Which AI Platform Best Supports Hybrid Cloud Deployments in Highly Regulated Environments?

Which AI platform best supports hybrid cloud deployments in highly regulated environments?

For most regulated US organizations already standardized on Microsoft 365 and Entra ID, Microsoft is the strongest hybrid answer, because Azure Government is authorized at FedRAMP High (Microsoft’s Compare Azure Government and global Azure article, last updated 2025-08-21) and Foundry Local on Azure Local runs inference on your own Arc-enabled Kubernetes cluster under the same identity plane. Choose Google Distributed Cloud air-gapped instead when the requirement is a genuinely disconnected or classified enclave, which is the case its documentation is written for. Amazon Bedrock is the strong choice for an AWS-native estate and is authorized at FedRAMP Class D (formerly High) and DoD IL4 and IL5 for a named model list in AWS GovCloud (AWS’s Amazon Bedrock models certification status page, last updated September 18, 2026), but AWS documents it as a serverless Region service, so the on-premises half of a hybrid design has to be solved somewhere else.

By Matt Lawson, Senior Consultant, i3solutions. Matt Lawson has spent more than 14 years at i3solutions guiding complex enterprise technology work from scope and system design through development, acceptance, and long-term support, with expertise spanning technical oversight, solution architecture, SharePoint and Microsoft 365 delivery, Power Platform implementation, business intelligence, enterprise search, and the practical application of AI to real business and operational challenges.

The hybrid AI programs that stall in regulated organizations almost never stall on the model. They stall because the architect and the security reviewer used the word hybrid to mean two different architectures for six weeks without noticing. One had in mind private networking to an accredited cloud region. The other had in mind inference running on hardware inside a boundary, with no path out. Those are different products, from different vendors, at different levels of maturity, and the gap only surfaces at the security review, which is the most expensive place for it to surface.

So the useful comparison is not a leaderboard. It is a narrower question with a checkable answer: which vendors actually publish documentation for running their own models on hardware you control, and what accreditation do they publish for the cloud half that has to sit alongside it. Three do, in different shapes, and the differences are large enough to decide an architecture. Everything below is quoted from the vendors’ own current documentation, with the revision date each page carries, because this is exactly the kind of ground where a remembered fact from six months ago is wrong.

The rest of this page is the documentation behind those three sentences, the five criteria that decide between them, and the two constraints that flip the answer before any of the criteria get a vote.

1. “Hybrid” is three different architectures, and only one of them is hard

Before comparing platforms, separate the shapes. In regulated work the word hybrid gets used for three arrangements that place completely different demands on a vendor.

1. Cloud inference over private networking. The model runs in the vendor’s accredited cloud region. Your data reaches it over private endpoints or a dedicated circuit rather than the public internet, and identity and logging stay in your tenant. This is what most organizations mean when they say hybrid, and every major vendor supports it.
2. Local inference under central management. The model runs on hardware you own, in your data center or at an edge site, while policy, model catalog, and lifecycle are managed from the cloud control plane. This is the shape that satisfies a data-residency or latency constraint without giving up centralized governance.
3. Disconnected or air-gapped inference. The model runs on your hardware with no connectivity back to the vendor at all, in perpetuity. This is the shape classified and some critical-infrastructure environments require, and it is the one that separates vendors, because it demands a whole product built to be operated without a phone-home path.

Shape one is a networking exercise, and every serious vendor solves it, which is why the honest comparison lives in shapes two and three, where a vendor either has built a product for your hardware or has not. If your requirement turns out to be only shape one, this decision is far less consequential than it feels in the room, and you should optimize for where your data and identity already sit rather than for AI features.

2. What Microsoft documents

Microsoft’s cloud half is the most heavily documented of the three for US regulated work. The Compare Azure Government and global Azure article, last updated 2025-08-21, states that “both cloud environments are assessed and authorized at the FedRAMP High impact level,” and that Azure Government “provides an extra layer of protection to customers through contractual commitments regarding storage of customer data in the United States and limiting potential access to systems processing customer data to screened US persons.” The same article’s service-endpoint table lists Azure OpenAI Service with a separate Azure Government endpoint at openai.azure.us, alongside separate government endpoints for AI Search, Document Intelligence, Speech, and Translator.

