What is on-premise AI?

On-premise AI runs on servers you own or control, so prompts, documents and answers stay inside your network. It usually means an open-weight model, the retrieval layer and the application all running in your own data centre or private cloud.

What is sovereign AI for a business?

It means your data, your models and the people who operate them stay under one legal jurisdiction. For most companies that is a mix of open-weight models on their own hardware and frontier models in a cloud region their regulator accepts.

Can GPT or Claude models run on-premise?

No. GPT and Claude models cannot be downloaded or run on your own servers. You can use GPT models in your own Azure tenant and Claude models in your own AWS or Google Cloud account, in a region you choose. Fully on-premise setups use open-weight models such as Llama, Mistral or Qwen.

Can you deploy AI in an air-gapped network?

Yes. Model files, updates and container images come in on approved media, and retrieval, logging and monitoring run inside the same network. Every update becomes a manual step, so we write and test that process with your team before launch.

Is on-premise AI more secure than a cloud API?

It removes the model vendor from the data path, which helps with residency and confidentiality. It does not secure the application for you. Agents still need scoped access, input checks, approval steps and audit logs, wherever the model runs.

How much does on-premise AI cost?

Projects start from $20K, scope-driven, and hardware is separate. The price depends on how long the build runs, its features and integrations, the size of the team, your timeline, and the level of polish, including air-gap needs. To see how pricing works at EdgeFirm, visit the pricing page.

On-Premise & Sovereign AI

On-premise AI and sovereign AI deployment built and handed over by EdgeFirm

We build AI applications and agents that run where your data has to stay. That can be your own servers, an air-gapped network, or a cloud region your regulator accepts. You choose the models, the logs stay with you, and you own the code.

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Engagement · Scope

EdgeFirm ships in 4 to 5 months. Fixed price.

What you get

Data stays in your network or region
Choice of open-weight or frontier models
Audit logs you hold
Hardware sized from real volumes

Timeline

4 to 5 months

Typical scope

1 to 5 integrations

Payback

Modeled before you sign

On-premise, private cloud or air-gappedIncluded

Open-weight or frontier modelsIncluded

You own the codeIncluded

Why it matters

Why Teams Are Moving AI Inside Their Own Walls

The same four concerns come up in almost every security review:

Data Leaving the Network

Every prompt sent to a model vendor carries your documents with it. For some data, that is not allowed at all.

Jurisdiction and Residency

Regulators and customers ask where data is processed and who can reach it. A shared global endpoint is hard to defend.

Dependence on One Vendor

Prices, models and terms change without notice. Teams want the option to switch models or run their own.

Agents With Real Access

Agents read records and call tools. Running them close to your systems, with your own logs, makes them easier to control and audit.

What we build

What On-Premise and Sovereign AI Deployment CoversFour parts of every build, decided before any code is written:

1 · Where the Model Runs

The Right Option for Each Workload

Open-weight models such as Llama, Mistral or Qwen on your own servers
GPT models in your Azure tenant, or Claude in your AWS or Google Cloud account, in a region you choose
Hybrid setups, with sensitive work on your hardware and the rest in your cloud
A model layer you can switch later without rebuilding the application

2 · Air-Gapped Deployment

No Connection to the Internet

Model files, updates and container images brought in on approved media
Retrieval and vector search running inside the same network
A documented update process your team can repeat
Monitoring that works without calling an outside service

3 · Private AI Applications and Agents

Built on Your Own Documents and Systems

Answers grounded in your documents, with the source cited
Agents with scoped access to your systems and an approval step for risky actions
Role-based access that follows your existing permissions
Every prompt, answer and tool call logged inside your network

4 · Sizing, Testing and Handover

Proof Before You Buy Hardware

Model and hardware sizing from your real volumes, before any purchase
An evaluation set built from your own questions, run on the chosen model
Runbooks, documentation and training for the team that will operate it
Complete code ownership at handover

What we measure

What EdgeFirm measuresset against your baseline in discovery

Every engagement is scoped to a fixed price and an outcome you can measure, not billable hours.

Delivery

4 to 5 months

from kickoff to production

Result

Pricing

From $20K

fixed after scoping, hardware separate

Result

Ownership

100%

of the code at handover

ResultSee case studies

The comparison

THE EDGEFIRM DIFFERENCE

Unlike AI platform vendors:

No per-seat or per-token licence on top
You are not tied to one model family
You own the code and the data

Unlike hardware resellers:

We build the application, not only the server
Sizing comes from your workload, not a catalogue
Accuracy is tested before launch

Unlike DIY with an open model:

Retrieval, access control and logging included
Agent controls designed in from week one
A team that hands over runbooks, not just a container

Common questions

Common Questions About On-Premise and Sovereign AI

: On-premise AI runs on servers you own or control, so prompts, documents and answers stay inside your network. It usually means an open-weight model, the retrieval layer and the application all running in your own data centre or private cloud.
: It means your data, your models and the people who operate them stay under one legal jurisdiction. For most companies that is a mix of open-weight models on their own hardware and frontier models in a cloud region their regulator accepts.
: No. GPT and Claude models cannot be downloaded or run on your own servers. You can use GPT models in your own Azure tenant and Claude models in your own AWS or Google Cloud account, in a region you choose. Fully on-premise setups use open-weight models such as Llama, Mistral or Qwen.
: Yes. Model files, updates and container images come in on approved media, and retrieval, logging and monitoring run inside the same network. Every update becomes a manual step, so we write and test that process with your team before launch.
: It removes the model vendor from the data path, which helps with residency and confidentiality. It does not secure the application for you. Agents still need scoped access, input checks, approval steps and audit logs, wherever the model runs.
: Projects start from $20K, scope-driven, and hardware is separate. The price depends on how long the build runs, its features and integrations, the size of the team, your timeline, and the level of polish, including air-gap needs. To see how pricing works at EdgeFirm, visit the pricing page.