On-premise AI
On-Premise AI Platform: Run AI on Your Own Infrastructure
Using enterprise AI does not mean you have to send your data to general-purpose AI services outside your organization.
Last updated: September 2026
On-premise AI is the practice of running AI models, enterprise data and AI applications on infrastructure the organization controls. LLM, RAG, machine learning and AI agent workloads can run in the organization's own data centre, in a private cloud, or on local cloud infrastructure that meets its data residency requirements. What decides it is less where the infrastructure physically sits than who holds control.
On-premise AI is an approach in which AI models, enterprise data and AI applications run on infrastructure the organization controls. It lets AI adoption spread under control, particularly where data security, regulation, data sovereignty and infrastructure control are decisive.
The Kauzas Data & AI Platform is Kubernetes-based, so data, analytics, machine learning and generative AI workloads run under the same platform approach on-premise, in a private cloud and on suitable public cloud environments.
Organizations can therefore control where their data, their models and their infrastructure run while they build AI solutions.
What is on-premise AI?
On-premise AI means running AI workloads — large language models (LLM), machine learning, retrieval-augmented generation (RAG) and AI agents — on infrastructure the organization controls.
That infrastructure may be the organization's own data centre, a private cloud, or local cloud infrastructure that meets its data residency requirements.
With public AI services, enterprise data may be processed by sending it to an external service. In an on-premise architecture, processing happens inside the infrastructure boundary the organization sets.
The approach matters most in sectors working with sensitive or regulated data:
- Finance
- Manufacturing
- Energy
- Telecommunications
- Defence
- Public sector
A model performing well is not enough on its own
In enterprise AI projects, the model performing well is not enough on its own.
Which data the model can reach, where that data is processed, what permissions users and AI agents hold, and who controls the infrastructure are all fundamental parts of the architecture.
Data control
Enterprise data can be held inside the infrastructure boundary the organization sets. LLM inference, RAG, embedding, vector search and other AI processes can run in that same environment.
Security and authorization
AI applications can work alongside existing enterprise security policy. User and AI agent access can be controlled at the level of data sources, applications and services.
Regulation and data sovereignty
The organization decides which country and which infrastructure its data is held on, which makes it possible to build architectures that fit KVKK and the organization's own data security policy.
Technology independence
Organizations can use different technologies within the same architecture without being tied to one AI model, cloud provider or technology vendor.
The Kauzas on-premise AI architecture
Kauzas is not only an LLM or RAG platform. It brings the Data & AI infrastructure together under one platform approach, from the point enterprise data is created through to AI applications.
- Enterprise Data Sources
- Data Integration & Processing
- Data Lakehouse
- Catalog & Governance
- Analytics & Machine Learning
- LLM / RAG / AI Agents
- Enterprise Applications
AI applications can therefore work not only with documents but with the structured and unstructured data held across the organization's different systems.
Enterprise data and AI on one platform
Many generative AI projects begin by connecting an LLM to a set of company documents. At enterprise scale, though, AI applications also need to reach data in ERP, CRM, manufacturing systems, IoT sources, databases, the data warehouse and other operational systems.
An enterprise AI architecture made only of the model layer is therefore not enough.
Kauzas brings the data platform and the AI platform together in one architecture, so structured data, unstructured data, analytics, machine learning and generative AI workloads run over a shared data and governance layer.
On-premise LLM
Large language models can run on the organization's own infrastructure. Open-source models, or models the organization selects, run on Kauzas so that inference happens inside the organization's infrastructure.
In this architecture the organization controls:
- The choice of model
- The infrastructure the model runs on
- The data the model can reach
- Usage policy for AI services
Different models can therefore be evaluated for different scenarios without being tied to a single model provider.
Enterprise RAG
Retrieval-augmented generation (RAG) lets large language models answer using the organization's own knowledge sources. Document processing, embedding, vector search, retrieval and LLM inference can all run on the organization's infrastructure.
