What is on-premise AI?
Public AI services can be quick and simple to start, but they are not suitable for every organization.
Some organizations work with data that, for security, contractual or architectural reasons, should not leave a defined environment without clear control.
On-premise deployment is one architectural option in this situation. Some or all AI components run on infrastructure directly controlled by the organization.
This infrastructure may include GPU servers, Virtualization, Kubernetes, Object Storage, Vector Database, Model Server, API Gateway, Monitoring and IAM.
The architecture should be selected according to real organizational requirements and the broader enterprise AI context.
On-premise vs. Cloud and Private Cloud
None of these models is inherently better. The right choice depends on organizational needs.
Cloud
The main model and infrastructure run on a cloud provider.
Private Cloud
The organization has more control over the runtime environment, network or dedicated resources while still using a Cloud architecture.
On-Premise
The infrastructure runs in a data center or environment controlled by the organization.
Why do organizations choose On-Premise?
More control over data flows
The organization can define where data is stored and processed.
Network control
The system can run on an internal or restricted network, including architectures without a direct Internet connection.
Integration with existing infrastructure
AI can be closer to internal systems, Storage and IAM.
Control over models and Runtime
The organization can manage the model, version, Runtime and execution policies.
A sample on-premise AI architecture
A possible architecture includes a User, Gateway / Auth, AI Platform, Model Server and Knowledge / Tools / Internal Systems. Logging, Monitoring, IAM and Audit act as control layers alongside these components.
Hardware requirements
Hardware needs depend on model size, Quantization, Context Length, Concurrent Users, Batch Size, Tokens per Second, KV Cache and Precision.
A small Quantized model may run on one GPU, while a multi-user system with a large model may require multiple GPUs and Orchestration. A fixed GPU or VRAM recommendation cannot apply to every project.
Security in On-Premise AI
Local does not automatically mean Secure. An internal system may still face Prompt Injection, Data Leakage, Excessive Permission, Credential Leakage, Insecure API, Model Theft and Misconfiguration.
IAM, Least Privilege, Audit, Network Segmentation, Secret Management and Monitoring remain essential.
On-premise limitations
On-premise transfers more responsibility to the internal team. The organization must manage hardware procurement, power, cooling, Storage, Drivers, Model Runtime, Scaling, Backup, Monitoring, Patching, Security and Availability.
In Cloud environments, providers manage part of these responsibilities.
Hybrid AI
For many organizations, the best architecture is neither pure Cloud nor pure On-Premise. Hybrid AI can keep sensitive data and the main model inside the organization, use selected public services in the Cloud and route requests according to data type.
This can balance control, cost and flexibility.
On-Premise and RAG
An internal architecture can run the full RAG chain inside the organization: documents, Parser, Embedding, Vector Store, Retrieval and LLM. Data and processing can remain within the network.
Read What is RAG? to learn more about this architecture.
Which organizations consider On-Premise?
Organizations with sensitive data, network-control requirements, existing GPU infrastructure, high usage volume, limited Internet connectivity or an internal deployment requirement may consider On-Premise more seriously.
It is not, however, the simplest or most economical choice for every organization.
On-Premise at Aivan
At Aivan, internal deployment can be part of an enterprise AI architecture, especially where control over processing location, networks and data access matters. Architecture should be selected according to real organizational needs rather than technology preference alone.
Frequently asked questions
What is on-premise AI?
It is a deployment where AI models and services run on infrastructure controlled by the organization.
Is On-Premise always more secure than Cloud?
No. Security depends on the complete architecture, IAM, network, configuration, access and maintenance.
Does On-Premise mean no data leaves the organization?
Not necessarily. Supporting services, Telemetry or external APIs may still move data outside the network.
Can RAG run entirely inside an organization?
Yes. Components such as Embedding, Vector Store and an LLM can run on internal infrastructure.
Is On-Premise suitable for every organization?
No. Cloud or Hybrid may be simpler and more economical for some organizations.
Sources and further reading
- NVIDIANVIDIA NIM MicroservicesOfficial Product / Deployment Documentation
- NVIDIA DocumentationNVIDIA NIM DocumentationOfficial Documentation
- NISTArtificial Intelligence Risk Management Framework: Generative Artificial Intelligence ProfileRisk Management Guidance · NIST AI 600-1