A private AI cloud, governed end to end.
"Private" gets used for anything from a VPC peering arrangement to a logo on a shared SaaS tenant. For regulated organizations it should mean something specific: your data, models, and agent runtime operate inside a boundary you control — jurisdictionally, contractually, and technically.
What "private" actually means
Three tests separate a private AI cloud from privately-branded shared infrastructure:
- Jurisdiction: who can be legally compelled to access your data? EU-operated infrastructure under EU law answers differently than a US hyperscaler's EU region under the CLOUD Act.
- Tenancy: is compute, storage, and the vector database yours, or shared with other customers behind logical separation?
- Operations: who holds the keys, applies the updates, and can read the logs — you, or a provider whose staff sit outside your jurisdiction?
Deployment models: shared to air-gapped
NeuroCluster runs the same platform — governed agents, AI gateway, policy gates, evidence packs — across four deployment profiles, so you can start fast and move inward as requirements harden:
- Shared cloud (EU): fastest validation; tenant-isolated on EU-operated infrastructure. Fits pilots and non-sensitive workloads.
- Dedicated tenant: your own compute, storage, and vector database on EU infrastructure — the default for regulated production workloads.
- On-premises: the full platform on your Kubernetes and GPU clusters, operated with GitOps. Your datacenter, your keys.
- Air-gapped: fully disconnected deployment for defense, critical infrastructure, and classified environments.
Models you can run
The AI gateway routes across open-weight models running inside your boundary (Qwen, Mistral, and other open models on your GPUs) and — where policy allows — external commercial models. Routing is policy-controlled per workflow: a sensitive casework agent can be pinned to in-boundary models while a public-content assistant uses external ones.
This is the practical answer to model lock-in: the governance layer, data layer, and evidence trail stay constant while models change underneath.
Security, residency, and evidence
Identity-bound agent execution, row-level data access policies, classification-aware retrieval, and sandboxed code execution come standard across all profiles. Every deployment exports the same evidence packs — deployment snapshot, policies, approvals, action logs — so a workload that starts in shared cloud and moves on-premises keeps its audit continuity.
Residency documentation, subprocessor lists, and the procurement pack for vendor review are in the Trust Center.
Frequently asked questions
How is a private AI cloud different from a hyperscaler's EU region?
An EU region of a US-headquartered provider still operates under US legal reach (CLOUD Act), and its control plane and staff typically sit outside your jurisdiction. A private AI cloud puts data, models, and operations inside a boundary you control — EU-operated infrastructure, a dedicated tenant, or your own datacenter.
Can we start shared and move to dedicated or on-premises later?
Yes — that is the intended path. The platform is identical across profiles, so workflows, policies, and audit history carry over when you move inward.
Which models run in a private deployment?
Open-weight models (Qwen, Mistral, and others) run on your GPUs inside the boundary. The AI gateway can also route to external commercial models per workflow where your policy allows it.
What does on-premises operation look like day to day?
The platform deploys on your Kubernetes with GitOps-based operations: declarative configuration, versioned changes, and updates you approve and apply inside your boundary.
Keep evaluating
Shared vs dedicated vs private AI cloud
The deployment-model comparison: isolation, residency, operations, and cost profile.
European sovereign cloud providers compared
How European providers and hyperscaler sovereign offerings differ on jurisdiction and control.
Platform architecture
The eight platform layers, from GPU compute to evidence export.
On-premise AI deployment
Air-gapped and on-premises profiles for the strictest environments.