In enterprise use, making documents searchable is not enough on its own: user authorization, data access policy, metadata, cataloguing and governance are part of the architecture too. Kauzas therefore treats RAG as a component of the wider Data & AI Platform architecture rather than as a standalone application.
On-premise AI agents
AI agents carry out tasks using an LLM, RAG, enterprise data and a range of tools. In the right architecture, those components can run inside the organization's own infrastructure.
In enterprise use, what the agent can reach matters as much as where it runs.
A Kubernetes-based AI platform
The core Kauzas architecture runs on Kubernetes. This container-based approach lets data and AI services run under the same platform model on-premise, in a private cloud, in a public cloud and in hybrid cloud environments.
That does not mean every dependency disappears; storage, networking, GPUs and identity still vary by infrastructure. What is portable is the platform's installation and operating model.
On-premise, private cloud and public cloud
The three environments are as much complements as alternatives. Kauzas does not require one deployment model to be preferred over another; the right infrastructure is chosen against what the workload needs.
| On-premise | Private cloud | Public cloud | |
|---|---|---|---|
| Infrastructure control | Very high | High | Provider-dependent |
| Data location | Under your control | Set by the organization | Region and service dependent |
| Scaling | Bounded by hardware | Flexible | Highly flexible |
| LLM | ✓ | ✓ | ✓ |
| RAG | ✓ | ✓ | ✓ |
| Machine learning | ✓ | ✓ | ✓ |
| AI agents | ✓ | ✓ | ✓ |
| Data lakehouse | ✓ | ✓ | ✓ |
| Kauzas | ✓ | ✓ | ✓ |
Kauzas runs where the workload needs to run.
Using AI while keeping data in-country
Organizations do not have to send their data to AI services abroad in order to use generative AI.
With the right infrastructure choices, Kauzas makes it possible to build architectures in which the data platform, LLM, RAG, machine learning and AI agent components all run on infrastructure inside the country — the organization's own data centre, a private cloud, or suitable local cloud infrastructure.
The geographic boundaries set for processing and storing data can therefore be preserved according to the organization's requirements.
Data & AI without vendor lock-in
Enterprise Data & AI platforms are long-lived infrastructure. One of the core design principles in the Kauzas architecture is therefore that the platform does not depend on a single cloud provider, AI model or data technology.
Thanks to the Kubernetes-based architecture and replaceable technology layers, organizations can use different technologies as they need them:
- Data processing technologies
- Query engines
- AI models
- Vector databases
- Object storage systems
- Analytics and ML technologies
The aim is to keep control of the organization's Data & AI architecture with the organization, rather than to make particular technologies compulsory.
Frequently asked questions
- What is on-premise AI?
- On-premise AI means running AI models and AI applications on infrastructure the organization controls. LLM, RAG, machine learning and AI agent workloads can all run within this architecture.
- Can a ChatGPT-like system be deployed on-premise?
- Yes. Suitable large language models can run on the organization's infrastructure, so conversational AI applications can be built against enterprise data. Which models are usable and what hardware they need depends on the scenario.
- Can RAG run on-premise?
- Yes. Document processing, embedding, the vector database, retrieval and LLM inference can all run inside the organization's infrastructure.
- Does using AI require data to leave the country?
- No. With the right choice of model, services and infrastructure, AI workloads can run on infrastructure inside the country.
- Does Kauzas only run on-premise?
- No. Its Kubernetes-based architecture means Kauzas can run on-premise, in a private cloud, in a public cloud and in hybrid cloud environments.
- Is Kauzas only a generative AI platform?
- No. Kauzas is a Data & AI Platform. It aims to run data lakehouse, data processing, analytics, machine learning, generative AI, RAG and AI agent workloads on a shared platform.
- Can Kauzas work with different LLMs?
- Its model-independent approach allows different AI models to be brought into the platform architecture according to the scenario.
Build Your Data & AI Platform
Let your infrastructure and your data policy decide where enterprise AI runs. Let's bring an AI use case built on your own data to life together, on the Kauzas Data & AI Platform.