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Self-hosted models refers to deploying and operating AI/ML models on private infrastructure rather than managed cloud services. It emphasizes data sovereignty, low-latency inference, compliance and full control over models, resources and integrations. Operations, monitoring and model updates must be supported by organizational capabilities.
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Self-hosted models are AI models operated on infrastructure owned or controlled by a team rather than used exclusively through an external model service.
The approach grew from the need to control data, runtime, and model operations. Open-source models and serving projects such as KServe made it more practical to run inference as a service in Kubernetes environments.
A model artifact lives in a controlled environment. An inference service loads it, accepts requests, and returns results; resource planning, versioning, scaling, monitoring, and access control become part of the team’s operating responsibility.
The model and its serving service run in infrastructure whose configuration and access the team controls.
The running service processes inputs with a trained model and produces predictions or text.
Scaling, updates, monitoring, cost, and resilience must be designed and maintained.
Self-hosted models can align data flows, latency, and availability with local requirements. They also increase operational work, hardware needs, patch duties, and responsibility for model quality and abuse prevention.
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