Question 1

What is sovereign AI?

Accepted Answer

Sovereign AI means generative AI infrastructure that runs entirely under your legal and physical control. On your hardware. In your jurisdiction. With no data leaving your premises. It is the architectural answer to GDPR, the EU AI Act, and the US CLOUD Act.

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Question 2

What is sovereign AI infrastructure?

Accepted Answer

Sovereign AI infrastructure is the full hardware and software stack that lets you run AI workloads without depending on a third party for access, availability, or data custody. With Xinity, that means your own GPUs, an inference engine you control, and an enterprise layer for compliance, all inside your own network. Nothing critical sits behind someone else's login or terms of service.

Question 3

What is an on-premise LLM inference engine?

Accepted Answer

An inference engine is the software that loads a large language model and serves its responses to your applications. On-premise means it runs on hardware you own and control, rather than in a vendor's cloud. Xinity Engine is exactly this: it loads open or proprietary models on your local GPUs and exposes them through a standard API, so prompts and outputs never leave your environment.

Question 4

What does "OpenAI-compatible" mean for a self-hosted LLM?

Accepted Answer

It means the engine speaks the same API language as OpenAI. Applications already built against the OpenAI SDK can point at your self-hosted endpoint by changing the base URL, with no rewrites to the code or prompts. Xinity Engine implements this OpenAI-compatible interface, so migrating from a public API to sovereign infrastructure is a configuration change, not a development project.

Question 5

What is the difference between cloud AI and on-premise AI?

Accepted Answer

Cloud AI sends your prompts and data to a provider's servers, often outside the EU, where you rely on their security, availability, and terms. On-premise AI runs the same models on hardware in your own facility, so data never leaves and you keep full custody and audit visibility. The trade-off used to be setup effort; Xinity removes most of it by shipping engine, platform, and hardware together.

Question 6

What are the best on-premise alternatives to OpenAI for regulated industries?

Accepted Answer

Regulated organisations typically look for a self-hosted engine, a compliance and audit layer, and a clear migration path off public APIs. Xinity covers all three in one stack: an Apache 2.0 engine with an OpenAI-compatible API, an enterprise platform with audit trails mapped to EU AI Act articles, and on-prem deployment on your own hardware. It is purpose-built for finance, healthcare, public sector, and media, where data residency and auditability are not optional.

Question 7

How can companies run LLMs without sending data to the US?

Accepted Answer

By running the models on infrastructure located in their own jurisdiction rather than calling a US-hosted API. Xinity deploys open or proprietary models on hardware inside your building, so every prompt and response stays on your network and outside the reach of the US CLOUD Act. There is no transatlantic transfer to document because there is no transfer at all.

Question 8

What is the most cost-effective way to self-host AI inference?

Accepted Answer

The biggest cost lever is moving off per-token pricing to capacity-based pricing once your volume is predictable. Self-hosting on right-sized GPUs lets you pay for the hardware you run instead of every request. One Xinity customer reduced per-request cost from €2.00 to €0.06, a 97% reduction, after moving inference on-prem. The break-even point depends on volume, which we size with you before any commitment.

Question 9

How does Xinity compare to using the OpenAI API?

Accepted Answer

The OpenAI API is fast to start with but sends your data to US-hosted servers and charges per token. Xinity gives you the same OpenAI-compatible interface while keeping data on your own hardware, adding a full audit trail, and replacing per-token billing with capacity-based pricing. You trade a small amount of initial setup for sovereignty, compliance, and predictable cost. Existing OpenAI integrations switch by changing the endpoint.

Question 10

Is Xinity compliant with the EU AI Act?

Accepted Answer

Xinity is built to support EU AI Act compliance. Every request is logged with a full audit trail mapped to the relevant articles, which is exactly the evidence regulators and auditors ask for around high-risk systems. The high-risk obligations for stand-alone systems now apply from 2 December 2027, but Xinity lets you put the technical controls in place today rather than under deadline pressure. Xinity provides the infrastructure; classification of your specific use case remains your responsibility.

Question 11

How does on-premise AI help with GDPR compliance?