Two boundary notes on that page are worth reading before anyone quotes it in an architecture document. First, its own scope disclaimer: “These lists and tables do not include feature or bundle availability in the Azure Government Secret or Azure Government Top Secret clouds. For more information about specific availability for air-gapped clouds, please contact your account team.” If your target is one of those clouds, this article is not your source. Second, it carries a hybrid and multicloud section documenting Edge RAG Preview enabled by Azure Arc in Azure Government, and notes that “Installation of the Edge RAG extension is only available for Azure Government by using Azure CLI.” A retrieval-augmented pattern running on Arc-managed infrastructure inside a government cloud is precisely the shape-two architecture, and it is documented as preview.

For the on-premises half, the newer and more substantial answer is Foundry Local on Azure Local. Microsoft’s Foundry Local on Azure Local overview, last updated 2026-08-03, states that it “brings AI inference to your Azure Local environment,” that you “Deploy and run AI models on an Arc-enabled Kubernetes cluster with Kubernetes-native operations,” and that the point is to “Keep your data processing on-premises where your data is generated.” Architecturally, it “runs on an Arc-enabled Kubernetes cluster and is deployed as an Azure Arc extension,” with an operator-based control plane and declarative Model and ModelDeployment resources.

Three published capabilities matter for a regulated design. Inference endpoints are secured “using API keys, Microsoft Entra ID authentication, and TLS-enabled gateway API patterns,” which keeps the on-premises half inside the same identity plane as the cloud half rather than introducing a second one. The listed use cases include the ability to “Operate in disconnected environments where internet connectivity isn’t available, with a deployment model consistent with connected scenarios.” And the stated reason to choose it leads with the compliance case: “Keep inference and data processing on-premises for sovereignty or regulatory requirements.”

The limits are the part that changes a plan. The same article states plainly that “Foundry Local is available in preview,” that “Features, approaches, and processes can change or have limited capabilities before general availability (GA),” and that deployment “is currently available by request during preview” through an access request form. Its supported-region list for the Arc extension names commercial Azure regions only, with no US Government region among them. Read that as published: it is a statement about where the extension is offered, not a statement that a government-cloud path does not exist. Our recommendation on that is committed rather than conditional. Do not put a production regulated workload on a preview service with a request-gated deployment path. Run the workload in Azure Government now, where the accreditation is documented today, and schedule the on-premises inference tier as a second phase gated on general availability in your cloud. A pilot on the preview path is a good use of a quarter, scoped to a dataset you could move back. A system of record is not.

3. What AWS documents

AWS has the clearest published answer of the three on the cloud half for defense work. In an announcement dated June 25, 2026, AWS states that OpenAI GPT, OpenAI GPT OSS, and NVIDIA Nemotron models now have FedRAMP High and DoD IL-4/5 approval in AWS GovCloud (US). DoD CC SRG is the Department of Defense Cloud Computing Security Requirements Guide, and its impact levels are the tiers it uses for data sensitivity. That is a specific, dated, model-level authorization statement, and it is a genuinely strong position.

The service documentation fills in the rest. The Amazon Bedrock in AWS GovCloud (US) page lists availability in the AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions, and points to AWS’s Amazon Bedrock models FedRAMP and DoD CSP SRG (IL4/IL5) certification status page, last updated September 18, 2026, which enumerates the models carrying FedRAMP Class D (formerly High) and IL4/5 authorization, among them the Titan Text Embeddings G1 and V2 models, Claude Sonnet 4.5, Claude 3.7 Sonnet, Claude 3.5 Sonnet, Claude 3 Haiku, and Llama 3 8B and 70B. One caveat on the GovCloud page specifically: unlike the Microsoft articles cited here, AWS does not stamp it with a visible revision date, and the model list is exactly the kind of content that moves, so treat the copy in this paragraph as a pointer and open the status page on the day you decide.

