Kubernetes (in sovereign AI)
The container orchestration platform that hosts the sovereign AI stack — provides namespace isolation, network-policy enforcement, GPU scheduling, and the lifecycle plumbing for upgrades.
What Kubernetes (in sovereign AI) means in practice
Kubernetes is the orchestration layer underneath every sovereign AI deployment MindMap ships. It provides three things that matter specifically for sovereignty: namespace isolation so the AI workloads share a cluster without sharing a security boundary; NetworkPolicy enforcement so egress can be blocked at a policy layer the runtime cannot bypass; and the operator pattern for managing the lifecycle of complex stateful components (vector databases, model registries) inside the air-gap. We deploy onto bare metal, VMware Tanzu, OpenShift, or any CNCF-conformant cluster — what the customer already operates rather than a new platform requirement.
Related terms
Sovereign AI →
An architecture where customer data never leaves the network perimeter, model weights run on customer-controlled hardware, inference logs stay in the customer's SIEM, and the entire stack can operate air-gapped.
vLLM →
The high-throughput open-source inference server for LLMs — uses PagedAttention and continuous batching to serve open-weights models at production rates on a single GPU.
Egress control →
Network policies that block outbound traffic from a deployment so no component can call external services, even by accident or compromise.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.