AZURE-CONSISTENT AI & DATA IN YOUR OWN DATACENTRE

AI & Data on Azure Local

Run the AI OS on Azure Local — Azure-consistent infrastructure operating inside your own datacentre, so your data, models, and prompts never leave your perimeter while you keep cloud-grade operations on hardware you control. We can also run Foundry Local on Azure Local on your own Arc-enabled cluster, in connected or fully disconnected mode.

Book a DemoCommon Questions

In Your Perimeter

Azure-consistent infrastructure runs in your own datacentre or edge site — data never leaves your control.

Trusted-Launch Hardening

Secure Boot, a virtual TPM, VBS memory isolation, and boot-integrity attestation harden every VM — on hardware in your own datacentre.

Foundry Local

Foundry Local on Azure Local serves models from your own Arc-enabled cluster — in connected or fully disconnected mode.

Definition

AI & data on Azure Local means running AI and analytics workloads on Microsoft's Azure Local (formerly Azure Stack HCI) — Azure-consistent infrastructure deployed in your own datacentre or edge location — so data and models stay physically within your perimeter and under your control, with Trusted-launch VM integrity and boot attestation available on your own hardware. The AI OS can also serve models through Foundry Local on Azure Local, running on your own Arc-enabled cluster, in connected or fully disconnected mode.

Hyperscale cloud is not always an option: data residency rules, sovereignty mandates, latency, or disconnected sites can require AI to run where the data lives. Azure Local brings Azure-consistent infrastructure into your own datacentre. Running the AI OS on Azure Local gives you the operational model of the cloud while data, models, and prompts stay physically inside your perimeter.

Where it fits

AI & Data on Azure Local in the Scrydon platform

One integrated, sovereign architecture. Here is where AI & Data on Azure Local sits — highlighted against the full stack it works with.

Cortex: Conversational Intelligence

Human + AI Orchestration

Sync CRM
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Welcome

The AI OS for Humans & AI Agents

Analytics

Revenue Overview — Q2 2026
Connected to Cognitive Enterprise
Revenue
€4.2M
+12%
Pipeline
€11.7M
+8%
Churn
2.1%
−0.3pp
Monthly RevenueJan – Dec 2025
JanMarJunSepDec
3rd
party

Personal AI

C

Cognitive Enterprise

Customer
Account
Order
Product
Contract
LineItem
Supplier
Billing
holds
placed
of

Ontology & Semantic Layer, one connected model for your data, knowledge & processes

Lakehouse

Combining the best of data lakes, data warehouses and search

TablesKnowledge

AI Agents

AI agents, workflows & automations that execute across your systems

AI Workflows

Integrations

Integrate across A2A, MCP, legacy systems and data sources

Data Spaces

Secure domain federation, trusted data sharing, and cross-boundary intelligence

Sovereign Foundations

Deploy from Air-gapped to Hyperscale
A closer look

AI & Data on Azure Local in depth

Sovereign Foundations

Observability
Full-stack monitoring & alerting
Zero-Trust
Continuous verification
Automation
GitOps & policy-as-code
Key Management
HSM-backed secrets
Kubernetes
Sovereign cluster orchestration
Identity
Federated IAM (SAML/OIDC)

The AI OS only works if it can be trusted. Every layer of the platform rests on a zero-trust infrastructure and identity foundation that operates consistently from fully air-gapped on-premises deployments through to hyperscale cloud environments. Sovereignty is not a feature added on top — it is the condition under which everything else operates.

  • Zero-trust architecture: Continuous verification for every request, every user, and every workload — no implicit trust, even inside the perimeter.
  • Federated identity: Seamless integration with your existing IdP (SAML, OAuth 2.0, OIDC) for unified, policy-enforced access control.
  • Air-gapped deployment: Run the complete platform with no external network dependencies — ideal for defence, critical national infrastructure, and classified workloads.
  • Confidential computing: Hardware-level encryption of data in use via AMD SEV-SNP and Intel SGX, protecting workloads even from infrastructure administrators.