The architectural fact that decides the hybrid question is in the same page’s own description of the service: “Since Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications.” Serverless is a feature, and for a cloud-resident workload it is a considerable one. It is also a statement about where the service lives. AWS’s published Bedrock documentation defines availability by Region, and we found no AWS documentation describing Bedrock inference running on customer-owned hardware. Absence of documentation is not a prohibition, so do not read that as AWS saying no. Read it as unresolved, and then decide anyway: design the on-premises half of an AWS hybrid architecture on something other than Bedrock. An architecture that depends on an undocumented capability arriving is a schedule risk with no mitigation available to you, and by the time it resolves you have already spent the quarter.

The export-control detail on that page also deserves a mention, because it is the sort of thing that surfaces late in a review. It documents that certain customer-defined metadata, including custom model metadata and provisioned-throughput metadata for the no-commit option, “may leave the AWS GovCloud (US) Regions only when the customer asks AWS to investigate a reported issue,” and that Bedrock model evaluation metadata “is not permitted to contain export-controlled data.” Those are workable constraints. They are not workable if nobody reads them until an ITAR review.

4. What Google documents

Google’s answer is the most distinctive, because it is built from the disconnected end rather than the cloud end. The Google Distributed Cloud air-gapped overview in the Google Cloud documentation carries a last-updated stamp of 2026-08-11 UTC. It states that GDC air-gapped “lets you host, control, and manage infrastructure and services directly on your premises,” and that it “does not require connectivity to Google Cloud and helps customers meet compliance and regulatory requirements.” The same Google Cloud documentation names a FIPS 140-2 certified Rocky Linux long-term support operating system underneath.

Google Cloud’s own announcement, published April 9, 2025, describes GDC as “a fully managed on-prem and edge cloud solution that is offered in both connected and air-gapped options, scaling from a single server to hundreds of racks.” The same announcement states that the GDC air-gapped product is authorized for US Government Secret and Top Secret missions and that Gemini is available on it. That is a vendor placing a frontier model inside an enclave with no return path, which is a capability the other two do not describe in the same terms.

The cloud half has a different shape and its own conditions. Google Cloud’s deployment guidance for Gemini for Government carries a last-updated stamp of 2026-08-14 UTC. It is explicit that what the platform supports turns on configuration rather than arriving by default: only models that support US multi-regional endpoints offer data residency commitments and can support FedRAMP High and DoD IL4 and IL5 deployments, and the guidance directs you to deploy inside an Assured Workloads folder configured for your specific compliance regime and to select US Multi-region as the app location. That is a build instruction. Opening a Google Cloud project and switching Gemini on leaves every one of those choices unmade, and the folder configuration is work somebody in your organization has to own and evidence.

One honest limit on this section. Google’s public documentation for the air-gapped product is thinner on model-by-model authorization detail than Microsoft’s Azure Government material or AWS’s model list, and much of the strongest public evidence sits in announcements rather than reference documentation. That changes how you should run the evaluation, not whether you should. Put Google on the shortlist only when the disconnected requirement is real, and when it is, run the evaluation as a direct engagement with Google under agreement rather than as a documentation exercise, because the material that would settle it is not published.

5. The three platforms, side by side

Table sources and as-of dates. ¹ Microsoft, Compare Azure Government and global Azure, last updated 2025-08-21. ² AWS, Amazon Bedrock models FedRAMP and DoD CSP SRG (IL4/IL5) certification status, last updated September 18, 2026, and AWS’s announcement of June 25, 2026. ³ Google Cloud, Deployment guidance for Gemini for Government, last updated 2026-08-14 UTC. ⁴ Google Cloud’s announcement of April 9, 2025, which states that the GDC air-gapped product is authorized for US Government Secret and Top Secret missions. Each page was read on September 18, 2026.

Read the table by column rather than by row. Microsoft is one governance and identity plane stretched across two locations. AWS is a very strong single location. Google is a platform built for the location most vendors cannot reach.

6. The five criteria that actually decide it

Where the data and the identities already live. This outranks every model benchmark and it is not close. If your content is in SharePoint and Microsoft 365, your users are in Entra ID, and your DLP policy is written in Purview, then choosing a non-Microsoft AI platform means building a second identity path, a second logging path, and a second policy surface for the sole purpose of reading data that is already governed. That cost is paid every month, by the team that has to evidence it at audit. The mirror image is equally true for an AWS-native estate.

