You own the AI/ML platform, and the constraints are stacking up: GPU cost and scarcity, data that can’t leave the jurisdiction, and customers who won’t accept a US model endpoint. Run training and inference on your own GPUs, multi-tenant, with no hyperscaler lock-in. Ænix delivers that as a turnkey AI platform, or builds it with you.
Pairs with: Sovereign AI and Ænix AI Platform for one-click GPU inference, or AI Platform Build to design it to your stack. Open core: Cozystack.
Where you are right now
- GPU spend and scarcity make hyperscaler instances expensive and hard to get.
- Training data is sensitive or regulated and can’t go to a third-party endpoint.
- You need multi-tenant GPU sharing across teams, not a cluster per project.
- You want production inference (vLLM/LLM serving) without re-platforming each time.
What you’re actually trying to do
Give data scientists and product teams self-service GPU — for training and for serving models — on infrastructure you control, so cost, data location and model choice stay your decision. That means multi-tenant GPU scheduling, one-click inference, and a path that doesn’t lock you to a single provider’s endpoints or pricing.
Two ways Ænix helps you
1. Run a turnkey AI platform. Ænix AI Platform adds GPU scheduling and one-click LLM/vLLM inference to the multi-tenant Cozystack core — self-service for your teams, on your hardware, with enterprise SLA.
2. Build your own, with our team. Cozystack is the framework; Ænix is your outsourced engineering team for an AI platform build — GPU topology, scheduling, inference serving and sovereign-AI controls designed around your models and data.
Quick facts
- What it is: a multi-tenant GPU platform for training and inference on your own hardware.
- Who it’s for: Heads of AI/ML, MLOps leads, AI platform owners.
- Control: your GPUs, your jurisdiction, your model choice — no hyperscaler endpoint dependency.
- License: Apache 2.0 core (Cozystack) — no per-GPU platform tax.
- Status: built on Cozystack, CNCF project (Sandbox 2025-02-28; Incubating expected late summer 2026).
- Common pitfall: prototyping on a hyperscaler endpoint, then discovering the data class can’t legally go there in production.
[Source: CNCF Landscape, Cozystack docs]
Why AI/ML leaders pick Ænix
- Sovereign by construction. Sensitive data and models stay on your hardware, in your jurisdiction — not on a third-party endpoint.
- Multi-tenant GPU, not silos. Share scarce GPUs across teams with quotas and isolation.
- Authors, not resellers. The team behind Cozystack designs and supports the platform.
FAQ
Can we do both training and inference? Yes — GPU scheduling for training plus one-click LLM/vLLM serving for inference on the same multi-tenant platform.
Do our models have to leave our infrastructure? No. Models and data stay on your GPUs in your jurisdiction; you choose open or self-hosted models, not a fixed vendor endpoint.
How do teams share scarce GPUs? Multi-tenant scheduling with quotas and isolation, so teams self-serve without a dedicated cluster each.
Build or buy? The AI Platform for speed; the build-with engagement when GPU topology and serving need to fit your stack. The call scopes it.
How does this relate to sovereign AI rules? See sovereign AI — running on customer-controlled hardware is the structural answer to data-class and endpoint restrictions.
Start with a 30-minute discovery call
Free, no prep. We look at your GPU footprint and model/data constraints and tell you whether the AI Platform or a build-with engagement fits.
Ænix is the team behind Cozystack — a CNCF project (Sandbox today; Incubating expected late summer 2026), Apache 2.0. Ænix commercializes it as Ænix Platform as three platforms on one engine — Public Cloud, Private Cloud and AI — that combine rather than exclude each other.