Deployment Options: From Air-gapped to Cloud

Air Gapped

  • No external network paths
  • Offline signed updates
  • Strict audit trails

On Premise

  • Runs in your datacenter
  • Identity-integrated & compliant
  • Full operational control

European Cloud

  • EU data residency
  • GDPR-first architecture
  • Managed sovereign services

Confidential Compute

  • TEE-protected workloads
  • Encrypted memory & attestations
  • Secure ML inference

Public Cloud

  • Multi-cloud compatible
  • Policy-driven data controls
  • Hybrid & burst-ready
HOW IT WORKS

Azure-consistent AI inside your own perimeter

The AI OS deploys onto Azure Local so the entire AI workload — data, model weights, and prompts — is processed on infrastructure that physically lives in your datacentre or edge location. You keep Azure-consistent operations and tooling without sending sensitive data to a public region.

  • Azure-consistent stack — Run Azure-aligned infrastructure and services on your own hardware, in your own datacentre or at the edge.

  • Trusted-launch VMs — Secure Boot, a virtual TPM, VBS memory isolation, and boot-integrity attestation harden every VM on your own hardware.

  • Boot-integrity attestation — Attest that a VM started in a known-good state before secrets, keys, or models are provisioned — the same zero-trust gate used in the cloud.

  • Sovereign key management — You hold the keys and the hardware, in your own datacentre — Microsoft operates nothing inside your perimeter.

WHY AZURE LOCAL

Cloud-grade operations without the data leaving your walls

Regulated and sovereignty-conscious organisations often want the operational model of Azure but cannot place sensitive data in a public region. Azure Local resolves the tension: you get Azure-consistent infrastructure, tooling, and confidential computing while data and models stay physically inside your perimeter — the same zero-trust posture the AI OS applies everywhere, from air-gapped on-premises through to hyperscale cloud.

FOUNDRY LOCAL

Foundry Local on Azure Local, on your own cluster — connected or fully disconnected

The AI OS can serve models through Foundry Local on Azure Local, running on your own Azure Local hardware, inside your perimeter. It deploys as an Azure Arc extension onto an Arc-enabled Kubernetes cluster, so inference is served by your cluster rather than by any single machine, and the AI OS orchestrates it the same way it orchestrates any other model — connected to Azure for management and updates, or fully disconnected with no outbound network at all. The capability is currently in preview and enabled by request.

  • In-cluster inference — Foundry Local on Azure Local serves models from your own Arc-enabled Kubernetes cluster — no inference call leaves your perimeter.

  • Connected mode — Operate connected to Azure for centralised management, model updates, and Azure-consistent tooling.

  • Fully disconnected mode — Run it with no outbound network at all — suitable for air-gapped and sovereignty-constrained sites.

  • Orchestrated by the AI OS — Its models are governed and orchestrated by the AI OS alongside your data, agents, and ontology.

THE DIFFERENCE

Microsoft Fabric, Databricks, and Foundry do not run in your datacentre at all. Our solution does.

The mainstream Azure analytics and AI platforms — Microsoft Fabric, Databricks, and Azure AI Foundry — are cloud services that process your data in public regions, not in your datacentre. Fabric and Azure AI Foundry run on standard, non-confidential compute. Azure Databricks can run its classic compute clusters on confidential VMs, but only in a public Azure region in your own subscription, and its control plane stays in Databricks' account either way. The AI OS runs the same class of analytics and AI workloads on Azure Local inside your own datacentre, so your data, models, and prompts stay within your perimeter and encrypted in use.

  • Fabric, Databricks, Foundry — Cloud services in public Azure regions — your data leaves your perimeter and is processed there, whatever compute it lands on.

  • The AI OS on Azure Local — Runs in your own datacentre on hardware you control — data and models stay physically within your perimeter, on Trusted-launch VMs, out of reach of any external cloud operator.

