An AI platform build is an end-to-end engagement in which Ænix designs and operates dedicated GPU infrastructure for organizations running sustained AI workloads — 24/7 inference, fine-tuning, and training — where renting hyperscaler GPU capacity becomes too expensive over time. It is built for AI startups, GPU operators, research-heavy organizations, telcos, and enterprises with regulated data that cannot send it to external model providers. Ænix delivers the platform on Cozystack, an Apache 2.0 CNCF project accepted into the CNCF Kubernetes AI Conformance program, which runs VM and container GPU workloads on one Kubernetes API via KubeVirt. NVIDIA data-centre GPUs are supported through the NVIDIA GPU Operator, with passthrough to VMs and sharing via HAMi; serving uses vLLM or Triton.
AI startups and AI-heavy enterprises in 2026 face the same architectural choice: rent inference at hyperscaler economics, or build dedicated infrastructure that pays back at scale. For sustained workloads (24/7 inference, fine-tuning, training), dedicated infrastructure usually wins after a year of operation. Ænix builds these platforms end-to-end.
Pairs with: Ænix AI Platform — AI infrastructure with multi-tenant GPU scheduling on NVIDIA data-centre GPUs, blueprints for inference + fine-tuning + RAG, sovereignty controls for regulated AI workloads. Free Sovereign AI Decision Guide →.
- AI startups with sustained inference workloads where hyperscaler GPU is too expensive
- AI/GPU operators offering inference as a customer-facing product
- Enterprises with regulated data that can’t go to model providers
- Research-heavy organizations with sustained training workloads
- Telcos and edge providers offering AI-at-edge
What we deliver
AI platform buildAssessmentBuildManaged operations
delivers
Cozystack GPU platformKubeVirtVM + container GPU workloadsOne Kubernetes API
runs
AI workloadsInferenceFine-tuningTraining
- Cozystack-based AI platform — KubeVirt + Kubernetes for both VM and container GPU workloads
- NVIDIA GPUs — through the NVIDIA GPU Operator: passthrough to VMs, NVIDIA vGPU for VMs (requires your NVIDIA vGPU licence), HAMi sharing for containers
- Inference serving — vLLM, Triton, custom; matched to model architecture
- Multi-tenant model serving — for customer-facing AI products
- Sovereignty controls for regulated data classes
- Operations model for 24×7 GPU clusters
Engagement structure
- Discovery call (30 minutes, free)
- Platform Readiness Assessment (14 or 28 days, fixed price) — workload fit, GPU sizing, architecture
- Build (3-12 months, depending on scope)
- Managed AI platform (optional)
Written up: 8×H100 inference on your own bare metal and GPU cost cut about five times for an academic SaaS.
For sovereignty-emphasized workloads see Sovereign AI.
Ænix created Cozystack, a CNCF project, and maintains it with maintainers from other companies.
Frequently asked questions
When does building a dedicated AI platform beat renting hyperscaler GPU?
For sustained workloads such as 24/7 inference, fine-tuning, and training, dedicated infrastructure usually wins after about a year of operation. Bursty or short-lived experimentation often stays cheaper on rented capacity. Ænix models the break-even during the assessment to determine the break-even point for a given workload.
What GPUs and inference stacks does Ænix support?
NVIDIA data-centre GPUs through the NVIDIA GPU Operator, passed through whole to VMs, split with NVIDIA vGPU for VMs (requires your NVIDIA vGPU licence), or shared between containers with HAMi; our published deployments include an 8×H100 inference server. We do not publish a validated-model list, and NVIDIA partner validation of the stack is pending. Inference serving is matched to model architecture using vLLM, Triton, or custom serving stacks, and the platform supports multi-tenant model serving for customer-facing AI products.
Can a dedicated AI platform keep regulated data on-premises?
Yes. The platform is built for enterprises with regulated data classes that cannot be sent to external model providers. Sovereignty controls are applied to the relevant data classes, and Cozystack’s Tenant CRD provides multi-tenant isolation. Sovereignty-led engagements are covered under Sovereign AI.
How is the platform built and how long does it take?
After a free discovery call, a fixed-price Platform Readiness Assessment of 14 or 28 days covers workload fit and GPU sizing. The build then takes 3-12 months depending on scope; one customer reached production on an 8×H100 server in about two months. Ænix can operate the platform afterwards as a managed service.
What software does the platform run on?
The platform is built on Cozystack, an Apache 2.0 CNCF Sandbox project. It runs both VM and container GPU workloads on a single Kubernetes API via KubeVirt, with Cilium (eBPF) networking and LINSTOR/DRBD storage. There is no per-CPU or per-core licensing.
What is the difference between this service and Ænix AI Platform?
The AI Platform is the third Ænix platform: multi-tenant GPU scheduling and blueprints for inference, fine-tuning and RAG on the Cozystack engine. The AI platform build is the services engagement that designs and delivers a custom platform end-to-end, usually on top of that product.
