An AI platform build is an end-to-end engagement in which Aenix 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. Aenix delivers the platform on Cozystack, an Apache 2.0 CNCF project that runs both VM and container GPU workloads on one Kubernetes API via KubeVirt, with validated support for A100, H100, H200, L40S, and Blackwell GPUs, inference serving (vLLM, Triton), multi-tenant model serving, and sovereignty controls for regulated data.
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 — turnkey AI infrastructure with multi-tenant GPU scheduling (H100/H200/L40S/A100/Blackwell), ready 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 buildDiscovery + workload-fitPhase 2 buildManaged 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
- GPU validated — A100, H100, H200, L40S, Blackwell
- 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 + workload-fit assessment (4 weeks)
- Phase 2 build (3-9 months)
- Phase 3 (optional) — managed AI platform
For sovereignty-emphasized workloads see Sovereign AI.
Ænix is the team behind Cozystack.
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. Aenix runs a workload-fit assessment during discovery to determine the break-even point for a given workload.
What GPUs and inference stacks does Aenix support?
Aenix has validated A100, H100, H200, L40S, and Blackwell GPUs. 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?
The engagement runs in three stages: a 4-week discovery and workload-fit assessment, a Phase 2 build lasting 3-9 months, and an optional Phase 3 in which Aenix operates the AI platform 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 productized, turnkey platform with multi-tenant GPU scheduling and ready blueprints for inference, fine-tuning, and RAG. The AI platform build is the services engagement that designs and delivers a custom platform end-to-end, usually on top of that product.
