European OpenAI alternative
for regulated workloads.
Drop-in OpenAI-compatible inference, run by an EU controller (France), with the model inside Intel TDX enclaves at our inference provider. Plans from $20/month to enterprise contracts up to $5,000/month.
Requests pass through our relay, which sees them in clear (TLS ends at our host). To keep plaintext off our servers, run your own inference server in a Confidential VM and terminate TLS inside its trust domain. Confidential VMs
For organisations evaluating OpenAI or Azure OpenAI but blocked by Schrems II, CLOUD Act exposure, or EU AI Act readiness.
The problem
Three frictions European buyers hit with OpenAI.
Forrester estimates the European sovereign AI market at €1.5 trillion in cumulative spend through 2030. The frictions below explain why so many of those euros are not going to OpenAI or Azure OpenAI by default.
Schrems II is still unresolved for OpenAI
OpenAI is a US controller subject to FISA 702 and the CLOUD Act. After the Schrems II ruling, any transfer of personal data to US providers requires Standard Contractual Clauses plus a transfer impact assessment. An increasing number of European DPOs decline to sign that combination for sensitive workloads, privileged legal documents, patient records, financial models, public-sector data.
Azure OpenAI does not eliminate the parent risk
Azure OpenAI offers EU regions and Microsoft Ireland as the contracting entity, but Microsoft Corporation remains the US parent. The CLOUD Act applies to the parent, not the regional billing entity. For tenders that explicitly screen out US-parent processors, Azure OpenAI does not pass the filter.
EU AI Act adds a provider-side burden
General-purpose AI providers placing systems on the EU market must publish model cards, transparency notices, copyright disclosures and post-market monitoring documentation. European buyers increasingly prefer a European controller that can respond to those obligations directly under EU law, rather than chasing US headquarters for documentation.
Our answer
EU controller plus models in Intel TDX.
We are not just another European inference provider. The combination that matters for regulated workloads is jurisdictional and technical: the controller is European AND the model's host is outside the trust boundary. Our relay still sees requests in clear.
EU controller (VOLTAGE EI, France)
The contracting and processing entity is registered in France (SIREN 943 808 824), with a sub-processor chain documented in the DPA rather than claimed to be EU-only. GDPR Article 28 DPA is provided by default. No US parent; our US sub-processors remain subject to US law.
Intel TDX enclaves with attestation
The model runs inside Intel TDX Trust Domains at our inference provider. Memory is encrypted by the CPU, the hypervisor and host operator are outside the trust boundary, and GET /v1/attestation returns the quotes of the serving instances on your nonce; it does not prove which image they run.
EU AI Act-aligned transparency
Public model cards, transparency notices, copyright posture for training data, retention rules and post-market monitoring documentation. The same pack a European buyer would expect from a regulated processor.
OpenAI-compatible API surface
The chat.completions endpoint accepts the same payloads as the OpenAI API; embeddings and images are not offered (no TEE model). Migrating a working integration is a base_url swap and an API key change in your existing SDK code. Requests pass through our relay, which sees them in clear (TLS ends at our host). To keep plaintext off our servers, run your own inference server in a Confidential VM and terminate TLS inside its trust domain.
Side-by-side
How we compare to the obvious alternatives.
Direct factual comparison on the dimensions that drive procurement decisions for regulated workloads in the EU. No marketing claims, only attributes you can verify from public documentation and your own DPA review.
Sources: OpenAI public DPA, Azure OpenAI Service documentation, Mistral La Plateforme terms, Aleph Alpha public materials. Values reflect public posture as of 2026 and are verifiable in each provider's contractual documents.
Use cases
Where this combination unlocks deployment.
Four sectors where OpenAI/Azure OpenAI deployment routinely stalls in legal review and where the VoltageGPU posture clears the path.
Legal, privileged work product
Law firms and in-house teams cannot send client documents through US-controlled inference. VoltageGPU runs contract review, due diligence, and legal research inside Intel TDX with French controller status, removing the FISA 702 question before it reaches the audit committee.
See Harvey AI comparisonFinance, model risk and DORA
Banks, asset managers and insurers operating under DORA need controllable processors and demonstrable resilience. VoltageGPU maps to Article 28 GDPR plus DORA third-party risk requirements, with sub-processor lists, attestation evidence and an EU controller for incident notification.
DORA compliancePublic sector, sovereignty by default
European public buyers increasingly mandate sovereign infrastructure (SecNumCloud qualification, EU control, no US extraterritorial reach). VoltageGPU is not SecNumCloud-qualified and runs GPU compute on US sub-processors; what it offers is a French controller, a sub-processor chain documented in the DPA rather than claimed to be EU-only, and attestation of the serving instances.
Sovereign AI FranceHealthcare, patient confidentiality
Hospitals, biotech and clinical research need processors that work under HDS (France) and equivalent national frameworks. Running the model in a hardware enclave takes the model's host out of the trust boundary; our relay still sees requests in clear, and we hold no HDS certification (see /trust).
Confidential AI agentsMigration
Drop-in OpenAI SDK swap.
Migrating a working OpenAI integration to VoltageGPU is a configuration change, not a rewrite. Three values move: the base_url, the API key and the model id (an exact -TEE id from GET /v1/models). Chat completion payloads stay the same. Requests pass through our relay, which sees them in clear (TLS ends at our host). To keep plaintext off our servers, run your own inference server in a Confidential VM and terminate TLS inside its trust domain.
