No operational standards remain after build.
Recombining Kubernetes, Ceph, Harbor, ArgoCD, and monitoring tools in each environment leaves no operational standards after implementation.
On-Premise Kubernetes Platform for Stable AI Execution
ORKESTRIX
Where AX Projects
Stall is Usually
Infrastructure Operations.
AI models and PoCs can start quickly. But in production operations, Kubernetes, GPU, data, security, deployment, and monitoring must work together. ORKESTRIX organizes this complex foundation into a structure customers can operate.
GPU costs grow but usage is invisible.
Without seeing GPU, network, and storage bottlenecks together, training and inference costs increase and incident response is delayed.
Public cloud is difficult in secure environments.
Manufacturing, pharma, finance, and public sectors require on-premise and air-gapped standards due to data export, external API, and internet dependency constraints.
Dependency on specialized personnel grows.
When SREs and platform engineers manually diagnose all layers, operational burden accumulates and scaling across departments and projects becomes difficult.
K8s Operations Environment
AX Workloads
Execution Foundation
Organized.
What is needed is not Kubernetes installation itself, but a platform operating system that keeps AI services reliably deployed, quickly identifies issues, and supports continuous operations while meeting security standards.
On-Premise K8s Operations Foundation
Provide an integrated Kubernetes, storage, network, image registry, and monitoring configuration tailored to the customer's security environment.
KubernetesStorageMonitoringRegistry
AI Service Execution Environment
Connect GPU resource management, model training and inference, LLMOps, and MLOps tools to the AI service execution flow.
GPULLMOpsMLOps
Security, Observability, Deployment Automation
Include authentication, permissions, audit logs, deployment, rollback, and monitoring in operational processes.
SSO/ RBACObservabilityAudit
From Initial Build to Managed K8s Operations
Expands as One Structure.
Build Kubernetes foundation for AI workload execution in on-premise and air-gapped environments.
Adding QKS Edition expands beyond single cluster building to a Managed K8s operations system for provisioning, isolating, observing, and recovering multiple clusters.
Build a Kubernetes-Based
AI Platform.
Provide Kubernetes, GPU, storage, network, registry, monitoring, and security configurations required for AI service operations in on-premise and air-gapped environments as a unified platform.
Kubernetes-based AI service deployment and operations environmentGPU, storage, network, monitoring integrationRBAC, SSO, internal registry, air-gapped configuration
Operate Multiple
Clusters with Managed K8s.
QKS Edition extends ORKESTRIX Core with multi-cluster provisioning, tenant isolation, cluster mesh, VM management, upgrades, backup/recovery, and unified observability.
Unified multi-cluster and multi-tenant managementCross-cluster service connectivity based on Cluster MeshVM Management and Backup/Recovery support
QKS Edition is an expanded configuration that broadens operations scope.
Expands beyond single-build platforms to Managed K8s operations platform provisioning and managing multiple Kubernetes clusters by same standards.
Cluster Provisioning and Initial Configuration
Configure Kubernetes clusters for new projects or AI services according to a standard architecture.
Multi-Cluster Unified Management
View and manage the status, resources, workloads, and policies of multiple clusters from a single operations dashboard.
Tenant and Permission Isolation
Isolate permissions, network, and resource usage scope by organization and project units.
Observability and Recovery Operations
Manage logs, metrics, events, GPU status, and backup/recovery history as operational processes.
Kubernetes Operations Method Comparison
Public cloud is convenient, but not all AI infrastructure can be operated in cloud.
ORKESTRIX QKS focuses on operating cluster provisioning, isolation, observability, and recovery by same standards even in internal networks.
Item
Public Cloud Managed K8s
Rancher/ OpenShift Series
DIY Build
ORKESTRIX QKS
Operations Location
Public cloud focused
On-premise possible
On-premise possible
On-premise and air-gapped focused
Cluster Provisioning
Cloud console provided
Supported per product and config
Operator builds directly
QKS-based provisioning, tenant and cluster operations standardized
Air-Gapped Operations
Highly constrained
Separate design needed
Possible but maintenance burden high
Offline packages, internal registry, update flow configured
Tenant Isolation
Account, VPC, cluster units
Namespace/Project focused
Direct design needed
Hard Tenancy, per-tenant control areas considered
AI Workload Observability
External service integration focused
Separate AI stack integration needed
Operator dependent
GPU and workload observability, SAMANDA integration expansion
ORKESTRIX
Operations Environment Needed
AI infrastructure is not complete with models alone. Kubernetes, GPU, security, deployment, and monitoring must be operated together to work stably in production services and AX environments.
Manufacturing and Pharma AX Execution Infrastructure
Build AI analysis and operations automation environment on internal network where equipment, quality, and production data cannot leave externally.
On-premise GPU cluster buildingAir-gapped model serving and data integrationAnomaly detection and operations automation based on logs and metrics
Air-Gapped and Segmented Network AI Operations Platform
Create Kubernetes-based AI service operations standards in organizations requiring network segmentation, access control, and audit logs.
Air-gapped installation and internal registrySSO, RBAC, audit logs integrationData sovereignty and compliance response
AI Service Cost and Operations Optimization
Improve operations structure with on-premise K8s and GPU visibility where public cloud costs become burdensome.
GPU utilization and resource monitoringMLOps and LLMOps pipeline integrationDeployment, rollback, and observability automation
Validated with Real Operations Cases.
Compared to Public Cloud 1B Annual Cost Savings and Operations Structure Improvement
Transitioned aDot service operations environment to ORKESTRIX-based on-premise K8s infrastructure, lowering cost burden and securing GPU visibility and stable operations system.
100M/month
Existing public cloud infrastructure cost burden
- 90%
Annual infrastructure cost savings effect
- 1B/year
Annual infrastructure cost reduction scale
Public Cloud Cost Burden
Infrastructure costs exceeding 100M per month consistently occurred for AI service operations.
ORKESTRIX-Based On-Premise Migration
Built Kubernetes infrastructure optimized for AI workloads as internal operations environment.
Cost Savings and Structure Improvement
Achieved approximately 90% annual infrastructure cost reduction and 1B annual cost improvement effect.
Sustainable Operations and Trust Building
Visualized GPU utilization, leading to sustained stable operations and trust relationship maintenance.
ORKESTRIX FOR AI PLATFORM
Build an
On-Premise AI Platform for Reliable AI Service Operations.
ORKESTRIX integrates Kubernetes, GPU, security, monitoring, and deployment automation in on-premise and air-gapped environments
providing infrastructure foundation for stable AI service execution and operations.