Which of the three hybrid shapes you actually need. Most organizations that describe a hybrid requirement need shape one, private-network access to cloud inference, and can meet it on any of the three platforms. A minority genuinely need shape two, and a smaller minority need shape three. Establishing which one you are before the platform conversation removes most of the disagreement in the room, because the shape, not the vendor, is what creates the work.

Whether the constraint is a rule, a regulation, or a preference. “The data cannot leave our network” is sometimes a control from an authorizing official, sometimes an internal policy written before FedRAMP High existed, and sometimes an instinct. These three have different costs, and the check to run first is which of the three you are holding, because the answer changes what you are allowed to buy. Standing up and operating an on-premises inference cluster is a real ongoing commitment, and it is the right one when the requirement is real. It is an expensive way to honor a preference that a documented control inheritance would have satisfied.

Who will operate the on-premises half at three in the morning. A Kubernetes cluster with GPU nodes serving production inference is infrastructure with an on-call rotation attached. If that team does not exist and is not being hired, then a hybrid design chosen for compliance reasons is a boundary nobody is qualified to operate, which is worse for your risk posture than the cloud option you rejected.

How fast the ground is moving under the answer. Every load-bearing fact on this page comes from a vendor page that has been revised this year, and one of them describes a service still in preview. That is a property of the market, not a flaw in the research. It means the decision should be documented with its evidence and its revision dates attached, and revisited on a schedule, rather than settled once in a slide.

7. Where regulated buyers actually land

Most regulated buyers land in one of three places, and the wrong one is expensive to leave.

Microsoft-standardized enterprise, sensitive but unclassified data. Put the workload in Azure Government, use the Azure OpenAI and AI Search government endpoints, and keep the identity and DLP machinery you already run. Bring an on-premises inference tier in only where a specific dataset or latency requirement demands it, and go in with clear eyes about the preview status of Foundry Local on Azure Local. This covers the large majority of regulated US enterprises, and it wins on the boring criterion of one governance plane rather than on any AI capability.

Classified, disconnected, or physically isolated mission. Google Distributed Cloud air-gapped is the option whose documentation is written for exactly this and whose vendor names Secret and Top Secret authorization with Gemini available on the product, in Google Cloud’s announcement of April 9, 2025. If that is your requirement, this is the shortest path in the market today, and the evaluation should be run directly with Google under agreement.

AWS-native estate with a strong defense compliance requirement. Bedrock in GovCloud with the named FedRAMP Class D (formerly High) and IL4 and IL5 model list, per AWS’s certification status page last updated September 18, 2026, is a strong, dated, specific answer for the cloud half. Design the on-premises half separately and deliberately, on a platform chosen for that job rather than on an extension of Bedrock that AWS has not documented, and do not let a slide labeled “hybrid” paper over the fact that it is a different platform with a different identity model.

The failure mode that costs the most is none of these three. It is picking a platform to satisfy an unwritten constraint, discovering during the security review that the constraint was negotiable, and being left operating infrastructure you did not need. The cheapest hour in this whole process is the one spent getting the actual control written down before anyone opens a vendor comparison.

8. What this means for the engagement, and where our own experience sits

Stated plainly, so you can weigh what you are reading. Nothing on this page determines whether your deployment is compliant. That determination belongs to your organization and its authorizing official, and what a vendor publishes is an input to it rather than a substitute for it. The platform comparison above is drawn from the vendors’ published documentation, cited and dated so you can check every line yourself. Our own delivery experience is in the Microsoft stack: Azure and Azure Government, Microsoft 365 and Copilot, and the Power Platform, in regulated and government tenancies. When we recommend, that is the ground we recommend from, and a firm’s documentation reading is worth less to you than its delivery record. i3solutions runs comparative platform-selection evaluations for clients, recommending among IAM platforms for hybrid estates and among workflow automation platforms against SOC 2 and HIPAA, rather than only implementing the Microsoft option.

