DeepSeek V4 Data Retention: What to Ask Before Integration

Hey fellow API integrators — if you've spent any time digging into DeepSeek's pricing and thought "wait, where exactly does my data go?" — this one's for you. I'm Hanks, and I test AI tools inside real workflows, not demos. When I started evaluating DeepSeek for a production pipeline in late 2025, the first thing that stopped me wasn't the model quality. It was a much simpler question:

Can I actually tell my compliance team where this data lives?

Turns out, that question is a lot harder to answer than DeepSeek's documentation makes it look. Here's everything I found — and the checklist I wish I'd had before I started.

Why Data Retention Matters

Let me be blunt. DeepSeek's models — V3.1, V3.2, and the anticipated V4 — are genuinely impressive. Token prices run 10–30x cheaper than OpenAI or Anthropic, and benchmark performance rivals GPT-4o-class models on most coding and reasoning tasks. The cost efficiency is real.

But here's the part that nearly made me close the browser tab: every prompt you send to DeepSeek's hosted API is processed and stored on servers in mainland China.

This isn't FUD. It's in DeepSeek's own privacy policy, updated December 22, 2025:

"We directly collect, process and store your Personal Data in the People's Republic of China."

For individual developers testing a side project? Maybe you shrug and move on. For teams handling customer data, proprietary codebases, or anything touching regulated industries — that sentence should give you pause.

Here's why this is operationally significant, not just legally abstract:

• Under China's National Intelligence Law, DeepSeek is legally required to cooperate with government data requests — and there's no independent court to push back against those requests the way a US or EU company might
• In early 2025, Italy's data protection authority banned DeepSeek outright after the company failed to adequately explain its cross-border data transfers under GDPR
• A January 2025 investigation by Wiz Research found a publicly accessible ClickHouse database containing over a million lines of real user chat histories and API secrets — it was locked down within 30 minutes of disclosure, but the exposure window is unknown

So before you commit to an integration, the retention question isn't philosophical. It's operational.

Questions for API Users

When I was evaluating DeepSeek's hosted API for a content processing pipeline, I ran through a set of questions that most teams skip in the excitement of seeing cheap inference costs. Here's what actually matters.

Logging Policies

DeepSeek's privacy policy acknowledges collecting:

• User inputs (prompts, uploaded files)
• Device and network data (IP, device model, OS)
• Keystroke patterns and rhythm data
• Log data from your API session

I kept asking myself: what exactly is logged at the API level versus the chat interface level? The honest answer is that the policy doesn't clearly separate these. The privacy policy states data is retained "as long as necessary to fulfill purposes" — which is vague enough to mean indefinitely depending on how DeepSeek defines "necessary."

What third-party researchers have estimated (though DeepSeek hasn't formally confirmed all of these figures):

The critical point: deleting a chat session in the UI does not guarantee that data has been removed from backup copies or prior training sets. There's currently no public evidence of anonymization techniques like differential privacy being applied to your inputs before they enter model training pipelines.

Questions to ask before signing any enterprise agreement:

1. Does the API suppress prompt logging for my account specifically?
2. Can I request deletion of all API-sourced records, and what's the verified timeline?
3. Are my inputs used in future model training? Can I opt out?
4. What's the backup retention schedule, and does deletion propagate to backups?

Zero-Retention Options

Here's where it gets interesting. DeepSeek's hosted API currently offers no publicly documented zero-retention tier for API users — unlike some Western providers that offer enterprise agreements with explicit no-training, no-logging commitments.

If your use case requires zero-retention guarantees, you have two realistic paths:

Path 1: Use DeepSeek weights via a compliant cloud provider. Google Cloud Vertex AI and Microsoft Azure AI Foundry both list DeepSeek models in their catalogs. When you run DeepSeek through these platforms, your data stays within that provider's infrastructure and is subject to their (not DeepSeek's) data policies. This is meaningfully different from hitting api.deepseek.com directly.

Path 2: Self-host. More on this below.

Self-Hosting Considerations

I'll be honest — this is where the data retention question gets cleanest, and also where the infrastructure requirements get genuinely daunting.

DeepSeek's V3.1 and V3.2 models are released under the MIT license, which means you can download the weights from Hugging Face and run them on your own infrastructure. No prompts leave your servers. No exposure to Chinese data laws. No ambiguity about where your data lives.

The catch: running DeepSeek at full fidelity requires 8× NVIDIA H200 GPUs. That's not a solo dev weekend project.

