Can I host DeepSeek GDPR-compliant in Europe?

Yes. DeepSeek models are open source (MIT licence) and can run entirely in your own infrastructure – on Azure (e.g. Germany West Central region), GCP (europe-west3) or on-premises. No data leaves the enterprise.

Do I need dedicated GPU hardware for DeepSeek?

Not necessarily. Azure and GCP offer GPU instances (A100, H100) as managed services. Self-hosting is an option for maximum control but not a requirement. The architecture decision depends on latency needs, costs and existing infrastructure.

What happens when a better model than DeepSeek emerges?

With a model-agnostic architecture: nothing. The orchestration layer routes between models. A new model is added, the old one remains available or is retired. No rebuild, no migration, no vendor lock-in.

DeepSeek is a Chinese model – is that a security risk?

Not with self-hosting. DeepSeek's open-source models run locally with no connection to DeepSeek servers. No data exfiltration, no API calls to China. The code is publicly auditable. The risk only exists when using the DeepSeek API – not when hosting the model yourself.

Hosting DeepSeek in Your Own Infrastructure

Why DeepSeek Matters for Enterprises

DeepSeek’s open-source models demonstrated that capable LLMs need not come exclusively from OpenAI or Google. DeepSeek R1 matches GPT-4-level performance across many benchmarks – at significantly lower operating costs and with full transparency over the model code.

For enterprises, this matters because it creates genuine choice: rather than committing to a single LLM vendor, organisations can run multiple models in parallel, compare them and deploy the best fit for each use case.

The real question is not whether DeepSeek is good enough. The question is how an enterprise operates LLMs in a way that ensures data sovereignty, compliance and future-proofing – regardless of which model is currently leading.

Three Hosting Options Compared

Azure: Enterprise Integration as Strength

Azure AI Foundry offers DeepSeek as a managed deployment. The advantage for organisations with existing Microsoft environments: integration with Azure Entra ID (formerly Azure AD), established network and security configurations, and region selection for EU data residency. GPU instances (A100, H100) are available as pay-as-you-go or provisioned throughput.

The drawback: vendor lock-in at the Azure level. Switching to GCP or self-hosted later requires rebuilding the deployment layer – unless the architecture is designed to be model- and platform-agnostic from the start.

GCP: Flexibility and Kubernetes-Native

Google Cloud Platform offers managed deployments for open-source models via Vertex AI. The strength lies in its Kubernetes-native architecture: organisations already using GKE (Google Kubernetes Engine) can run LLMs as container workloads alongside existing services. TPU options provide an alternative to NVIDIA GPUs.

Self-Hosted: Maximum Control

For enterprises with the strictest data protection requirements – in financial services or healthcare, for instance – self-hosting is the consistent choice. DeepSeek models run on dedicated servers or in a private data centre with zero cloud dependency. The trade-off: higher operational effort for hardware management, updates and scaling.

All three options are technically equivalent. There are no architectural compromises with self-hosting. The decision depends on the existing IT landscape, compliance requirements and internal operating model.

Why the Hosting Decision Is Not the Most Important One

Most articles about LLM hosting end at the hosting decision. But for enterprises, hosting is merely the foundation – the real questions come after.

How is it governed which model serves which use case? How are prompts and responses logged without compromising employee data? How is the works council (Betriebsrat) given transparency over AI usage? How is a model switch executed without changing the interface for 5,000 employees?

These are not hosting questions. These are architecture and governance questions. And this is precisely where an enterprise AI infrastructure differs from a hosted model.

Model-Agnostic Architecture as Strategy

The LLM landscape shifts faster than any enterprise procurement cycle. What is state-of-the-art today may be superseded by a new model in six months. Anyone who builds their entire infrastructure on DeepSeek – or on GPT-4, or on Claude – carries a strategic risk.

A model-agnostic architecture decouples the usage layer from the model layer. Employees use a unified chat interface. Behind it, an orchestration layer routes between models: DeepSeek for cost-effective text analysis, Claude for complex reasoning tasks, GPT-4o for multimodal applications, Llama or Mistral for specialised domains.

Model switches, model comparisons and A/B testing happen in the orchestration layer – transparent to users, auditable for IT, traceable for the works council (Betriebsrat).

DeepSeek as a Building Block, Not a Platform

DeepSeek is a capable model. But no model alone solves the enterprise problem. What organisations need is not a hosted LLM but an infrastructure in which LLMs operate as components – embedded in Governance by Design, integrated with existing systems, extensible with AI agents that process documents and orchestrate workflows.

The Decision Layer separates LLM analysis from business decisions. The model prepares, the human decides – with a complete audit trail.

At Gosign, we build this AI infrastructure: model-agnostic, GDPR-compliant, on Azure, GCP or self-hosted. DeepSeek is one of many building blocks. The architecture makes the difference.