Self-hosting

Technical terms
About 1 min read

A deployment model where AI models and software are installed and run on servers directly owned by an enterprise or individual, or on an independent cloud instance, rather than using an external vendor's cloud service (SaaS).

Also known as
private cloud deploymenton-premise deployment

Detailed explanation

Self-hosting is a deployment method that shifts the responsibility of securing sensitive data, model execution location, and disaster recovery to the service user. Because data is processed within a local environment or dedicated infrastructure rather than being sent to external servers, it is advantageous for privacy protection and compliance. With the recent emergence of high-performance open-source models like Llama 3 and Mistral, there is a growing trend of building in-house AI environments using tools like Ollama and vLLM. Although it requires upfront hardware investment and operational personnel, it can provide a more predictable cost structure than API usage fees when processing large volumes of data over the long term.

Why It Matters in Tool Selection

It completely eliminates the risk of data leaks. For enterprises that need to analyze sensitive internal data with AI, self-hosting is a safer choice than SaaS-based AI. Furthermore, it allows you to directly manage service stability without being affected by external variables, such as model update cycles or API outages.

What to Look For

  • Do you have the GPU resources (such as VRAM) required to run the model?
  • Do you have the technical personnel to maintain the infrastructure in-house?
  • Are server operating costs (electricity, hosting fees, etc.) more economical compared to API call costs?
  • Do your data security regulations prohibit the use of external cloud services?

Examples

A scenario where a law firm that prioritizes security installs Ollama on its internal server and deploys the Llama 3 model to analyze legal case data without risking external leaks.

Related terms

GPU