DeepSeek: the tradeoff between cost, performance, and data sovereignty

TL;DR

• DeepSeek offers GPT-5/Claude-level performance at a fraction of the cost
• Problem: their APIs send data to servers in China, subject to state access laws
• Solution for enterprises: run the model locally (open-source) or use alternatives
• GDPR complicates DeepSeek cloud API usage for personal or sensitive data

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In a matter of months, DeepSeek went from unknown to the most debated topic in the industry. Their open-source models rival GPT-5 at a fraction of the training cost. They’re democratizing access to advanced AI in emerging markets. And they’re generating a trust crisis that goes far beyond technology.

If you want to learn how to use DeepSeek, I have a complete guide. Here I focus on risk analysis.

The DeepSeek phenomenon

The numbers are impressive. DeepSeek R1, their reasoning model, offers performance comparable to OpenAI’s o1 but at dramatically lower API costs. The model is open-source, meaning anyone can download and run it locally.

For startups, researchers, and budget-constrained companies, this sounds like a gift from heaven. And in markets like Africa or Latin America, where access to OpenAI or Anthropic APIs can be prohibitive, DeepSeek is opening doors that seemed closed.

The problem nobody wants to see

Here’s the uncomfortable part. According to multiple security analyses, DeepSeek stores user data on servers located in China. And Chinese law requires companies to share data with state intelligence services if requested.

Bill Conner, CEO of Jitterbit and former advisor to Interpol and GCHQ, puts it bluntly: companies cannot justify integrating systems where data residency, usage intent, and state influence are fundamentally opaque.

It’s not paranoia. It’s basic risk management.

The dilemma for European companies

If you work at a European company, the equation gets even more complicated. GDPR establishes strict requirements for data transfers to third countries. China is not on the list of countries with adequate protection levels.

Does this mean you can’t use DeepSeek? Not necessarily. There are important nuances.

Using DeepSeek’s cloud API: Problematic. Your prompts and data travel to their servers. If you process personal data or sensitive company information, you’re in legal and security risk territory. This applies to any cloud AI, as I explain in AI as the new data leak vector.

Downloading and running the model locally: Much safer. The open-source model can run on your own infrastructure. Data never leaves your servers. This eliminates the sovereignty problem, though it requires significant compute capacity.

Using it for non-sensitive tasks: If you only use DeepSeek to generate boilerplate code or answer generic questions, the risk is lower. But where do you draw the line?

Alternatives to consider

The market isn’t just OpenAI vs DeepSeek. Intermediate options exist:

Mistral (France): High-performance open-source models with European development. Native GDPR compliance.

Meta’s Llama: Open-source, locally executable, no Chinese infrastructure dependency.

Anthropic’s Claude: Cloud API, but with servers in regions you can choose and more transparent privacy policies.

Local models with Ollama: If you have sufficient hardware, you can run Llama, Mistral, or even DeepSeek completely offline.

For a detailed capability comparison, check my article on ChatGPT vs Gemini vs Claude.

My take

DeepSeek’s appeal is undeniable. They’ve proven you can compete with giants without billion-dollar budgets. That’s good for the industry.

But trust, transparency, and data sovereignty aren’t “nice to have.” They’re fundamental requirements for any technology you integrate into your enterprise stack.

If you’re an individual developer experimenting, go ahead. If you’re a company processing European customer data, the answer should be running the model locally or finding alternatives.

The lowest cost means nothing if the real price is your reputation, your customers’ trust, or a GDPR fine.

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Do you use DeepSeek at work? Have you found ways to mitigate the risks?