DeepSeek: A complete guide for French companies

DeepSeek disrupted the global artificial intelligence ecosystem in just a few weeks in early 2025. An open language model, developed in China, capable of competing with the best American models at a fraction of the development cost. For French companies, the question is no longer whether DeepSeek is impressive. The question is: how can DeepSeek be used practically, securely, and in compliance with GDPR in a professional context?

This guide answers that question directly.

• DeepSeek R1 outperforms GPT-4o on several mathematical reasoning and coding benchmarks (Hugging Face, 2025)
• DeepSeek's entry-level model costs up to 30 times less than flagship models from OpenAI or Anthropic, a gap which narrows, however, when compared to their most economical offerings (price comparisons, September 2026).
• Data sovereignty has become a key criterion in the choice of AI models in business.

What is DeepSeek?

A Large Language Model ( LLM ) is an artificial intelligence system based on machine learning, trained on massive volumes of text to understand and generate natural language. It can write, summarize, translate, code, analyze, and answer complex questions with a level of consistency and relevance that approaches human reasoning on many tasks.

Generative AI refers to the family of technologies capable of producing original content through the generation of text, code, images, or audio. LLM (Learning-Learning Models) are the most widely deployed component in businesses today, particularly through interfaces such as ChatGPT, Copilot, or Mistral.

DeepSeek is a Learning Management System (LMS) developed by the Chinese company DeepSeek AI, founded in 2023. Its distinguishing features are twofold. First, its models are released under an open-source license: their weights, i.e., the trained model, are freely downloadable, modifiable, and deployable by any organization (the training data itself is not published). Second, its performance on reasoning, coding, and data analysis tasks is comparable to the best proprietary American models, at a significantly lower training and inference cost.

It is this combination of performance and accessibility that caused a shockwave in the global generative AI ecosystem in January 2025. Since April 2026, DeepSeek has been offering a new family of models, V4, of which the V4-Pro version has been available since August 2026 in the application, on the web and via the API.

How to use DeepSeek in a business setting?

Understanding how to use DeepSeek begins with choosing the usage mode. There are three, depending on the desired level of control and sovereignty.

Mode 1: DeepSeek web interface

The simplest to implement. Accessible at chat.deepseek.com, it allows you to use the model directly from a browser, without installation.

Useful for testing and occasional individual use. Not recommended for sensitive business data: processing business data on Chinese servers presents significant GDPR risks.

Mode 2: The DeepSeek API

DeepSeek offers an API compatible with the OpenAI format, facilitating its integration into existing applications and workflows. The cost remains competitive compared to proprietary alternatives, even after the price increase in August 2026. As with the web interface, data is routed through the DeepSeek infrastructure.

Mode 3: Local deployment

This is the recommended method for French companies subject to the GDPR and concerned about their digital sovereignty and the protection of their data.

Since DeepSeek model weights are published, they can be downloaded and deployed on the organization's internal infrastructure or on a sovereign European hosting service. The data never leaves the company's perimeter.

Concrete use cases in business

Four areas concentrate the bulk of DeepSeek's added value in a professional context.

Content generation and writing : drafting reports, summaries, presentations, and internal communications. DeepSeek produces structured and consistent texts in French with a quality comparable to the best models on the market.

Code development and review : code generation, bug detection, automatic documentation. DeepSeek's reasoning models excel particularly at logic and programming tasks.

Data and document analysis : summarizing long documents, extracting structured information, comparative analysis. Particularly useful for legal, finance, and HR teams dealing with large volumes of documents.

Internal support and FAQ : building conversational assistants based on the organization's knowledge base, deployable on internal platforms or customer service interfaces.

DeepSeek and GDPR: what French companies need to know

This is the number one point of vigilance for any French organization that plans to integrate DeepSeek into its processes.

Where is the data hosted?

When you use the DeepSeek web interface or API, your data is processed on servers located in China. China is not recognized by the European Commission as providing an adequate level of protection for personal data as defined by the General Data Protection Regulation (GDPR). Transferring personal or confidential data through these channels exposes the organization to a real legal risk.

