Self-hosted AI places model weights and inference software on a computer or private infrastructure controlled by the operator. It can support local data handling, fixed model versions and integration with internal security controls.
The organization takes responsibility for hardware, updates, monitoring and performance. Local deployment is most valuable when compliance, privacy or data control outweigh the convenience and scale of a hosted provider.
ELI5
Self-hosted AI runs on computers controlled by the person or organization using it, instead of depending entirely on a model hosted by another company. This can give the operator more control over data location, model versions, access rules, and when updates happen.
For example, a hospital might run a suitable model on its own private servers so selected records do not have to leave its managed environment. The hospital must then maintain the hardware, software, security, monitoring, and performance that a hosted provider would otherwise manage.
Why deploy an AI model locally?
Local deployment can keep sensitive inputs within controlled infrastructure and provide greater control over versions, access and service availability.
Is self-hosted AI always cheaper?
No. Hardware, energy, maintenance and specialist operations can cost more than hosted inference depending on workload and scale.
