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. The DeepSeek family spans R1 (January 2025, MIT licence) through V4-Flash (April 2026, 284B/13B active MoE, MIT) to V4-Pro (1.6T/49B active, 1M context, MIT) and reaches GPT-5.5 / Claude Opus 4.7 territory in benchmarks - V4-Pro approaches frontier closed-source performance under MIT licence, at significantly lower operating costs and with full transparency over the model code.

Update May 2026: DeepSeek V4-Pro (1.6T total / 49B active MoE) and V4-Flash (284B total / 13B active) were released as preview on April 24, 2026 under MIT license. For new deployments, V4-Flash is the typical workhorse, while V4-Pro fits hyperscaler-class setups or runs via API/hosted for smaller deployments. The R1 architecture remains mature and production-ready but is being replaced by V4-Flash for new deployments. Details in the Self-hosted Open-Source AI 2026 article.

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.

Criterion	Azure	GCP	Self-Hosted
Setup	Managed (AI Foundry)	Managed (Vertex AI)	Manual (bare-metal/VM)
EU Data Residency	Germany West Central	europe-west3	Own data center
GPU Availability	A100, H100 (pay-as-you-go)	A100, H100, TPU	A100, H100 (purchase/lease)
Vendor Lock-in Risk	High (Azure-specific)	Medium (Kubernetes-portable)	None
Operational Effort	Low	Low-Medium	High
Best For	Microsoft-centric orgs	Kubernetes-native orgs	Maximum data sovereignty

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 a single frontier model - whether DeepSeek V4-Pro, GPT-5.5 or Claude Opus 4.7 - carries a strategic risk. A model-agnostic decision-routing layer (see When which model?) absorbs the next generational shift as a configuration change rather than a re-engineering effort.

A model-agnostic architecture decouples the usage layer from the model layer. Employees use a unified chat interface. Behind it, an orchestration layer routes per micro-decision rather than committing to a champion model: Mistral Small 3.2 (24B, Apache 2.0, RTX 4090) as the volume workhorse for 50-70% of decisions (classification, extraction); DeepSeek V4-Flash / V4-Pro (MIT) or gpt-oss-120b (Apache 2.0) for on-prem reasoning; Claude Opus 4.7 / GPT-5.5 (cloud) for frontier reasoning and agentic workflows; Gemini 3.1 Pro (cloud) for multimodal applications; Qwen 3 Coder / DeepSeek Coder V4 for on-prem code generation. Llama and Mistral round out the stack 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.