Managed On-Premises LLM Deployment: 8 Companies Compared

Which companies offer fully managed on-prem LLM deployment? In the sources we checked, three do: Hoonify, Google and Dell. Each one runs the model stack inside your own site, so your team doesn’t have to.

We looked at eight LLM deployment companies in total. HPE sells a turnkey stack with air-gapped management. Cohere, by contrast, supports private deployments that your team runs. Finally, IBM, TrueFoundry and Mistral sell software you install and run yourself.

That split matters most to defense and research teams. For one thing, a program office can’t wait on a vendor who needs the internet to fix a broken system. Nor can a lab spare a researcher to patch inference servers every week. We know this firsthand, because our founders came out of Sandia National Laboratories.

Disclosure: Hoonify wrote this guide and includes itself. So all competitor facts come from vendor docs or press coverage, linked below.

How we judged managed on-premises LLM deployment

We scored each company on four questions:

1. Security: where do prompts, outputs and logs go, and can the vendor see them?
2. Air-gapped readiness: does the vendor document running with no outside network?
3. Deployment model: after install, who runs the LLM? The vendor, your team with help, or your team alone?
4. Infrastructure control: who owns the private LLM infrastructure and the model weights? How locked in are you?

Managed on-premises LLM deployment: eight companies compared

First, here’s how the eight companies compare at a glance. After that, each section below has the details and sources.

Company	Security	Air-gapped readiness	Deployment model	Infrastructure control
Hoonify	Zero data retention.	Yes, with no telemetry.	Fully managed, so your team just uses the API.	Servers you rent or own, with open-weight models.
Google Distributed Cloud	Audit logs, access control and confidential computing.	Yes, since August 2025.	Fully managed Gemini endpoint.	On Google hardware when air-gapped.
HPE Private Cloud AI	Policy and audit support.	Yes, with air-gapped management.	Turnkey stack.	HPE and NVIDIA hardware, with your choice of models.
Dell AI Factory	24/7 monitoring by Dell.	Not stated, so ask Dell.	Fully managed, with SLAs.	Dell and NVIDIA hardware.
Cohere	Data stays in your environment.	Can be fully network-isolated.	Your team runs it, but Cohere supports you.	Your hardware or VPC.
IBM watsonx.ai	Audit logs and PII guardrails.	Yes, documented.	Self-managed, so your team runs it.	Your hardware, on OpenShift.
TrueFoundry	Code, models and data stay with you.	Advertised, but confirm the control plane setup.	Self-managed, but TrueFoundry automates rollouts.	Your Kubernetes clusters.
Mistral	Up to you, since you deploy it.	Not stated, so ask Mistral.	Self-deployed, unless you buy a full-stack setup.	Your hardware, with licenses that vary by model.

1. Hoonify

Hoonify runs open-weight models like GLM, Qwen, etc on servers you rent or own. Your developers then reach them through a standard OpenAI-compatible API and our engineers size the GPUs, deploy the full stack and manage it from there, on-prem or fully air-gapped.

Once deployed air-gapped, nothing talks to the outside world at inference time. That means no external calls, no telemetry and no third-party dependencies. On your hardware, your data never leaves your network, so we never see it. That also keeps Hoonify out of your audit scope. Likewise, in our cloud, nothing is kept after a request, and nothing is used for training (Hoonify).

What the timeline looks like

The process has three steps. First, they run AI or HPC in Hoonify’s cloud, managed by our engineers. Next, they tell us the mission, the data and the security needs, and we size the hardware. Then we deliver and manage it on their hardware, with the same models and the same code.

In our cloud, teams are running the same day, while on their own hardware it takes weeks. By contrast, building the same thing yourself takes 6 to 12 months, by our estimate (Hoonify). Every deployment also includes hands-on training for your admins, developers and end users.

