In January 2026, OpenAI updated its terms of service to explicitly allow using user conversations to train future models by default. Within 24 hours, the #1 post on Hacker News was a thread on how to delete your OpenAI account — with 210 points and 131 comments from developers who'd had enough.
The concern isn't paranoia. When you paste production code, internal prompts, or customer data into ChatGPT, you're sending that data to OpenAI's servers. What happens to it after that is governed by a terms of service document that changes without notice.
This is why an accelerating number of engineering teams are either running LLMs locally or switching to API providers with explicit no-training guarantees.
The Privacy Problem with ChatGPT (And Similar Products)
ChatGPT, Gemini, and Claude.ai all share a fundamental characteristic: they're conversational products, not developer APIs. When you use the product, your data flows into infrastructure optimized for model improvement, not data isolation.
The specific risks that enterprise teams flag most often:
- Training data exposure: Default settings in most consumer AI products allow conversation data to improve models
- No data residency control: You can't guarantee where your data is processed or stored
- Retroactive policy changes: Terms can change with 30-day notice; you're locked into whatever they decide
- No audit trail: Enterprise teams often need to prove what AI systems accessed their data — impossible with black-box products
Option 1: Run LLMs Locally
The most privacy-preserving option is running a model entirely on your own hardware. Tools like Ollama, LM Studio, and Jan make this straightforward on modern hardware.
Best local models in 2026:
- Llama 3.3 70B — Meta's best open weights model; competitive with GPT-4o on most benchmarks; requires ~40GB VRAM
- Unsloth Dynamic 2.0 GGUFs — Quantized Llama variants that run on consumer GPUs (8–16GB VRAM) with minimal quality loss
- Mistral 7B / Mixtral 8x7B — Strong performance per compute dollar; excellent for code tasks
- Phi-4 — Microsoft's small model; remarkable at reasoning tasks for its size; runs on CPU
The tradeoff: Local inference requires hardware investment, ongoing maintenance, and loses access to frontier model capabilities. A 70B local model is good — but it's not GPT-4o Turbo or Claude Opus 4.
Option 2: Private API Access (Best of Both Worlds)
For teams that need frontier model quality without consumer product privacy risks, dedicated API access is the practical middle ground.
The key difference between the ModelsLab API and ChatGPT: when you call an API with your API key, you're making a programmatic request under a specific commercial agreement. You can negotiate data handling, you have audit logs of every call, and your data is not being mixed with consumer conversations.
ModelsLab's API platform gives developers direct access to Stable Diffusion, LLaMA, Flux, and 200+ other models with:
- No training on your API calls
- Full request/response logging you control
- Predictable pricing per inference call
- On-premise deployment options for sensitive workloads
The Developer's Checklist for AI Privacy
Before integrating any AI tool into a production workflow, run through these questions:
- Is this a consumer product or a developer API? Consumer products have worse data isolation by default.
- Does the provider's ToS allow training on API calls? Read the specific API terms, not the consumer terms.
- Can you request data deletion? GDPR and CCPA require this for EU/CA users.
- Is there an enterprise agreement available? Most providers offer stricter data handling for teams that ask.
- What's the data residency story? For regulated industries, you need to know where inference happens.
Running Llama 3.3 Locally with Ollama
The simplest path to local LLM inference:
# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh
# Pull and run Llama 3.3 70B
ollama pull llama3.3:70b
ollama run llama3.3:70b
# Or use a quantized variant for lower VRAM
ollama pull llama3.3:70b-instruct-q4_K_M
For production use, Ollama exposes an OpenAI-compatible API at localhost:11434. You can swap out the OpenAI base URL in your existing code and get fully local inference with no code changes.
from openai import OpenAI
# Point to local Ollama instead of OpenAI
client = OpenAI(
base_url="http://localhost:11434/v1",
api_key="ollama" # not used, but required
)
response = client.chat.completions.create(
model="llama3.3:70b",
messages=[{"role": "user", "content": "Explain quantum entanglement"}]
)
print(response.choices[0].message.content)
When Local Isn't Enough: API Alternatives
Some workloads genuinely need frontier model quality that local models can't match — complex reasoning, multimodal tasks, or long-context processing. For those use cases, the question becomes: which API provider has the strongest privacy posture?
Key factors to compare:
- Explicit no-training clause in API terms (not just product terms)
- SOC 2 Type II or ISO 27001 certification
- Data residency options (EU/US/APAC)
- Enterprise DPA (Data Processing Agreement) availability
ModelsLab's API tier includes a DPA on enterprise plans and processes requests without storing conversation data beyond the session window needed for the request. See the API docs →
The Bottom Line
ChatGPT is a product. The API is a tool. The difference matters — especially when you're putting production code, user data, or proprietary prompts into an AI system.
For most developer teams in 2026, the right answer is a hybrid: local models for sensitive internal tasks, frontier APIs with explicit data handling agreements for customer-facing features.
The developers deleting their OpenAI accounts aren't abandoning AI — they're getting more deliberate about which AI infrastructure their business depends on.