Accepted Answer

GDPR is far easier to satisfy when personal data never leaves your control. On-premise AI keeps prompts and outputs inside your own network, removing the transatlantic data transfer that public APIs create and that is hard to legitimise. With Xinity, every request is logged, access is role-based, and there is no third-party processor handling your data, which simplifies your records of processing and your DPIA.

Question 12

Can EU companies use AI without violating data residency rules?

Accepted Answer

Yes, provided the data and the processing stay within the required jurisdiction. The reliable way to guarantee that is to run the models on infrastructure you physically control inside the EU. Xinity deploys on your own hardware in your own location, so data residency is satisfied by design rather than by contractual promise from a cloud provider.

Question 13

What does the EU AI Act mean for companies using LLMs?

Accepted Answer

It introduces obligations that scale with risk. Many business uses of LLMs fall into limited-risk transparency duties, while uses in areas like hiring, credit, or critical infrastructure can be classified as high-risk, with requirements for risk management, data governance, human oversight, and record-keeping. High-risk obligations for stand-alone systems apply from 2 December 2027. Running LLMs on infrastructure you control makes the logging, audit trail, and oversight requirements far easier to meet.

Question 14

What hardware do I need to run Xinity?

Accepted Answer

It depends on the models and the throughput you need. The Engine alone runs on a single workstation GPU for development and smaller workloads. Production deployments use one or more server GPUs, and we size the exact configuration with you during planning. Xinity can also ship validated hardware, such as the ASUS Ascent GX10, so you do not have to spec it yourself.

Question 15

Is Xinity open source?

Accepted Answer

Xinity Engine is open source under the Apache 2.0 licence and free forever, available on GitHub. The Platform layer that adds enterprise deployment, hardware provisioning, compliance tooling, and support is the commercial product. You can run sovereign inference on the open-source engine today and add the Platform when you need the enterprise capabilities.

Question 16

What models can run on Xinity?

Accepted Answer

Open-weight models such as Llama, Mistral, Qwen, DeepSeek, and Nemotron, your own fine-tunes, and proprietary models you have the rights to run. Xinity routes between multiple models automatically based on your policies, so you can match each request to the right model for cost, quality, or sensitivity without changing your applications.

Question 17

How much does it cost to switch from a cloud LLM API to Xinity?

Accepted Answer

The engineering cost of switching is low because the OpenAI-compatible API means existing integrations move by changing an endpoint, not by rewriting code. The infrastructure cost depends on the hardware you need, which we size against your volume during planning. Many organisations reach break-even quickly: one customer cut per-request cost by 97% after moving on-prem. We map the full picture with you before any commitment.

Question 18

How long does it take to deploy Xinity on-premise?

Accepted Answer

The Engine alone runs in minutes on existing hardware. A full Platform deployment with on-prem GPUs typically takes a matter of hours, not the months associated with traditional enterprise infrastructure projects, because the engine, platform, and hardware are designed to ship and install together.

Question 19

Is Xinity a cloud provider?

Accepted Answer

No. Xinity is software you deploy on your own hardware. We do not host your data; we bring AI to it. You keep ownership, control, and physical custody of every byte.

Question 20

Who owns the data and the models?

Accepted Answer

You do. Always. Xinity is software you run; you own the models, the data, the logs, and the infrastructure.

Question 21

What happens if Xinity shuts down?

Accepted Answer

The Engine is Apache 2.0 open source. You keep it forever. The Platform runs on your hardware with no external dependencies. Worst case, you keep operating exactly as you are today.

Sovereign AI infrastructure,
deployed in hours.

What happens when you lose control of your AI?

US server lock in

GDPR & AI Act exposure

No audit trail

Unpredictable costs

The engine answers your engineers. The platform answers your auditors.

Xinity Engine

Xinity Platform

From public API to sovereign AI, in hours.

Plan

Deploy

Operate

We are not here to experiment. We are here to deliver.

On your hardware, in your building

OpenAI-compatible, drop-in

Deployed in hours, not months

Audit trail by default

100% European, aligned by architecture

Predictable, capacity-based pricing

Trusted by industry leaders

We gave developers the keys.

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