# Before, OpenAI
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# After, VoltageGPU (same SDK; base_url, key and model change)
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VOLTAGEGPU_API_KEY"],
base_url="https://api.voltagegpu.com/v1",
)
# Everything else stays identical
resp = client.chat.completions.create(
model="google/gemma-4-31B-turbo-TEE",
messages=[{"role": "user", "content": "Summarise this contract clause."}],
)// Before, OpenAI
import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
// After, VoltageGPU
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.VOLTAGEGPU_API_KEY,
baseURL: "https://api.voltagegpu.com/v1",
});
// Same .chat.completions.create() call shape afterwards.Swap base_url
Point the existing OpenAI client at https://api.voltagegpu.com/v1 and use a VoltageGPU API key. Requests pass through our relay, which sees them in clear (TLS ends at our host). To keep plaintext off our servers, run your own inference server in a Confidential VM and terminate TLS inside its trust domain.
Pin a TEE model
Set the model to an exact -TEE id from GET /v1/models, such as google/gemma-4-31B-turbo-TEE. Each runs inside an Intel TDX enclave at our inference provider.
Run a parallel eval
Mirror a representative slice of production prompts to the new endpoint and compare outputs before cutover.
Cut over with a flag
Use a feature flag or a percentage rollout to move traffic without a hard switch. Roll back instantly if needed.
Pricing
Solo to enterprise, predictable plans.
Five tiers cover the full range, from a single regulated user up to a dedicated regional cluster with a signed DPA, named DPO contact, and a BYOA deployment.
Plus
Individual user, single seat
Starter
Small team, shared workspace
Pro
Up to 10 seats, OpenAI-compatible API
Enterprise
SSO, SCIM, audit logs, DPO contact
Custom
Dedicated capacity, BYOA, regional cluster
FAQ
Frequently asked questions.
Can I migrate without rewriting my OpenAI integration?
Yes. The inference API is OpenAI-compatible. Change the base_url in your existing OpenAI SDK code (Python, Node, .NET, Go, Java, all the official SDKs accept a custom base URL) and the API key. Chat completions, streaming and tool calling keep the same request and response schemas. Requests pass through our relay, which sees them in clear (TLS ends at our host). To keep plaintext off our servers, run your own inference server in a Confidential VM and terminate TLS inside its trust domain. Most teams complete a functional migration in an afternoon and then run a parallel evaluation before cutting traffic.
What models do you run, and how do they compare to GPT-4?
We run Qwen3.5-397B-TEE and DeepSeek-V3.2-TEE inside the Intel TDX enclave. On open benchmarks (MMLU, GSM8K, HumanEval, LegalBench, MedQA) these models score within striking distance of GPT-4-class systems, and DeepSeek-V3.2-TEE specifically performs strongly on chain-of-thought reasoning. We trade a small amount of marginal capability on some tasks for hardware confidentiality and EU jurisdiction. For regulated workloads, that trade is the right one.
How does the controller relationship actually work?
VOLTAGE EI (France, SIREN 943 808 824) is the controller for your account data and the processor for the inference workload, depending on the use case. The DPA is GDPR Article 28 by default, sub-processors are listed in Annex III, and security measures are documented in Annex II. There is a named DPO contact for Enterprise customers and a vendor questionnaire pack covering ISO 27001-aligned controls.
Are you cheaper than Azure OpenAI for high-volume API workloads?
For per-token API workloads on Pro and Enterprise plans, our list price is competitive with Azure OpenAI. For organisations that already pay Azure OpenAI a steep enterprise commitment but only use a fraction of the capacity, our usage-based plans are typically more efficient. We are happy to run a real-cost comparison from a sample month of your Azure OpenAI invoice.
How do you handle the EU AI Act?
We publish model cards for every TEE model, transparency notices on how outputs are produced, copyright posture for training data sourced from open datasets, retention rules and attestation of the serving instances. Because we are an EU controller, the EU AI Act applies directly to us.
Can I run my own custom agent or fine-tuned model in the same enclave?
Yes. The Bring-Your-Own-Agent (BYOA) program packages your custom agent or fine-tuned model into a TEE image, signs it, and runs it under your tenant on attested hardware. Enterprise customers can also reserve dedicated capacity in a regional cluster.
What is the realistic deployment timeline?
A solo user is productive on the Plus plan within minutes. A small team on Starter is productive within a day. A Pro deployment with API integration into an existing application typically takes one to two weeks including evaluation. Enterprise deployments with SSO, SCIM, audit log piping into a SIEM and a signed DPA typically run four to eight weeks end-to-end.
Where can I read the technical proof?
The /confidential-compute page exposes the attestation flow, the model cards, the sub-processor list and the security measures. For deeper engineering due diligence, Enterprise prospects can ask for a technical brief on the Intel TDX threat model and GET /v1/attestation.
Migrate one workload first. Run it under EU jurisdiction.
Start with a free account. Move a single endpoint with a base_url swap. Keep the rest of your stack identical. Requests pass through our relay, which sees them in clear (TLS ends at our host). To keep plaintext off our servers, run your own inference server in a Confidential VM and terminate TLS inside its trust domain.