FAQ

Frequently asked questions

How can I run AI and data on Azure Local?+
Deploy the AI OS onto Azure Local (formerly Azure Stack HCI): your AI and data workloads run on Azure-consistent infrastructure inside your own datacentre or edge site, so data, models, and prompts stay physically within your perimeter. You get a cloud-grade operating model with Trusted-launch VMs (Secure Boot, vTPM, VBS memory isolation) on hardware in your own datacentre, and you can connect to Foundry Local on Azure Local for in-cluster model inference in connected or fully disconnected mode. It is the same AI OS, ontology, agents, and analytics you would run in the cloud — just resident on your own hardware.
What is Azure Local?+
Azure Local (formerly Azure Stack HCI) is Microsoft's Azure-consistent infrastructure that runs in your own datacentre or edge location rather than a public Azure region. It lets you operate Azure-aligned services and tooling on your own hardware, so data can stay physically within your perimeter.
Can you run Foundry Local on Azure Local?+
Yes — the product for this is Foundry Local on Azure Local, which deploys as an Azure Arc extension onto an Arc-enabled Kubernetes cluster on your own hardware, inside your perimeter. Run it in connected mode (linked to Azure for management and updates) or fully disconnected mode (no outbound network at all, suitable for air-gapped sites). The AI OS orchestrates its models alongside your data, agents, and ontology. Note this is distinct from Foundry Local, which Microsoft scopes to inference on an end user's own device and does not design as a server inference stack. The Azure Local variant is currently in preview and enabled by request. See Microsoft's documentation for more detail.
Do Microsoft Fabric, Databricks, and Azure AI Foundry run inside my datacentre?+
No. Microsoft Fabric and Azure AI Foundry are cloud services that process data in public Azure regions on standard, non-confidential compute, so your data leaves your perimeter and is decrypted in memory while in use. Azure Databricks can run classic compute clusters on confidential VMs, but only in a public Azure region in your own subscription — never in your datacentre — and its control plane runs in Databricks' account regardless. The AI OS is different: it runs the same class of analytics and AI workloads on Azure Local inside your own datacentre, on hardware you control, so data, models, and prompts stay physically within your perimeter.
How is AI data kept secure on Azure Local?+
Azure Local VMs support Trusted launch — Secure Boot, a virtual TPM, VBS memory isolation, and boot-integrity attestation — and the hardware sits in your own datacentre, so AI inference and training run on infrastructure you physically control. Hardware Trusted Execution Environments (AMD SEV-SNP, Intel TDX confidential VMs) are an Azure public-region capability; on Azure Local the protection comes from your own perimeter plus Trusted-launch VM integrity.
Is data protected from Microsoft and administrators on Azure Local?+
Yes — primarily because the hardware is in your own datacentre, under your own administrators, not Microsoft's. Trusted launch adds Secure Boot, a virtual TPM, VBS memory isolation, and boot-integrity attestation, and the VM guest state (including the vTPM) is protected by a key held in your own local key vault. Microsoft runs no infrastructure inside your datacentre, so the cloud-operator access question does not arise the way it does in a public region. Hardware Trusted Execution Environments that encrypt memory from the host (AMD SEV-SNP, Intel TDX confidential VMs) are an Azure public-region capability rather than an Azure Local one.
How is Azure Local different from running in a public Azure region?+
Azure Local gives you Azure-consistent operations and tooling, but the infrastructure physically lives in your own datacentre or edge site rather than a Microsoft region. That keeps data resident and within your perimeter — important for sovereignty, data-residency rules, latency, and disconnected or edge locations — while you keep a cloud-grade operating model.
Does the AI OS run the same way on Azure Local as in the cloud?+
Yes. The AI OS, its ontology, agents, and insights run consistently from fully air-gapped on-premises deployments, through Azure Local in your own datacentre, to hyperscale cloud. Sovereignty never depends on where you deploy — the same zero-trust controls apply everywhere.
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