UPC Vektor - Enterprise Private Cloud with Private AI

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.

The platform

AI Factory Operations, Governed from
Silicon to Token

VEKTOR runs the full stack — fleet, fabric, workloads, tokens, tenants — and turns racked
hardware into a metered, revenue-generating service.

Manage your GPU fleet end-to-end

Provision, monitor, and optimize clusters at scale. Discover rack topology and switch fabric, track thermal and power draw per rack, and plan capacity across idle, cool, warm, and hot GPU pools.

Govern token economics and cost-to-serve

Measure live token throughput and latency by model, tenant, and workload. Compute exact cost-to-serve in real time, and benchmark private inference against public API spend to drive placement decisions.

Deliver multi-tenant AI as a service

Onboard tenants with dedicated GPU allocations, self-service API access, and RBAC. Schedule workloads by SLA, cost, and capacity — then meter per-tenant consumption for chargeback and billing.

Orchestrate and port LLM workloads freely

Route inference across vLLM, LiteLLM, TensorRT-LLM, and public APIs. Migrate models between private and public providers without re-engineering, with support for PyTorch, TensorFlow, ONNX, and CUDA.

Enforce governance, compliance, and security

Apply RBAC, SSO, and MFA with per-tenant policies. Maintain full audit logs and compliance dashboards, and secure execution with hardware-backed confidential computing.

Operate the factory with an ITOps copilot

Run everything through ChatOps, ask about fleet health, idle GPUs, and underperforming racks; trigger runbooks, rollouts, and rollbacks through human approval.

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

Deliver & Manage AI Factory Faster
with Vektor

Why VEKTOR

Extract Maximum ROI from Your AI Factory

Platform teams get real-time control over performance, power, and cooling. Operators get tokenomics, isolation, and portability that hold across private and public LLMs.

Higher fleet utilization

Real-time cost per million tokens, computed from GPU power, PUE, and interconnect — so placement decisions are made on economics, not guesswork.

Revenue visibility, tenant by tenant

Metered token consumption and workload-level economics make chargeback, showback, and pricing defensible.

Freedom from lock-in

Vendor-neutral orchestration keeps workloads portable across private and public LLMs, protecting margin as the market shifts.

Governance that scales with you

Per-tenant policy, audit trails, and confidential computing let you grow tenants and workloads without loosening control.

Faster time to production

A structured path — design, build, operate, handover — takes a factory from topology plan to client-autonomous operation.

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.

01

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.

02

Build

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