and who maintains it afterwards.
Assemble Ollama or vLLM for serving, an open-source workspace for chat and RAG, then wire in SSO, DLP, audit logging, policy and monitoring yourself. Every component exists in open source, and for a single technical team running non-sensitive workloads it can be a sound choice. At enterprise scale, the integration and the ongoing maintenance - model updates, breaking API changes, security patching, compliance evidence - become a permanent engineering commitment before the first business user sees value.
Compare the DIY open-source pathDeploy a pre-hardened golden image that ships with the full stack integrated: model routing, RAG, SSO, DLP, audit, policy and monitoring in one deployable unit, running entirely inside your infrastructure. You keep every privacy property of the build path - the platform runs where your data lives - but reach a governed production deployment in days rather than quarters, with the vendor carrying the maintenance burden.
Read the self-hosted LLM guideThis page is the overview. Each guide below goes deep on one part of the private LLM decision.
The plain-language definition, with examples and a decision checklist.
Read the guideHow a gateway routes, meters and secures model traffic.
Read the guideThreats, controls and standards for securing language models.
Read the guideSecuring retrieval pipelines and the documents they expose.
Read the guideWhat separates enterprise deployments from consumer AI tools.
Read the guideA practical guide to running your own models in production.
Read the guideWhat to evaluate before you buy an on-premise assistant.
Read the guideHardening the popular open-source workspace for company use.
Read the guideWhat ChatGPT Enterprise really costs at typical headcounts.
Read the guideHow the hardened platform compares with the open-source base.
Read the guideThe questions security, IT and procurement teams ask most when evaluating private AI.
A private LLM is a large language model deployed inside infrastructure your organisation controls - on-premises servers, a private cloud (VPC), or an air-gapped network - instead of being accessed as a shared public service. Prompts, documents and outputs stay within your security boundary, governed by your own access controls, audit logging and policies, and are never used to train third-party models.
A private LLM removes the structural risks of consumer AI tools: prompts are not processed on shared third-party infrastructure, are not retained by an external provider, and cannot be used for model training. Security still depends on how the deployment is operated - you need SSO, role-based access, DLP, audit logging and patching. A governed private deployment gives you a strictly stronger control position than staff pasting company data into a public chatbot.
Open-weight models such as Llama, Mistral and Qwen are free to license for most commercial use, so the real cost is infrastructure plus platform. A pilot can run on a single GPU server or even a workstation via Ollama. Production deployments add serving, RAG, security and governance: Areebi plans start at about US$30,000 per year for 50-200 users, often less than per-seat enterprise AI subscriptions at equivalent headcount.
Yes. An air-gapped deployment runs open-weight models with zero external network dependencies - the model weights, inference server, RAG pipeline and user interface all operate inside the isolated network. This is the standard pattern for defence, government and critical infrastructure. Areebi ships as a single golden image that supports fully air-gapped operation when paired with local models.
Open-weight model families such as Meta Llama, Mistral, Qwen, Google Gemma, DeepSeek and Microsoft Phi run on your own hardware through serving tools like Ollama, vLLM or LM Studio. Commercial models can also be consumed privately through dedicated cloud endpoints such as Azure OpenAI or AWS Bedrock inside your VPC. Areebi connects to 30+ providers, so you can mix local and private-cloud models under one set of controls.
They describe the same idea at different levels. Private GPT usually refers to a ChatGPT-style assistant deployed privately, while a private LLM refers to the underlying model itself. In practice an enterprise deploys a private LLM stack - model, serving, retrieval and governance - and exposes it to staff as a private GPT-style workspace.
See the full private AI stack - models, RAG, SSO, DLP, audit, policy and monitoring - running inside your own environment in days, not quarters.
Questions? Contact us at hello@areebi.com