Business · AI Products
Focus on AI Innovation,
Not on AI Infrastructure
Heterogeneous GPU and NPU hardware, model serving, deployment, and monitoring: every layer of infrastructure an AI service needs runs from a single Control Plane. AURDA is a Kubernetes-based PaaS that spans on-premise and multi-cloud environments.
Key Features
Infrastructure Operations, Finished On Screen
These are the actual product screens. GPU allocation, deployment, and monitoring all run from one console.
GPU·NPU Console
NVIDIA GPUs and Korean NPUs on One Screen
Run NVIDIA H200 GPUs alongside FuriosaAI and Rebellions NPUs from a single console. Pick the best hardware for each workload without vendor lock-in. New nodes are detected automatically and join with no downtime.
MIG Partitioning
Split One GPU Across Workloads, Precisely
MIG divides a high-performance GPU into up to seven independent instances, physically isolating compute and memory. GPU state is tracked live, down to temperature, power draw, and DRAM utilization.
Model Serving
Deploy an LLM in One Click
Choose a model, GPU resources, and a runtime, and the console deploys it immediately. The vLLM high-performance inference engine is supported. In air-gapped networks, models load directly from an internal repository, so the service runs with no external internet.
GitOps
Infrastructure and Deployment as Code, Rollback Anytime
Built on ArgoCD, the state defined in Git is applied to the cluster automatically. Drift between the Git definition and the live state is detected at once, and a failure restores to the previous version immediately.
AI Gateway & Autoscale
Traffic Routes Itself, Instances Scale Themselves
Private and public LLMs are unified behind one API, with traffic distributed automatically by load, model version, and service type. When load spikes, serving instances scale out and requests spread across the available capacity.
Observability
One Dashboard, from Infrastructure to LLM Inference
Track GPU utilization, temperature, and power alongside LLM performance metrics such as TTFT and TPS on a single dashboard. Centralized logging on Loki and threshold-based live alerts shorten incident response time.
Platform
Defining the Standard for AI Infrastructure Operations
From container virtualization to security, everything AI operations require sits on top of Kubernetes orchestration.
Architecture
Six Management Domains, One Platform
Container virtualization, service development (CI/CD), service optimization, cloud infrastructure, resource management, and security management. It is one Kubernetes-based structure covering on-premise and multi-cloud.
Agentic ChatOps
"Scale That to Two Servers": Infrastructure Run by Conversation
An MCP-based AI Agent interprets natural-language commands and carries them out, from root-cause analysis through to scale-out. Incident response gets shorter, and repetitive operations work ends in a conversation.
Role-based Workflow
Operators, Administrators, Developers: Each Role Has Its Flow
Cluster installation and security setup for the infrastructure operator. Model serving and inference tuning for the platform administrator. RAG and Agent development for the AI developer. Each operating flow is designed into the platform.
Universal AI Runtime
n8n, Dify, Docker: Heterogeneous Workloads, Unified
The AURDA Control Plane executes, secures, and monitors the workflows, containers, and legacy Agents scattered across departments against a single standard. That is what makes enterprise-wide AI governance possible.
Customer Story
AURDA, Proven in the Field
KODATA
An Enterprise-Wide Standard AI Development Platform (LLMOps + KMS)
A standard AI development and operations platform was built on an H200-based Kubernetes environment, bringing infrastructure, models, data, and Agents together. Thirteen AI Agents that had been developed project by project now share one development and deployment system.
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AURDA Product Overview
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