What that experience looks like on the accreditation side. i3solutions installs and helps configure applications inside IL4 and IL6 government cloud environments and other government networks. i3solutions runs a governed Power Platform for a federal defense agency supporting roughly 10,000 personnel across about 180 locations, which works because it is governed, not despite it. The detail is in our Modernizing Internal Operations Processes With Digital Transformation case study. On the compliance side, the reading is done as its own piece of work rather than as a restatement of a vendor page: i3solutions advises clients on federal compliance posture as its own assessment rather than as a restatement of Microsoft’s documentation, including whether SharePoint Online meets NIST 800-53, whether Azure Government is required under the DoD Cloud Computing SRG, and whether a CMMC gap assessment is needed to bid. That assessment is a named deliverable. The i3solutions Federal Compliance Assessment evaluates a client tenant against NIST SP 800-53 and CMMC using automated tenant configuration scripts and a 42-point security checklist. i3solutions engages its Federal Compliance Assessment when the scope spans a FedRAMP Moderate or FedRAMP High boundary, or when the client operates in a GCC High tenant.

On the governance side, which is where a hybrid AI estate actually succeeds or fails, the same discipline applies to the platform the agents and apps sit on. i3solutions governs client Power Platform tenants with the Center of Excellence Starter Kit, tenant-level and environment-level DLP policies, and managed environment controls. i3solutions runs client Power Platform work on a multi-tenant Center of Excellence model with separate Dev, Test, UAT, and Production environments promoted through managed solutions. i3solutions delivery includes ALM practices with Power Platform pipelines or Azure DevOps integration, environment separation strategies, and change control processes. And on the data layer that an AI workload will read: i3solutions selects Dataverse over SharePoint as the primary relational store when a client needs scalable high-volume transactional data, and holds application secrets in Azure Key Vault.

Two notes on scope and schedule, because a platform decision is usually not the expensive part. The assessment work sizes to the estate: i3solutions AI readiness assessments scope from a shallow tenant readiness scan at the low end to deep unstructured data discovery at the high end. AI readiness assessment cost variance at i3solutions is driven by custom semantic index building, multi-tenant vector database configuration on Azure AI Search, and prompt-engineering compliance reviews for regulated data. The larger surprise is almost always underneath the AI. The order-of-magnitude difference between AI readiness assessment quotes is data governance cleanup: when Dataverse and SharePoint access-control alignment has to precede a Copilot or OpenAI API integration, that remediation dominates the engagement. An AI platform does not create oversharing. It finds it, and then it quotes it to someone with a citation.

On timelines, hold the government-cloud reality next to the AI enthusiasm. How long a government-cloud deployment with a FedRAMP authorization takes, with control inheritance from Azure Government and without it, is covered in Azure Government Migration: What Moving from Azure Commercial Actually Takes.

What to require of the firm you engage

1. They cite the vendor page and its revision date, in front of you. Every fact that decides this moves on the vendors’ own publishing cycles, and one of the three on-premises options is in preview right now. A recommendation delivered without a link and a date is a memory wearing a citation.
2. They ask which of the three hybrid shapes you need before naming a platform. A firm that opens with a vendor rather than with your constraint is selling a partnership, not an architecture.
3. They make you produce the actual control. The written requirement, from the authorizing official or the framework, that says the data cannot go where you think it cannot go. If nobody can produce it, that is the finding, and it is usually worth more than the platform recommendation.
4. They name who operates the on-premises tier, by role, before recommending one. GPU nodes, cluster upgrades, model lifecycle, and an on-call rotation. If those have no owner in the plan, the plan is a diagram.
5. They separate the platform decision from the data remediation. Access-control cleanup on the content the AI will read is its own scope, its own owner, and its own schedule. Burying it inside a platform selection is how a six-week decision becomes a year.

Frequently asked questions

Which AI platform best supports hybrid cloud deployments in highly regulated environments?

For a Microsoft-standardized regulated enterprise, Microsoft, because Azure Government is documented as assessed and authorized at FedRAMP High (Microsoft’s Compare Azure Government and global Azure article, last updated 2025-08-21) and Foundry Local on Azure Local runs inference on an Arc-enabled Kubernetes cluster you own, secured with Microsoft Entra ID, so both halves sit in one identity and governance plane. For a genuinely disconnected or classified enclave, Google Distributed Cloud air-gapped, which Google describes as not requiring connectivity to Google Cloud and on which it says Gemini is available. For an AWS-native estate, Amazon Bedrock in AWS GovCloud, which AWS’s certification status page, last updated September 18, 2026, lists at FedRAMP Class D (formerly High) and DoD IL4 and IL5 for a named model list, with the on-premises half designed separately.

