title: "EU LLM API Comparison 2026: JuiceFactory vs Mistral vs Scaleway vs Nebius" description: "Hands-on comparison of four EU LLM APIs. Pricing, GDPR compliance, latency benchmarks, and code examples to help you choose the right provider." date: 2026-04-06 slug: eu-llm-api-comparison tags: [LLM, EU, AI, API, GDPR, comparison, guide] category: AI Infrastructure schema: TechArticle

EU LLM API Comparison 2026: JuiceFactory vs Mistral vs Scaleway vs Nebius

Not all "EU AI" providers are the same. EU data residency is the baseline — what differs is whether the provider retains your data, what models are available, how latency holds up from European regions, and whether their compliance story actually works under audit.

This guide compares four providers directly: JuiceFactory, Mistral, Scaleway, and Nebius. We cover pricing, data retention, GDPR architecture, latency from Sweden, and the cases where each one makes sense. Working Python code throughout.

To test JuiceFactory yourself: Get a free API key — no credit card required.

The short version

If you need strict GDPR compliance for sensitive data — healthcare, finance, legal, public sector — JuiceFactory is the clearest choice. Stateless architecture means nothing is retained, and the compliance story holds up under technical audit.

If cost is your primary driver and your data isn't sensitive, Mistral is worth evaluating. Their Mixtral 8x7B model is significantly cheaper and performs well for general use cases.

For everything else, the decision matrix below covers the nuances.

Provider overview

What "stateless" actually means

Most EU providers process data in EU data centers — that's necessary but not sufficient. What they do with your data during and after the request varies significantly.

Mistral, Scaleway, and Nebius retain request data for some period (30 days in some configurations, configurable in others). This means your prompts sit in their infrastructure, subject to their security practices, and potentially in scope for GDPR Article 17 erasure requests.

JuiceFactory processes requests in memory and discards everything immediately after the response. No logs containing your prompts, no storage, no training. For applications handling sensitive data, this is the difference between a simple compliance story and a complex one.

Pricing

Per million tokens (EUR, as of March 2026). Nebius uses per-hour GPU billing instead.

Mistral pricing excluded — their published rates change frequently. Check mistral.ai for current prices.

Note: Scaleway's generative API and Nebius GPU pricing look dramatically cheaper per token. The comparison isn't apples-to-apples — neither offers zero-retention stateless processing. You're comparing a compliance-ready managed service against raw infrastructure.

On the cost difference

Scaleway's generative API is substantially cheaper than JuiceFactory per token. That's real, and worth acknowledging.

The calculation changes when you factor in compliance overhead. Working with a stateful provider still requires:

• Legal review of their DPA (typically €5K–15K one-time cost)
• Ongoing GDPR compliance verification
• Handling Article 17 erasure requests for stored data
• Incident response planning for retained data

For organizations in regulated sectors, the TCO difference narrows significantly.

Cost example — processing 100,000 documents (2,000 input tokens, 500 output tokens each):

def monthly_cost(docs, input_tokens, output_tokens, input_price, output_price):
    """Calculate monthly API cost. Prices are per million tokens."""
    return (docs * input_tokens / 1_000_000 * input_price +
            docs * output_tokens / 1_000_000 * output_price)

print(f"JuiceFactory Qwen3 30B: €{monthly_cost(100_000, 2000, 500, 2.00, 10.00):,.0f}/month")
print(f"Scaleway Generative API: €{monthly_cost(100_000, 2000, 500, 0.15, 0.35):,.0f}/month")

JuiceFactory Qwen3 30B: €900/month
Scaleway Generative API: €48/month

For cost-sensitive use cases with low data sensitivity, Scaleway wins on price. For regulated industries handling personal data, factor in the full compliance picture — and whether your DPO is comfortable with data retention at the provider.

GDPR compliance: what each provider actually gives you

flowchart TD
    A[Your prompt] --> B{EU Provider}
    B --> C[JuiceFactory]
    B --> D[Scaleway / Mistral / Nebius]
    C --> E[Processed in RAM]
    E --> F[Response returned]
    F --> G[Data discarded — nothing stored]
    D --> H[Processed]
    H --> I[Response returned]
    I --> J[Data retained for configurable period]
    J --> K[Subject to erasure requests]

Data retention policies across providers:

Mistral's retention policies have changed multiple times. Verify their current terms at mistral.ai before relying on specific numbers.

For JuiceFactory specifically: your prompt loads into GPU memory, the model generates a response, and both are discarded. No disk writes during the request lifecycle. Operational logs contain only timestamps, request IDs, token counts, and latency — not your content.

This matters practically for GDPR Article 17 (Right to Erasure). With stateless processing, there's nothing to erase. With providers that retain data, you need a mechanism for handling deletion requests across their infrastructure.

