Foundry Local Goes Sovereign: Running Large AI Models Fully Disconnected on Azure (Feb 2026)

TL;DR: On February 24, 2026, Microsoft announced that its Sovereign Cloud stack — Azure Local, Microsoft 365 Local, and Foundry Local — can now run large, multimodal AI models in completely air-gapped, disconnected environments. For .NET and Azure engineers building for defense, healthcare, finance, or any regulated sector, this means your AI inferencing stack can finally live entirely inside your organization’s sovereign boundary, no internet required.

What Just Shipped

On February 24, 2026, Douglas Phillips (President and CTO, Microsoft Specialized Clouds) announced a major expansion of the Microsoft Sovereign Cloud stack. The headline feature for AI engineers: organizations can now bring large AI models into fully disconnected, sovereign environments with Foundry Local — using modern infrastructure from partners like NVIDIA, customers with sovereign needs will now be able to run multimodal models locally on their own hardware, inside strict sovereign boundaries, enabling powerful local AI inferencing. This is the “offline enterprise AI” story that regulated industries have been waiting for. Sovereign Private Cloud unifies Azure Local, Microsoft 365 Local, and Foundry Local, bringing modern infrastructure, productivity, and support for large AI models to any operational boundary — delivering a truly localized full-stack experience designed to stay resilient across any connectivity condition.

In other words: your AI agents can keep running even if someone cuts the fiber. Which, in classified or critical-infrastructure scenarios, is not a hypothetical.

The Three-Layer Stack — What It Means for You

Azure Local disconnected operations (now available): Organizations can now run mission-critical infrastructure with Azure governance and policy control, with no cloud connectivity, optimizing continuity for sovereign, classified, or isolated environments. Microsoft 365 Local disconnected (now available): Core productivity workloads — Exchange Server, SharePoint Server, and Skype for Business Server — can run fully inside the customer’s sovereign operational boundary on Azure Local, keeping teams productive even when disconnected from the cloud. Foundry Local (new AI layer): Organizations can now bring large AI models into fully disconnected, sovereign environments with Foundry Local. From an engineering perspective, the key detail is that with Azure Local disconnected operations, management, policy, and workload execution stay within the customer-operated environments, so services continue running securely even when environments must be isolated or connectivity is not available — using familiar Azure experiences and consistent policies, organizations can deploy and govern workloads locally without depending on continuous connection to public cloud services. That last sentence matters a lot: consistent Azure policies and APIs on-prem. You’re not learning a separate stack.

Why This Is Bigger Than “Just” On-Prem AI

As digital sovereignty becomes a strategic requirement, organizations are rethinking how they deploy critical infrastructure and AI capabilities under tighter regulatory expectations and higher risk conditions — Microsoft’s approach to sovereignty is grounded in enabling enterprises, public sectors and regulated industries to participate in the digital economy securely, independently and on their own terms. The Sovereign Private Cloud model is delivered via Azure Local and Microsoft 365 Local, and is ideal for defense, critical infrastructure, and national security scenarios. For .NET engineers, this means the same Azure.AI.Projects SDK surface you’re already targeting in the public cloud is the surface you’ll target on-prem. No forked codebases, no custom shims. (At least, that’s the promise — validate carefully before going all-in.)

Practical Integration Steps

The Azure SDK for .NET already ships the foundation. The January 2026 Azure SDK release brought feature support for Microsoft Foundry Agents Service, integration with the new Azure.AI.Projects.OpenAI package, expanded evaluation capabilities, and more — a significant expansion of AI capabilities for .NET developers working with Azure AI services. Here’s a minimal pattern to scaffold a Foundry-compatible agent in C# that will work whether the endpoint is public cloud or a sovereign on-prem Foundry Local deployment — you just swap the endpoint URI:

using Azure.AI.Projects;
using Azure.Identity;

var client = new AIProjectClient(
    new Uri("https://<your-foundry-local-endpoint>"),
    new DefaultAzureCredential());

// List available agents — same API surface on sovereign and public cloud
var agents = client.GetAgentsAsync();
await foreach (var agent in agents)
{
    Console.WriteLine($"Agent: {agent.Name} | Model: {agent.Model}");
}

And for deploying your own MCP tool server (a popular pattern for giving agents access to enterprise data), spin up a C# Azure Functions project in seconds:

# Scaffold a .NET MCP server with azd
azd init --template remote-mcp-functions-dotnet -e my-mcp-server

# Run locally (requires Azurite for storage emulation)
azd up

This is a quickstart template to easily build and deploy a custom remote MCP server to the cloud using Azure Functions — you can clone, restore, and run on your local machine with debugging, then azd up to have it in the cloud in a couple of minutes. The MCP server is configured with built-in authentication using Microsoft Entra as the identity provider, and you can also use API Management to secure the server, as well as network isolation using VNET. For cost-sensitive sovereign deployments: the Flex Consumption plan follows a pay-for-what-you-use billing model — completing a quickstart incurs a small cost of a few USD cents or less in your Azure account. On disconnected Azure Local, you’ll be working with dedicated hardware instead, but the same Bicep/IaC templates apply.

Cost, Latency, and Compliance Considerations

Concern	Public Cloud Foundry	Sovereign / Disconnected Foundry Local
Cost model	Pay-per-token / serverless	CapEx (dedicated NVIDIA HW)
Latency	Network round-trip to Azure region	Local inferencing — lowest possible latency
Compliance	Data residency controls, EU Data Boundary	Full data sovereignty; no egress
Model catalog	Full Microsoft Foundry catalog	Models must be pre-provisioned on-prem
SDK surface	`Azure.AI.Projects` v2 beta	Same SDK, different endpoint URI

⚠️ One caveat to flag: The Azure Machine Learning SDK v1 reaches end of support on June 30, 2026 — after this date, existing workflows may face security risks and breaking changes without active Microsoft support. If you have any v1-based pipelines feeding your current AI workloads, migrate now before sovereign deployments add complexity.

The MCP Connection

This sovereign push pairs neatly with the Azure Functions MCP GA story from January. Microsoft has moved its Model Context Protocol support for Azure Functions to General Availability, signaling a shift toward standardized, identity-secure agentic workflows — by integrating native OBO authentication and streamable HTTP transport, the update aims to solve the “security pain point” that has historically prevented AI agents from accessing sensitive downstream enterprise data. In a sovereign scenario, your MCP tool server (exposing, say, a classified document store or a regulated healthcare API) runs on Azure Local, your Foundry Local model calls it over local network, and the entire transaction never leaves your sovereign boundary. That’s a legitimately new architectural option that didn’t exist cleanly six months ago. The platform integrates directly with Azure AI Foundry, allowing agents to discover and invoke MCP tools without additional configuration layers. —

Key Takeaways for Engineers

Foundry Local now handles large models disconnected — test your Foundry-based agents against a sovereign endpoint from day one, not as an afterthought.
Same SDK, different endpoint — Azure.AI.Projects v2 is the canonical SDK for both public and sovereign Foundry deployments. Pin to the beta and migrate off azure-ai-agents now.
MCP + Azure Functions is the recommended tool-serving pattern — Entra-backed, serverless-scalable, and ready for VNET isolation on sovereign deployments.
AzureML SDK v1 EOL is June 30, 2026 — don’t let sovereign migration projects get blocked by a dependency on a deprecated SDK.
Hardware matters — sovereign Foundry Local requires NVIDIA-class on-prem GPU infrastructure; factor that into your cost/compliance trade-off conversation.