Sovereign AI infrastructure,
deployed in hours.
Xinity runs validated open-weight models on your own hardware and gives you the control layer around them: role-based access, single sign-on, and a per-request audit trail. The API is OpenAI-compatible, so your existing applications do not need rewriting.
What happens when you lose control of your AI?
Most enterprises adopted AI through public APIs before they understood what that meant for sovereignty, compliance, and unit economics. Here is what it actually costs.
US server lock in
Your data lives on US servers, subject to the CLOUD Act and beyond EU jurisdiction.
GDPR & AI Act exposure
Fines reach up to 4% of global annual revenue under GDPR and 7% under the AI Act. Public-API LLMs put compliance largely outside your control.
No audit trail
You can't prove to regulators what data was sent where, or which model produced which output.
Unpredictable costs
Per-token pricing becomes unsustainable at scale and breaks every annual budget you submit.
The engine answers your engineers. The platform answers your auditors.
Every AI deployment in a regulated organization has to pass two tests. Can we run models on our own hardware? And are we allowed to put this in production? Xinity is built as two layers because those are two different questions, asked by two different people.
Xinity Engine
The open source core, free forever. Replace public LLM APIs with a sovereign endpoint by pointing your applications at a new base URL with a new API key.
- OpenAI-compatible API. Drop-in replacement.
- Multi-model routing across local GPUs.
- Bring your own model, open source or proprietary.
- Per-request observability and audit log.
- Self-host on a laptop, a server, or a cluster.
Xinity Platform
The enterprise layer: everything your CISO, works council and auditor need before they can say yes. A liable vendor, governed access, and evidence on demand.
- On-prem deployment. Your hardware. Your building.
- Full audit trail on every inference request.
- Role-based access control and SSO.
- Hardware sizing, install, and lifecycle support.
From public API to sovereign AI, in hours.
Plan
We map your use cases, data residency requirements, and hardware footprint in a single 30-minute session. You leave with a concrete deployment plan.
Deploy
Engine and Platform ship together. We rack hardware in your facility, or guide your team. Point your applications at the OpenAI-compatible endpoint and run your first model. Typically hours, not months.
Operate
Route across models, watch every request, prove compliance to auditors, and scale capacity as adoption grows, without touching the public internet.
We are not here to experiment. We are here to deliver.
Six reasons European enterprises choose Xinity over public APIs and US-headquartered platforms.
On your hardware, in your building
No cloud, no hyperscaler, no data leaving your premises. Architectural sovereignty by default.
OpenAI-compatible, drop-in
Point your existing applications at a new base URL with a new API key. Same SDK, same prompts, sovereign infrastructure.
Deployed in hours, not months
Engine, Platform, and hardware ship together. First model running in your environment in hours, not months.
Audit trail by default
Every request, every model, every cost. Logged and mapped to EU AI Act articles your auditor will ask about.
100% European, aligned by architecture
Outside the reach of the US CLOUD Act by architecture. Aligned with GDPR, the EU AI Act, and NIS2 controls.
Predictable, capacity-based pricing
Pay for the GPUs you run, not for every token. Annual budgets you can actually defend.
All deployments are supporting compliance for
Trusted by industry leaders
We gave developers the keys.
Xinity Engine is live on GitHub, open source and free forever. Connect it to your existing AI setup and keep every request on your own infrastructure. When you are ready to go enterprise, the Platform layer is there.
Your window to get compliant is closing.
The Article 50 transparency duties have applied since 2 August 2026, and the Article 4 AI literacy duty since February 2025. The high-risk system requirements take full effect on 2 December 2027. Deploy now and do it at your own pace, not under a regulator's deadline.
Frequently asked questions
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.
Read more about sovereign AISovereign 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.
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.
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 and the API key, 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.
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.
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 open source 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.
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.
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.
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 and the key.
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. Some obligations already apply: the Article 50 transparency duties have applied since 2 August 2026, and the Article 4 AI literacy duty since February 2025. The high-risk obligations for stand-alone systems follow on 2 December 2027, so 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.
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.
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.
It introduces obligations that scale with risk. Many business uses of LLMs already fall under the Article 50 transparency duties, which have applied since 2 August 2026, and every organisation using AI has been subject to the Article 4 AI literacy duty since February 2025. 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; those obligations apply to stand-alone systems from 2 December 2027. Running LLMs on infrastructure you control makes the logging, audit trail, and oversight requirements far easier to meet.
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.
Xinity Engine is open source 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.
Open-weight models such as Qwen, Mistral, Llama, and Gemma, 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.
The engineering cost of switching is low because the OpenAI-compatible API means existing integrations move by changing an endpoint and a key, 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.
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.
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.
You do. Always. Xinity is software you run; you own the models, the data, the logs, and the infrastructure.
The Engine is 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.
Still have a question? Contact us.
