Private LLM & Inference
Deploy open or appropriately licensed models behind controlled inference endpoints with authentication and access policies.
OPENKUBES · INHOUSE AI
Your Inhouse AI.
Your infrastructure.
Private LLMs, RAG and AI inference on controlled Kubernetes infrastructure. OpenKubes Inhouse AI brings together data sovereignty, GPU resources and repeatable model operations.
THE CHALLENGE
Bring AI into production without giving up control.
Prototypes are easy to start, but enterprises need clear data flows, identity, isolation, capacity planning, model versions, observability and accountability. These concerns belong in the platform architecture.
OPENKUBES / INHOUSE AI CAPABILITIES
Sovereign AI infrastructure.
GPU & Kubernetes
GPU scheduling and resource management for teams and workloads, based on available hardware and infrastructure.
RAG & Data Integration
Retrieval-augmented generation using controlled access to enterprise knowledge, vector retrieval and governed data flows.
MLOps & Model Lifecycle
Pipelines, model registries, versioning, rollout, rollback and monitoring using suitable components such as MLflow, Kubeflow or KServe.
Security & Governance
Policies, network segmentation, secrets, audit logging and reviewable approval processes for AI workloads.
Observability & Operations
Inference, GPU utilization, availability, cost and model-operation metrics integrated with platform operations.
OpenKubes Inhouse AI describes a configurable platform architecture, not a blanket promise of an immediately available managed AI product. Model, GPU and component availability are validated per project.
LET'S TALK ENGINEERING
Private AI on your terms.
Let's assess data requirements, GPU options, model operations and a realistic pilot scope.