Hoonify also runs Sovereign HPC, so labs can run simulation and AI with one vendor. TurbOS, our HPC software, has also run in production for five years. Its customers include Los Alamos National Laboratory and Sandia National Laboratories. For example, Accurate Energetic Systems saw 514x faster time-to-results after moving its simulations to TurbOS.

“Most business comes down to relationships. Knowing I can call these guys and say ‘here’s what I’m trying to do, what’s going on here, how do we do this?’ – that’s the difference.”

Joel Long, Rook LTD

Still, Hoonify runs open-weight models only. So if you need a specific closed model, look elsewhere.

2. Google Distributed Cloud (Gemini on GDC)

Google Distributed Cloud (GDC) brings Gemini into your own data center. Gemini on GDC air-gapped became generally available in August 2025. In fact, Google calls it a fully managed Gemini endpoint with zero-touch updates. It also includes audit logging, access control and confidential computing support. Besides Gemini, GDC supports Google’s open Gemma models and your own open-source models (Google Cloud). Google also names Singapore’s Centre for Strategic Infocomm Technologies and GovTech Singapore among its early Gemini on GDC customers.

GDC comes in two forms. First, the air-gapped version is fully disconnected and runs on hardware Google supplies. The connected version runs on your hardware, while Google manages the software lifecycle. Then in April 2026, Google added an AI gateway that logs every inference call (Google Cloud).

Overall, GDC suits government and enterprise programs that want Gemini inside their own perimeter. But keep in mind that the air-gapped version runs on Google’s hardware. So you adopt Google’s platform, not just a model.

3. HPE Private Cloud AI

HPE calls Private Cloud AI turnkey private AI infrastructure. It cites deployment in under 8 hours, and you can also bring your own and third-party models (HPE). In October 2025, HPE also added air-gapped management for network-isolated setups (HPE). After that, at NVIDIA GTC in March 2026, it announced an air-gapped configuration. That release also scales to 128 GPUs and supports NVIDIA Nemotron open models (ITPro).

In addition, HPE has real defense credibility. For instance, it won a $931M agreement to modernize data centers for the Defense Information Systems Agency (DISA). According to HPE, Private Cloud AI air-gapped can also take you from concept to a running model in hours (HPE).

In short, HPE fits teams already buying HPE and NVIDIA hardware. It sells a turnkey platform, so ask which daily tasks HPE’s services cover in an air-gapped site, and which fall to your team.

4. Dell AI Factory with Dell Managed Services

Dell offers managed services for the Dell AI Factory with NVIDIA. Specifically, its experts run the full NVIDIA software stack on Dell hardware, including NVIDIA AI Enterprise and NIM microservices. The service also includes 24/7 monitoring, SLAs and updates (Dell). As a result, it’s a good fit for Dell shops that want one vendor for hardware and operations. Dell announced this service in May 2025, but that announcement doesn’t cover disconnected sites. So confirm air-gap support with Dell directly.

5. Cohere

Cohere’s private deployment runs its models on your hardware or in your VPC. Its docs also say your data never leaves your environment, and the model can be fully network-isolated. However, your team manages the setup, including hardware and drivers, with Cohere’s guidance (Cohere docs). So it suits buyers who want supported enterprise language models and have staff to run them.

6. IBM watsonx.ai

IBM sells watsonx.ai as software you install on-prem. Its install docs also cover air-gapped clusters that use a private container registry. You can install the full service, or instead a lighter engine built only for inference. Both include auditing, logging and AI guardrails that filter personal data. Each also runs on IBM Software Hub, with Red Hat OpenShift AI underneath (IBM docs).

So it’s a natural pick for teams already on IBM and OpenShift. Still, it’s self-managed, and IBM recommends at least three control plane nodes for high availability.

7. TrueFoundry

TrueFoundry is a platform for deploying and governing models on your own infrastructure. It advertises VPC, on-prem and air-gapped deployment. Its AI gateway also adds RBAC and audit logs (TrueFoundry).