The concrete risks

Three levels of risk associated with using DeepSeek via the cloud need to be assessed:

a violation of the GDPR in the event of the transfer of personal data to an inadequate third country,

the breach of confidentiality of company data if sensitive information is submitted to the model,

the risk of data being requisitioned by Chinese authorities under local legislation.

The solution: local deployment or sovereign hosting

Deploying DeepSeek locally on internal infrastructure or on sovereign European hosting significantly reduces these risks. Data remains within the organization's control, processed by a model whose hosting it fully manages.

DeepSeek vs ChatGPT vs Mistral: what are the differences for businesses?

Mistral, developed in France, has the advantage of a European base and a more mature French-speaking community. DeepSeek stands out for its performance/cost ratio and its ability to deploy locally without the constraints of a very permissive license. ChatGPT remains the benchmark in terms of its ecosystem and available integrations, at the cost of dependence on an American vendor and a higher price.

How to deploy DeepSeek securely in your organization?

Here's how to use DeepSeek securely, in five steps.

1. Define the scope of use

Identify priority use cases and consider the data categories involved in each scenario. An internal drafting assistant without sensitive data does not impose the same constraints as a customer contract analysis tool.

2. Choose the appropriate deployment method

For any use involving business data, avoid DeepSeek's web interface and API, as their data is processed in China. Opt for local deployment, or for sovereign hosting certified HDS or SecNumCloud if the data is sensitive.

3. Select the model version

DeepSeek offers several variants depending on the needs: the V4 (2026) family and DeepSeek-V3 for general tasks, DeepSeek-R1 for reasoning and code, and lighter distilled versions for deployment on limited infrastructure.

4. Implement a usage policy

Define the usage rules for your employees: what data can be submitted to the model, what types of output must be reviewed by a human, how intellectual property issues regarding generated content are handled, and how results are tracked and archived. Since February 2026, the AI Act also requires organizations using AI systems to train their employees on these tools (section 4).

5. Audit and monitor continuously

A production LLM requires regular security monitoring: output quality, potential deviations, model updates, and regulatory changes. Integrate this governance into your IT risk management framework.

Smile, your partner for integrating generative AI into business

At Smile, we support French organizations in the evaluation, deployment, and governance of open-source LLM. Our expertise covers the entire chain: selecting the model best suited to their needs, deploying on sovereign infrastructure, integrating into business workflows, and training teams.

We have been closely monitoring the evolution of DeepSeek and open-source models since their emergence. Our approach is pragmatic: we help implement the model best suited to each context, whether it be DeepSeek, Mistral, Llama, or a combination of several models depending on the use case.

Data sovereignty and GDPR compliance are not constraints we circumvent. They are requirements we integrate from the very design stage of every AI project.

Do you want to understand how to use DeepSeek securely and compliantly in your organization? Discover our complete analysis and feedback .

Frequently Asked Questions about DeepSeek in Business

Is DeepSeek truly open source?

Yes, in the sense that its models are published under the MIT license: their weights are freely accessible, modifiable, and deployable without licensing fees. The training data, however, is not published, which is why it's also referred to as an "open-weight" model. This openness is one of the reasons for its rapid adoption in the global AI community. In particular, it allows for local deployment, which resolves data sovereignty issues for European companies.

Can DeepSeek be used for free in a business setting?

Access to the web interface is free for limited use. The API is chargeable based on the volume of tokens used, with very competitive rates compared to alternatives, despite the price increase in August 2026. Local deployment on your infrastructure is free in terms of licensing, but requires technical and hardware resources appropriate to the size of the chosen model.

Does DeepSeek speak French well?

Yes, DeepSeek handles French well for most professional use cases. Its performance in French is slightly lower than that of Mistral, which was specifically optimized for European languages, but more than sufficient for writing, analysis, and internal support tasks.

What infrastructure is needed to deploy DeepSeek locally?

It depends on the model version. Lightweight distilled versions (7B to 14B parameters) can run on a server with one or two consumer-grade GPUs. Full versions, which have several hundred billion parameters, require significant GPU infrastructure. An expert integrator can size the infrastructure to suit your use cases and budget.