DeepSeek: what it is and why it's being talked about

DeepSeek is a family of artificial intelligence models developed by the Chinese company of the same name, which has become known for two precise reasons: High performance at very low cost and a strong focus on reasoning (its "reasoning" models break the problem down into steps before giving the answer). In practice it is a direct competitor of ChatGPT, Claude and Gemini, with a positioning based on efficiency rather than brand prestige.

The reason why so much has been talked about is above all economic: DeepSeek has shown that competitive results can be achieved by spending a fraction of what was thought necessary, both in training and in the cost per single request. To this it is added that the weights of some models are downloadable, so a company can also run them on their own servers instead of switching from the cloud service.

However, there is one point that matters more to those who work in Europe than anyone else: DeepSeek's app and cloud service send data to infrastructure in China. It is not a political judgment, it is a fact of compliance: for personal or confidential data this completely changes the assessment. In this guide we see what DeepSeek is, why it is talked about, and how to reason about privacy and data residency for serious business use.

What DeepSeek is and who develops it

DeepSeek is the name of both the company and the models it produces. It is a Chinese company specializing in large language models (the LLM, the same bricks that stand behind ChatGPT). To understand the category can help guide you on What are LLMs and the wider one on the Main AI Models.

The DeepSeek family typically includes two types of models:

• Generic language models (chats). They answer questions, write texts, summarize documents, generate code: the classic use of a conversational assistant.
• Reasoning-oriented models. Before answering "they think out loud", breaking the problem down into intermediate steps. They are designed for tasks that require logic, mathematics or chains of deduction, where responding instinctively would lead to more errors.

This second category is one of the reasons for DeepSeek's popularity: it brought the reasoning models, previously perceived as expensive and niche, into a much more affordable price range.

Because we talk about it so much

The clamor around DeepSeek does not arise from a single "magical" function, but from a change in expectations about how much it costs to make high-level AI. Three elements explain the attention.

1. Low training costs

The message that hit the industry was: you can train competitive models by spending a lot less than you took for granted. Even after the debate about how these costs really matter, the idea that the advantage doesn't just depend on the accumulated computing power has had a strong impact, even on markets.

2. Cost per request very low

For those who have to put AI into a business process, the cost that matters is that for every request (linked to Token, the units with which models measure incoming and outgoing text). DeepSeek has positioned itself with prices via API typically lower than many competitors: on high volumes, the difference becomes significant.

3. Open and downloadable weights

For some models, DeepSeek has published the weights, i.e. the parameters resulting from training. This means that a company with the proper skills can download and run them on its infrastructure, without depending on the cloud service. It is the same approach as other open weight models such as Llama from Meta and Mistral, and that's why DeepSeek fits into the talk about Open Source AI Models.

DeepSeek by comparison: what sets it apart

To orient yourself without absolute rankings (which age quickly), it is better to think by criteria. The table compares the aspects that really matter in a business choice.

The values above describe structural features, not point numbers: prices, versions, and data residency options change all the time. Always check the supplier's official page.

The point to take home: DeepSeek is strong on performance/price ratio and self-hosting option, while the themes of data residency and compliance are its most delicate side for a European company. If you want a method to weigh these criteria on your case, see how to choose an AI model.

Privacy and data residency: the point that matters for EU companies

This is the section that for an SME or an Italian professional makes the difference between "curious use" and "proper business use". No alarms, just facts.

When you use the DeepSeek app or web chat, what you write (prompt, pasted documents, attachments) is processed on infrastructure in China, then outside the European Economic Area. For the GDPR, a transfer of personal data outside the EU requires specific safeguards and a risk assessment; the Chinese regulatory framework is also different from the European one in terms of data access. Translated into practice:

The roads to reasonable business use are basically three.

For the full picture of these assessments — legal basis, extra-EU transfers, data minimisation è it is useful to set the theme with a AI Consulting before bringing such an instrument into the processes.

Practical examples

Two realistic scenarios of Italian realities, to see how the choice changes based on the data at stake.

1. Marketing agency — drafts of content. An agency wants to reduce the cost of generating first drafts (posts, descriptions, copy variants) for its customers. Texts start from generic briefs and public information, without personal data. Here the DeepSeek API or a self-hosted model can make sense: the volume is high, the cost per request counts and the data is not sensitive. Editorial review remains human.

2. Professional study — analysis of clients' documents. A study would like to use a reasoning model to analyze practices and contracts. Here the documents contain personal and confidential data: using the Chinese app or cloud is not appropriate. The correct route is a model self-hosted inside the studio infrastructure (or a provider with EU data residence), so the information does not leave the perimeter. The theme is the same as in the AI for law firms and in the AI for accountants.

In both cases it does not change the "in-itself" model, but the Data context: it is the one to decide if and how to use it.

Mistakes to Avoid

• Treating DeepSeek as "Cheapest Chinese ChatGPT" and that's it. Price is a real advantage, but ignoring the residence of data on sensitive cases is the most expensive error at the compliance level.
• Pasting customer data into the cloud app. Documents, emails and personal data entered in the cloud service leave the EU. For that data you need self-hosting or an alternative with European data residency.
• Confusing "open weights" with "pure open source". Being able to download the weights does not mean that the data and training process is public or that the license is unconditional. Always read the license of the version you are interested in.
• Citing benchmarks or prices as eternal truths. Numbers, rankings and costs change with each release. Always check the official page and date its context, instead of treating them as stable facts.
• Choosing the model before the process. As with any AI tool, first you define the use case and data type, then you choose the model — not the other way around.

How to apply it in the company

Introducing DeepSeek (or deciding not to use it) follows the same method as any AI model choice, with an extra step devoted to compliance.

1. Define the use case and data. What does the model have to do and, above all, what data does it work on? Public, internal, personal, confidential? This answer guides everything else.
2. Classify data risk. If there is no personal or confidential data, you have more freedom. If there are, the GDPR evaluation on the extra-EU transfer is triggered.
3. Choose the mode of use. Non-sensitive data and tight budget → Official API with caution. Sensitive data → self-hosting of open weights or provider with EU residency.
4. Take a measured test. Compare quality, cost per request and speed on a real sample, against the alternative you already use. Decide on numbers, not sensations.
5. Document the choice. Write down why you chose that model and that mode, especially for privacy themes. It serves you and it serves you in the event of an audit.

This is exactly the kind of assessment in which a consultation saves time and errors: figuring out if DeepSeek is the right choice for your case, or if another road is convenient, and possibly building on it a process automation or a AI Agent that integrate it into your streams. If you just want to see comparison models in real contexts, take a look at the experiments of The Lab.

Conclusion

DeepSeek is a serious player in the AI model landscape: it brings competitive performance, a strong focus on reasoning and low costs, with the valuable option of open weights for those who want self-hosting. It is this combination that explains why so much is said about it. For a European company, however, the decisive factor is not the price but the residence of data: the app and cloud pass from China, so for personal or confidential data the correct way is self-hosting or an alternative with EU data residency. Used with this awareness, DeepSeek is a useful tool; used without, it is a risk of compliance. From here you can continue with the Open Source AI Models and with the method for choose an AI model suitable for your case.

Do you want to understand what processes in your company can be automated with AI? Giallo Studio can help you turn the problem into a real workflow.