Can you run Microsoft AI models on your own hardware?

Yes, with a maturity caveat you should not skip. Microsoft’s Foundry Local on Azure Local documentation describes deploying and running AI models on an Arc-enabled Kubernetes cluster, keeping data processing on-premises, serving generative and predictive inference on CPU or GPU nodes, and operating in disconnected environments where internet connectivity is not available. The same article states that Foundry Local is available in preview, that features and capabilities can change before general availability, and that deployment is currently available by request during the preview. Its published supported-region list for the Arc extension names commercial Azure regions only, so confirm the path for your specific cloud with Microsoft rather than inferring it.

Is Amazon Bedrock available on-premises?

Not according to the AWS documentation reviewed for this page. AWS describes Bedrock as serverless, with availability defined by Region, including the AWS GovCloud (US-West) and (US-East) Regions for regulated workloads. We found no AWS documentation describing Bedrock inference running on customer-owned hardware. That is an absence of documentation rather than a prohibition, so if an on-premises requirement is firm and AWS is your incumbent, put the question to your AWS account team in writing and design the on-premises tier around the answer you get, not around the assumption.

Does Gemini run in an air-gapped environment?

Google says yes, and the document that says so is the Google Distributed Cloud air-gapped overview in the Google Cloud documentation, last updated 2026-08-11 UTC. That overview states that the product lets you host, control, and manage infrastructure and services directly on your premises and does not require connectivity to Google Cloud, and Google’s announcement of April 9, 2025 states that the air-gapped product is authorized for US Government Secret and Top Secret missions and that Gemini is available on it. Because the public reference documentation is lighter on model-level detail than the equivalent Microsoft and AWS material, get the current model catalog and reference architecture for your specific enclave from Google directly.

Which generative AI services are authorized at FedRAMP High and DoD IL4 or IL5?

The clearest published statements differ by vendor. AWS announced on June 25, 2026 that OpenAI GPT, OpenAI GPT OSS, and NVIDIA Nemotron models have FedRAMP High and DoD IL-4/5 approval in AWS GovCloud (US), and its certification status page, last updated September 18, 2026, lists the Titan, Claude, and Llama models carrying that authorization. Microsoft documents Azure Government as assessed and authorized at FedRAMP High with separate Azure OpenAI government endpoints (article last updated 2025-08-21). Google Cloud’s guidance conditions FedRAMP High and DoD IL4 and IL5 support on using models with US multi-regional endpoints inside a correctly configured Assured Workloads folder (guidance last updated 2026-08-14 UTC). Authorization is granted per service and often per model, so verify at the model level for the exact model you intend to deploy.

What usually goes wrong in a hybrid AI deployment in a regulated enterprise?

Three things, in roughly this order. The constraint that drove the architecture turns out to be a preference rather than a written control, which means an on-premises tier is being operated for no compliance benefit. The permissions on the content the AI reads were already wrong, and the AI surfaces that at speed and with citations, which turns into a remediation project nobody scoped. And the seam between the two halves gets governed by nobody, because the cloud half answers to one owner and the on-premises half to another, and connectors, identity, and logging fall between them. All three are cheaper to find in an assessment than in a security review.

You are one unwritten control away from the wrong architecture

Four answers narrow this decision to one platform for almost every organization: where your data and identities live today, which of the three hybrid shapes your requirement actually is, whether the constraint driving it is a written control or a preference, and whether you have a team that will operate on-premises inference infrastructure. Most conversations resolve on the third question.

What you should get out of that conversation is a platform recommendation with the vendor documentation and revision dates attached, a boundary map naming where inference, retrieval, and logs land for each workload class, and a written statement of what has to be true for the recommendation to change. If you are building an internal case rather than buying this quarter, the last of those three is the part that survives the meeting.

Contact a senior architect

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