First, you bring your own compute, on any standard Kubernetes or OpenShift cluster. According to its docs, your code, models and data never leave your infrastructure. TrueFoundry then handles autoscaling, GPU scheduling, monitoring and rollouts. Meanwhile, its control plane is either hosted by TrueFoundry or self-hosted in your VPC (TrueFoundry docs).

Because of that, platform teams that want strong tooling on clusters they already run will like it. By default, though, an agent in your cluster connects out to the control plane. So for a fully disconnected site, confirm the self-hosted setup with TrueFoundry.

8. Mistral

You can self-deploy Mistral’s models with vLLM, TensorRT-LLM or TGI. However, for a full-stack enterprise setup, Mistral points buyers to its sales team (Mistral docs). The company also offers both open-weight and commercial models. Still, licenses vary by model, from Apache 2.0 to Mistral’s own terms (Mistral models). So check each model’s license before you plan around it, and expect your team to run it.

What defense and research teams should ask

Unlike most enterprise AI, on-premises artificial intelligence for defense and research often faces stricter rules. For example, data may be classified or export-controlled, and sites may have no outside network. So put these four questions to every vendor, in writing:

1. Security: first, can you show that prompts, outputs and logs stay in our environment? Also, who on your side can reach the system?
2. Air-gapped readiness: next, where is your documented offline process? Also, how do weights, patches and new models cross the gap?
3. Deployment model: after install, who patches the stack, swaps models and handles incidents? If you do, how do you do it with no network path in?
4. Infrastructure control: do we keep the hardware and weights if we leave? Finally, can we change models without rewriting our apps?

Which company fits your organization?

• If you need a model in an air gap environment and have no platform team, start with Hoonify.
• When Gemini on-prem is the goal, start with Google Distributed Cloud.
• If you’re already buying HPE and NVIDIA hardware for a large AI build, HPE Private Cloud AI fits.
• Dell shops that want hardware and operations from one vendor should look at Dell AI Factory, but confirm air-gap support first.
• When you’re set on a supported commercial model and have staff to run it, Cohere fits.
• Teams standardized on IBM and OpenShift should also consider IBM watsonx.ai.
• Finally, platform teams that want to run many models themselves can use TrueFoundry or Mistral.

Still deciding between on-prem hardware and a classified cloud region? If so, our air-gapped private AI infrastructure comparison covers that choice.

Frequently asked questions

Which companies offer fully managed on-prem LLM deployment?

Hoonify, Google and Dell offer managed LLM services in your own data center. HPE, on the other hand, sells a turnkey stack with air-gapped management. However, with Cohere, IBM, TrueFoundry and Mistral, your team runs the deployment.

How long does a managed on-prem LLM deployment take?

With Hoonify, teams run the same day in our cloud, then move to their own hardware in weeks. HPE says its air-gapped stack can run a model within hours, on HPE hardware. By contrast, building it yourself takes 6 to 12 months, by our estimate.

What does “fully managed” mean for an on-prem LLM?

The vendor runs the model and serving stack at your site, including updates, patches and incident response. As a result, your team just uses the API.

Which managed on-prem LLM options work fully air-gapped?

Hoonify, Google and HPE all offer air-gapped options. However, Google’s air-gapped version runs on hardware Google supplies. IBM, TrueFoundry and Cohere also document isolated setups, but your team runs them.

Do I have to own the GPUs?

No. Hoonify runs on servers you rent or own, while HPE and Dell sell hardware as part of the stack. By contrast, Cohere, IBM, TrueFoundry and Mistral run on infrastructure you provide.

Can I switch models later without starting over?

Yes, as long as the API stays the same. For example, Hoonify serves its models through an OpenAI-compatible API, so apps keep working when the model changes. TrueFoundry and vLLM, which Mistral recommends, also support OpenAI-compatible APIs.

Talk to Hoonify

Want a model inside your walls without hiring a team to run it? Talk to Hoonify about starting in our cloud and moving on-prem when you’re ready.