How on-premises, cloud and hybrid architectures differ
| Comparison | On-premises | Cloud models | Hybrid |
|---|---|---|---|
| Data flow | Stays in the company environment | Prompts are sent to the model provider | Routed by data sensitivity |
| Upfront investment | Requires servers and GPUs | Lower, billed by usage | Between the two |
| Model choice | Models that can run on-premises | Models offered by the provider | Either |
| Operations | The company or vendor | The model provider | Requires a clear division of responsibility |
| Best suited to | Highly sensitive data, regulatory requirements | General office work and drafting | Most mid-sized and large companies |
When on-premises deployment is the right fit
- You handle highly sensitive data such as customer personal data, financial data, or R&D and process documents.
- Industry regulations or customer contracts prohibit sending data outside.
- Usage is high and steady, so building your own compute is more cost-effective in the long run.
- You need to integrate directly with in-plant systems (MES, PBX and so on) over the internal network.
7 things to check when evaluating an on-premises enterprise AI platform
- Fully on-premises support: whether models, knowledge bases, applications and records can all run in the company environment.
- Model flexibility: whether models can be swapped as needed, and whether governance settings carry over after a swap.
- Hardware assessment: whether the vendor can recommend GPUs and servers based on user numbers and use cases.
- System integration: whether it can connect to existing systems such as ERP, MES, CRM and phone systems (PBX) over the internal network.
- Permission control: whether the AI's access to data follows employees' existing permissions, and whether revocation takes effect immediately.
- Audit records: whether records of user actions, model calls and execution are complete enough to investigate.
- Operations responsibilities: who handles installation, updates, monitoring and troubleshooting, and whether local support is available.
A common misconception
On-premises deployment only solves the problem of data leaking to a model provider. It does not automatically address internal permissions, prompt injection or wrong answers. Even with everything on-premises, you still need permission control, answers with cited sources and complete activity records.
How NOVARK helps
NOVARK supports fully on-premises deployment; if a cloud model is selected, prompts are sent to that model provider. Companies can also set conversation retention periods and deletion records, and enable personal data masking when importing into the knowledge base. When models are changed or features added, the same AI governance standard carries over. Intelligent customer service prioritises on-premises GPU deployment. Actual hardware specifications are assessed based on the model and usage; contact us by email to discuss.
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Is on-premises deployment always more secure than cloud?
On-premises deployment prevents data being sent to an external model provider, but internal risks such as misconfigured permissions or unrecorded activity can still exist. How secure you are depends on overall governance, not just where the system is deployed.
What hardware does on-premises deployment require?
It mainly depends on model size, the number of concurrent users and response-time requirements. Take stock of your use cases first, then have the vendor recommend GPU and server specifications.
Can we run partly on-premises and partly in the cloud?
Yes. A common hybrid approach handles sensitive data and core systems on-premises and uses cloud models for general office work, with clear rules on which data may be sent outside.
This article is general guidance. Adapt the approach to your organisation's size, industry regulations and data. NOVARK is an enterprise AI agent platform.