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Build, Run, and Manage Your
AI Factory

Maximize GPU utilization, lower cost-to-serve, and eliminate bottlenecks across training and inference — from a single
platform.
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The Platform

Run Your GPU Fleet. Govern Your Tokens. Deliver AI at Scale.

Gain real-time visibility into GPU utilization, token throughput, hybrid LLM spend, and cost-to-serve – all while delivering AI securely, efficiently, and at scale.

Full Stack GPU Fleet Management & Observability

Provision, monitor, and optimize GPU clusters at scale. Manage fleet, fabric, thermal, power, capacity, fleet topology, and inventory from one console.

  • Discover and visualize rack topology, switch fabric, and GPU node inventory
  • Monitor GPU utilization, thermal state, and power draw (kW per rack) in real time
  • Plan capacity across idle, cool, warm, and hot GPU pools to maximize fleet efficiency
GPU Observability & AIOps console preview

Manage Token Economics & Cost-to-Serve

Track token usage across every workload and LLM API. Know your exact cost-to-serve per million tokens – private and public.

  • Measure live token throughput and latency per model, tenant, and workload
  • Compute cost-to-serve in real time by GPU power + PUE, and NVLink/WAN costs
  • Benchmark private inference economics against public API spend to drive workload placement decisions
AI Compute Economics console preview

Multi-Tenancy & Workload Delivery

Multi-tenant GPU environments with full isolation, self-service portals, intelligent workload scheduling, and workload-level token economics.

  • Onboard tenants with dedicated GPU allocations, self-service API access, and RBAC
  • Schedule AI workloads across GPU pools based on SLA, cost-to-serve, and available capacity
  • Get per-tenant token consumption, cost, and workload-level economics via metered dashboards
Multi-Tenant GPU Cloud Platform console preview

Seamless LLM Orchestration & Portability

Orchestrate workloads across LLM APIs. Migrate models between providers – keeping your AI factory flexible and cost-efficient.

  • Route inference requests across vLLM, LiteLLM, TensorRT-LLM, and public APIs
  • Migrate workloads between private and public LLMs without re-engineering
  • Support PyTorch, TensorFlow, ONNX, and CUDA across distributed, multi-cluster AI infrastructure
AI Workload Orchestration console preview

Govern Every AI Workload

Set approval levels, role-based access, and apply per-tenant policies for safe execution. Ensure trusted and secure workloads using hardware-backed confidential computing.

  • Enforce RBAC, SSO, and MFA with per-tenant access policies
  • Maintain full audit logs, compliance dashboards, and exportable configs
  • Secure workload execution with hardware-backed confidential computing
Compliance, Governance & Security console preview

Manage AI Factory with ITOps Copilot

Operate your AI factory with ChatOps. Simply prompt, orchestrate, and control everything from infrastructure to workloads with LUMI.

  • Ask Lumi about fleet health, underperforming racks, idle GPUs, and optimization
  • Trigger runbooks, rollbacks, rollouts, and workload actions through human approvals
  • Lumi sees the full operator/tenant workspace, executing policy-driven actions
Manage AI Factory with ChatOps console preview

Delivery model

Scale from Infrastructure to Production-Ready AI

VEKTOR structures AI factory operations across four delivery stages, enabling operators to hand over a production-ready AI factory with complete client autonomy post-delivery.

Design

Plan your GPU fleet topology, define tenant architecture, configure switch fabric, and establish your service catalog and token pricing model before a single workload runs.

Build

Deploy and configure the full infrastructure stack. Onboard tenants, activate workload pipelines, connect public and private LLM APIs, and validate token flows.

Operate

Run your AI factory in production. Monitor GPU health, manage token throughput, track revenue, optimize workloads, and enforce governance at scale.

Monetize

Turn your AI factory into a revenue engine. Meter token usage per tenant, apply pricing and marketplace policies, run chargeback and showback, and track revenue against cost-to-serve.

Sovereignty & private inference

Run AI Inference Inside Your Own Boundary

Models, data, and tokens stay within your perimeter. Meet data residency and regulatory requirements without giving up model choice or economics.
  • Private LLM inference on your GPUs
  • Data residency & regulatory control
  • Hardware-backed confidential computing
  • Benchmark cost-to-serve, private-vs-public

Use Cases

One Platform. Three Powerful Use Cases.

VEKTOR structures AI factory operations across four delivery stages, enabling operators to hand over a production-ready AI factory with complete client autonomy post-delivery.

GPUaaS at Enterprise Scale

Manage your fleet, onboard tenants, set pricing, track revenue, and deliver SLA-backed GPU compute as a commercial service from a single operator console.

Private AI Factory Operations

Bring commercial-grade operational rigor to internal AI factories. Full tokenomics, workload governance, and hybrid LLM spend management across business units.

Production AI Factory Delivery

Build, manage, and handover production-ready AI factories – with complete audit trails, self-service tooling, and support hooks baked in from day one.

GPU Farm Management

Extract Maximum ROI from Your AI Factory

Orchestrate the future of enterprise intelligence with governed AI factory operations. Platform teams get real-time control over performance, power, and cooling. Operators get tokenomics, isolation, and portability that hold across private and public LLMs.
Governed-Token-Utilization
GPU-Platform-Operations
GPU-Observability

GPU Platform Operations

  • Provision, monitor, and optimize GPU clusters at scale.
  • Support AI training, inference, HPC, and enterprise AI workloads.
  • Govern capacity across multi-cloud and distributed environments.

GPU and AI Observability

  • Monitor GPU health, telemetry, utilization, and alerts.
  • Track AI cluster performance across large-scale environments.
  • Schedule, scale, and prioritize workloads intelligently.

Governed Token Utilization

  • Track token usage, allocation, and consumption across tenants.
  • Enforce pricing, marketplace capacity, and access policies.
  • Improve revenue visibility across AI compute services.

Simplify How AI Factories Are Deployed and Operated

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About UnityOne AI ™

UnityOne AI™ is an agentic intelligence platform for ITOps management, comprising CERNE™, LUMI™, and VEKTOR™. CERNE™ replaces dozens of cloud management tools by unifying DCIM, AIOps, HCMP, FinOps, and GreenOps within a single AI-driven control plane. LUMI™, the AI copilot, provides contextual intelligence, operational recommendations, and workflow automation, while VEKTOR™ enables enterprises to provision, orchestrate, and scale AI factories with the lowest cost-to-serve. The UnityOne AI™ suite enables enterprises to simplify hybrid/multicloud operations, strengthen governance, optimize resource utilization, and accelerate transformation to AI-driven ITOps.

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