Here's a practical breakdown of self-hosting paths:

The quantized distilled variants are worth noting specifically. DeepSeek released six distilled versions of R1 — ranging from 1.5B to 70B parameters — built on Llama 3.1/3.3 and Qwen 2.5 base models. The 32B and 70B versions retain most of the reasoning capability and can run on high-end consumer hardware. If your use case doesn't require the full 671B model, this is a legitimately viable path for teams that need data control without enterprise GPU budgets.

A quick self-host test I ran using the 70B distilled variant via Ollama:

bash

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull DeepSeek R1 70B distilled
ollama pull deepseek-r1:70b
# Run with OpenAI-compatible endpoint
ollama serve
# Then query at http://localhost:11434/v1/chat/completions

The tradeoff: you're not getting the full V3.1/V3.2 capability. But you're also not routing any data through China.

Template: Vendor Assessment Checklist

When evaluating DeepSeek or any third-party AI provider for integration, this is the checklist I now run before any technical proof-of-concept begins. I built this after getting burned by assumptions.

Data Location & Sovereignty

• Where are servers physically located?
• Which legal jurisdictions govern data access requests?
• Is there an adequacy decision or equivalent safeguard for cross-border transfers under your region's privacy law (GDPR, CCPA/CPRA, etc.)?

Logging & Retention

• What data is logged at the API layer (prompts, responses, metadata)?
• What are the retention periods by data type?
• Is there a formal zero-retention or no-training tier? Is it documented in a Data Processing Agreement (DPA)?
• Does prompt deletion propagate to backups?

Training Data Usage

• Are API inputs used to train or fine-tune models?
• Is there an opt-out mechanism, and is it enforceable in your contract?

Security Posture

• Has the vendor undergone independent security audits? Are reports available?
• What encryption standards apply to data at rest and in transit?
• Are there known past exposure incidents? What was the remediation timeline?

Regulatory Compliance

• Is the vendor currently under investigation by any data protection authority?
• For EU users: has the vendor appointed an EU representative (required under GDPR Article 27)?
• For US government or defense contractors: are there existing restrictions on this vendor's use?

For DeepSeek specifically as of February 2026: Italy has banned the service; multiple EU data protection authorities (Ireland, France, Belgium, Netherlands) have open investigations; the U.S. House of Representatives has warned members against using the app on official devices. These are facts, not FUD — they belong in your vendor assessment.

Get Your Questions Answered Before You Commit

The friction I kept running into during this evaluation wasn't technical — it was getting the right questions organized before a vendor conversation. Vendor assessment checklists are easy to forget in the rush of a proof-of-concept; use Macaron to prepare your critical data retention questions before integration by creating a custom mini-app from that checklist, so nothing slips through when you're talking to a vendor or running a compliance review.

FAQ

Q: Does DeepSeek V4's API offer a zero-retention option like OpenAI's enterprise tier? A: Not as of February 2026. DeepSeek's hosted API does not publicly document a zero-retention or no-training enterprise tier. If this is a hard requirement, your options are using DeepSeek weights through a compliant cloud provider (Azure, GCP) or self-hosting.

Q: Is the DeepSeek API GDPR-compliant? A: Currently no, by most regulatory assessments. Italy's data protection authority banned DeepSeek in early 2025 for failing to demonstrate GDPR-compliant cross-border data transfer safeguards. Multiple other EU DPAs have open investigations. DeepSeek appointed an EU representative in late May 2025 — five months after Italy's ban — which is one required step, but doesn't resolve the underlying data transfer question.

Q: If I self-host DeepSeek weights, does China data law still apply? A: No. When you run DeepSeek on your own infrastructure, prompts never reach DeepSeek's servers. There's no data transfer to China, no exposure to Chinese national security laws. This is the most direct way to address data sovereignty concerns, and the IAPP has confirmed this position.

Q: Are DeepSeek distilled models safe to self-host? A: From a data sovereignty perspective, yes — if you're running them locally, no data leaves your environment. Note that multiple security evaluations found that vulnerabilities in model behavior (like censorship of certain topics and susceptibility to jailbreaks) exist in the model weights themselves and persist regardless of hosting location. The U.S. Center for AI Standards and Innovation published findings on this in October 2025, testing on locally run weights.

Q: What's the most practical path for a small team that wants DeepSeek performance without China data exposure? A: Run DeepSeek via Azure AI Foundry or Google Vertex AI. You get access to the model within an infrastructure you can actually sign a DPA with, and your data stays in your chosen region. Slightly higher cost than hitting the native API, but operationally much cleaner for any regulated use case.