An objective comparison of TensorX and OpenAI. OpenAI is an excellent platform that helped define modern AI. This page is not an attack piece; it is here to help you understand the trade-offs around privacy, regulatory compliance and data sovereignty, and to decide which platform fits your needs.
Quick decision guide
OpenAI may suit you if you:
- Need a specific proprietary model or feature only OpenAI offers
- Have no GDPR or sector-specific compliance obligations
- Are comfortable with your data being processed under US jurisdiction
TensorX may suit you better if you:
- Need GDPR or sector-specific compliance (regulated industries)
- Want zero data retention and genuine privacy
- Need EU data sovereignty, free of US jurisdiction
- Prefer leading open-source / open-weight models with no vendor lock-in
- Want the latest leading models live within about 24 hours of release
The core differences
| Feature | TensorX | OpenAI |
|---|---|---|
| Data retention | Zero; deleted after processing | Retained for a limited window for abuse and misuse monitoring |
| Data location | 100% EU (Dublin, Helsinki) | US-based, subject to the US CLOUD Act |
| GDPR | Compliant by design; EU-registered controller | Requires a DPA and Standard Contractual Clauses; transfer risk remains |
| Training on your data | Never | API data excluded by default; the consumer ChatGPT product may use conversations unless you opt out |
| Models | Leading open-source / open-weight models | Proprietary models |
| New models | Latest leading models live within about 24 hours of release | Tied to OpenAI’s own release cycle |
| Vendor lock-in | None; switch models at any time | High; proprietary models |
| API compatibility | OpenAI-compatible; drop-in migration | OpenAI API (the standard) |
Data, retention and EU jurisdiction
Data retention
When you send data to OpenAI’s API, it is retained for a limited window for abuse and misuse monitoring (see OpenAI’s published API data usage policy). For regulated or sensitive workloads, that window can be a compliance consideration. TensorX takes a different approach: prompts and responses are deleted after processing, so there is no retention window to manage.
US jurisdiction and the CLOUD Act
OpenAI is a US-based company, so data flowing through its systems is subject to US jurisdiction. The US CLOUD Act can compel US companies to hand over data they hold, wherever it is stored. For EU organisations this raises well-documented transfer questions under GDPR Article 44 and the Schrems II ruling. TensorX is EU-registered (Ireland) and runs entirely on EU infrastructure, so your data stays within EU jurisdiction.
Training on customer data
OpenAI states that API data is not used to train its models by default, although the consumer ChatGPT product may use conversations unless you opt out. The broader point is about data access: retained data can, in principle, be accessed. TensorX never trains on customer data and does not retain it to analyse.
Migration: how easy is it?
TensorX is OpenAI-compatible, so switching is typically a one-line change. Point the SDK at the TensorX base URL, use your TensorX key, and choose a model.
# OpenAI
from openai import OpenAI
client = OpenAI(api_key="sk-...")
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello!"}]
)# TensorX (OpenAI-compatible)
from openai import OpenAI
client = OpenAI(
base_url="https://api.tensorx.ai/v1",
api_key="tx_..."
)
response = client.chat.completions.create(
model="z-ai/glm-5",
messages=[{"role": "user", "content": "Hello!"}]
)Same SDK, same interface, a drop-in replacement.
When OpenAI makes sense
- You need a specific proprietary model or feature that only OpenAI provides
- You are building something with no personal data and no compliance obligations
- Processing under US jurisdiction is acceptable for your use case
It is worth noting that open-source models have closed much of the gap, and now match or exceed proprietary models on many public benchmarks.
When TensorX makes more sense
- Compliance is required: healthcare, finance, legal, public sector and other regulated industries in the EU
- You handle sensitive data: customer data, PII, trade secrets, legal or medical records
- You want EU data sovereignty: no US jurisdiction over your data
- You want to avoid vendor lock-in: switch open-source models freely as performance and needs change
Common questions
Are open-source models as good as proprietary ones?
For most use cases, yes. Leading open-source models match or exceed proprietary models on common benchmarks, and for the majority of workloads (chat, content generation, code assistance, summarisation) they are an excellent fit. TensorX curates the models that are in demand and adds important new ones within about 24 hours of release.
What about reliability?
TensorX runs enterprise-grade infrastructure with redundant EU GPU capacity, monitoring and support, backed by our Service Level Agreement.
Can I use OpenAI and TensorX together?
Yes. Many teams use a hybrid approach: a proprietary model where a specific feature requires it, and TensorX for privacy-critical or compliance-critical workloads. Because TensorX is OpenAI-compatible, routing between them is straightforward.
The bottom line
OpenAI is a strong platform. Its trade-offs simply do not work for everyone:
- A retention window rather than zero retention
- US jurisdiction rather than EU sovereignty
- Proprietary models rather than open-source flexibility
If privacy, compliance or EU data sovereignty matter to you, TensorX is worth evaluating. You do not have to choose just one: test both and use the right tool for each job.
Get started
TensorX is OpenAI-compatible and EU-hosted. Explore the available models and our transparent pricing, or read about our approach to trust and security and our Data Processing Agreement.