On-premises deployment
In one sentence
Installing AI systems or models on an enterprise's own servers or dedicated cloud environment so data never passes through public platforms.
Companies with strict data security requirements (such as customer privacy or financial data) often consider on-premises deployment. The trade-off is requiring dedicated servers and operations staff, and model capabilities are constrained by hardware.
Deciding whether to deploy on-premises depends on data sensitivity: public-facing materials can use cloud services, while sensitive data warrants on-premises evaluation.
Example
A company deploys its internal knowledge base and AI agents on its own cloud servers, ensuring employee prompts and files never leave the corporate environment.
Related terms
- AI governance
- An enterprise's rules and oversight for AI usage: who can use it, what data it can handle, who is accountable for errors, and how actions are logged and audited.
- Knowledge Base
- A repository of company documents and institutional knowledge that AI can search and reference, enabling it to answer questions and produce content grounded in internal facts.
- API
- Interfaces that allow software programs to interact with each other. Enterprise systems and AI agents use APIs to access capabilities such as large language models, search, image generation, and publishing.