Integration code

All four providers can be accessed through the OpenAI SDK (with varying degrees of compatibility). Here's how to set up JuiceFactory:

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ.get("JUICEFACTORY_API_KEY"),
    base_url="https://api.juicefactory.ai/v1"
)

def generate(prompt: str, system: str = None) -> str:
    messages = []
    if system:
        messages.append({"role": "system", "content": system})
    messages.append({"role": "user", "content": prompt})

    response = client.chat.completions.create(
        model="qwen3-vl",
        messages=messages,
        max_tokens=500,
        temperature=0.7
    )
    return response.choices[0].message.content

# Embeddings
def embed(text: str) -> list[float]:
    result = client.embeddings.create(
        model="qwen3-embed",
        input=text
    )
    return result.data[0].embedding  # 2560-dimensional vector

Multi-provider failover

If you want redundancy across providers, this pattern handles automatic failover:

from openai import OpenAI
import os

class MultiProviderLLM:
    PROVIDERS = {
        "juicefactory": {
            "base_url": "https://api.juicefactory.ai/v1",
            "api_key_env": "JUICEFACTORY_API_KEY",
            "model": "qwen3-vl"
        },
        "mistral": {
            "base_url": "https://api.mistral.ai/v1",
            "api_key_env": "MISTRAL_API_KEY",
            "model": "mistral-large-latest"
        },
        "scaleway": {
            "base_url": "https://api.scaleway.com/llm/v1",
            "api_key_env": "SCALEWAY_API_KEY",
            "model": "llama-3.1-70b"
        }
    }

    def __init__(self, failover_order=None):
        self.failover_order = failover_order or ["juicefactory", "mistral", "scaleway"]
        self.clients = {
            name: OpenAI(
                api_key=os.environ.get(cfg["api_key_env"], ""),
                base_url=cfg["base_url"]
            )
            for name, cfg in self.PROVIDERS.items()
            if os.environ.get(cfg["api_key_env"])
        }

    def generate(self, prompt: str, max_tokens: int = 500) -> tuple[str, str]:
        for provider in self.failover_order:
            if provider not in self.clients:
                continue
            try:
                cfg = self.PROVIDERS[provider]
                response = self.clients[provider].chat.completions.create(
                    model=cfg["model"],
                    messages=[{"role": "user", "content": prompt}],
                    max_tokens=max_tokens
                )
                return response.choices[0].message.content, provider
            except Exception as e:
                print(f"{provider} failed: {e}")
        raise RuntimeError("All providers failed")

llm = MultiProviderLLM()
answer, used_provider = llm.generate("Summarize GDPR Article 28 in one paragraph.")
print(f"[{used_provider}] {answer}")

Latency: run your own benchmark

Latency depends heavily on your location, request size, and current load. Rather than publishing numbers that will be outdated by the time you read this, here's a script to measure it yourself from your own infrastructure:

To benchmark any provider:

import time
import statistics
from openai import OpenAI

def benchmark(client: OpenAI, model: str, iterations: int = 100) -> dict:
    latencies = []
    ttft_values = []
    prompt = "Summarize GDPR data minimization requirements in 2 sentences."

    for _ in range(iterations):
        start = time.time()
        stream = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            max_tokens=100,
            stream=True
        )
        first_token_time = None
        for chunk in stream:
            if chunk.choices[0].delta.content and first_token_time is None:
                first_token_time = time.time()
                ttft_values.append((first_token_time - start) * 1000)
        latencies.append((time.time() - start) * 1000)

    return {
        "p50_ms": round(statistics.median(latencies)),
        "p95_ms": round(sorted(latencies)[int(len(latencies) * 0.95)]),
        "ttft_avg_ms": round(statistics.mean(ttft_values))
    }

When to choose which provider

JuiceFactory — when GDPR compliance is non-negotiable. Healthcare, finance, legal, government, or any application processing personal data where you need a clean audit trail. The 128K context window also makes it practical for long-document use cases.

Mistral — when cost is the primary driver and your data sensitivity is low. Strong French-language support. Their proprietary models are competitive for general text tasks.

Scaleway — when you want to fine-tune or host custom models, or you're already in the Scaleway ecosystem. Good for teams that want more control over the model layer.

Nebius — when you have specific GPU requirements or need to deploy open-source models at scale. Raw infrastructure rather than a managed inference service.

A note on the CLOUD Act

US providers can be compelled by US courts to disclose EU customer data — regardless of GDPR compliance. This applies to any provider with a US parent company, even if data is hosted in EU data centers.

JuiceFactory is a Swedish company with no US parent. There's no CLOUD Act exposure. For regulated industries and public sector clients, this is increasingly a procurement requirement, not just a preference.

FAQ

Is EU data residency enough for GDPR compliance? No, it's necessary but not sufficient. You also need documented data processing agreements, defined retention policies, lawful basis for processing, and data subject rights infrastructure. Stateless providers like JuiceFactory simplify this significantly.

Do all EU providers offer zero data retention? No. Mistral and Scaleway retain data with configurable windows. JuiceFactory's zero-retention is architectural — there's no configuration option to enable retention because the system never stores data.

Can I use multiple providers for redundancy? Yes. The multi-provider failover pattern above handles this. All four providers can coexist in the same application.

What's the practical difference between 1024-dim and 2560-dim embeddings? Higher-dimensional embeddings capture more semantic nuance and generally perform better on retrieval tasks, especially for technical or specialized content. JuiceFactory's 2560-dim Qwen3-Embed outperforms standard 1024-dim models on most RAG benchmarks.

Start testing: Get a free JuiceFactory API key — or explore the portal.

Related guides: Migrate from OpenAI to EU API · Stateless Inference and GDPR · RAG in Python