# Cloud LLM Hosting: Scalable, Private, and Green Infrastructure

## 👉[Try Regolo Green LLM Hosting](https://dashboard.regolo.ai)

European companies are stuck in a dilemma. You need the scale of cloud LLMs to compete, but **relying on US providers means losing control over data residency (GDPR risks)**, facing opaque billing spikes, and inheriting a massive, hidden carbon footprint that violates emerging ESG reporting rules.

Running "private" AI often means managing painful bare-metal clusters or accepting that your provider might silently use your data for training.

**Deploy open-source LLMs on a 100% European, GDPR-native infrastructure in minutes.** [Regolo](https://regolo.ai/) offers serverless GPU scaling, zero-retention privacy, and real-time energy tracking (Token/Watt) for the AI Act era.

## **Outcome**

- **Data Sovereignty:** All inference runs in Italy (EU) on renewable-powered data centers. No data ever leaves the continent or is retained for training.
- **Green Compliance:** Regolo is the first provider to expose "Token-to-Watt" metrics, allowing you to audit the exact energy cost of your AI—essential for new EU AI Act disclosure rules.​
- **Elastic Scale:** Kubernetes-native serverless architecture means you pay only for inference time (per token/second), not for idle GPUs.

## **Prerequisites (Fast)**

- **Regolo Account**: **[Sign up for free (no credit card for sandbox)](https://regolo.ai/)**
- **OpenAI-Compatible Client**: Works with existing codebases (Python/JS).
- **Model Choice**: Pick from Llama 3, Qwen, Mistral, or bring your own weights.

## **Step-by-Step (Code Blocks**)

### **1) Get Your API Key (EU Region)**

Create a key in the [Regolo](https://regolo.ai/) dashboard. This key routes all requests to our Milan-based green data centers.

### **2) **Configure the Client (Drop-in Replacement)****

Regolo speaks "OpenAI". You don't need to learn a new SDK. Just change the **base\_url**.

```
from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ.get("REGOLO_API_KEY"),
    base_url="https://api.regolo.ai/v1"  # EU-based endpoint
)Code language: Python (python)
```

### ****3) Run Inference with Green Metrics****

Execute a standard chat completion. In the response headers, Regolo provides unique telemetry.

```
response = client.chat.completions.create(
    model="Llama-3.1-70B-Instruct",
    messages=[{"role": "user", "content": "Explain the EU AI Act."}],
    stream=False
)

# Standard Output
print(response.choices[0].message.content)

# Green Telemetry (Hypothetical Header Access)
# print(response.headers['x-regolo-watts-consumed']) Code language: Python (python)
```

Expected output: High-speed text generation with zero data retention logs on the server side.

### **4) Fine-Tune Privately (Optional)**

Upload a dataset to fine-tune a model. The data is processed in an ephemeral container, the weights are yours, and the original data is wiped post-training.

```
# Upload training file (EU storage)
curl https://api.regolo.ai/v1/files -F file=@my_dataset.jsonl

# Start Fine-Tuning Job
curl https://api.regolo.ai/v1/fine_tuning/jobs \
  -d '{"training_file": "file-id", "model": "llama-3-8b"}'Code language: Bash (bash)
```

Expected output: A custom model ID deployable instantly on the same serverless infrastructure.

## **Production-Ready: Zero-Retention Architecture**

**Regolo** isn't just "compliant"; it's hostile to data leaks.

- **Ephemeral Containers**: Every request spins up/down isolated compute.
- **No " Improvement" Loop**: We explicitly disable the "training on customer data" loop that other providers enable by default.
- **Physical Location**: Seeweb data centers in Italy (Frosinone/Milan), subject to strict Italian/EU labor and privacy laws.

## **Benchmarks & Costs**

| **Feature** | **Regolo (Green Cloud)** | **US Hyperscalers** |
|---|---|---|
| **Location** | **Italy (EU)**. | US / Global Regions. |
| **Sustainability** | **Green Energy + Watt Reporting**.​ | Carbon offsets (opaque). |
| **Privacy** | **Zero Retention Default**. | "Opt-out" often required. |
| **Architecture** | **Serverless K8s**.​ | VM / Instance allocation. |
| **Cost** | **Pay-per-token/Watt**. | Pay-per-hour (often idle). |

## **👉[Try Regolo Green LLM Hosting](https://dashboard.regolo.ai)**

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## **Resources & Community**

**Official Documentation:**

- [Regolo Platform](https://regolo.ai) - European LLM provider, Zero Data-Retention and 100% Green

**Related Guides:**

- [Boost Your Workflows with Regolo AI on n8n](https://regolo.ai/boost-your-workflows-with-regolo-ai-on-n8n/)
- [Build Multi-Agent Workflows with crewAI Teams](https://regolo.ai/build-multi-agent-workflows-with-crewai-teams/)

**Join the Community:**

- [Regolo Discord](https://discord.gg/ZzZvuR2y) - Share your automation builds
- [CheshireCat GitHub](https://github.com/cheshire-cat-ai) - Contribute plugins
- Follow Us on X[ @regolo\_ai](https://x.com/regolo_ai) - Show your integrations!
- Open discussion on our[ Subreddit Community](https://www.reddit.com/r/regolo_ai/)

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## **🚀 Ready to Deploy?**

## [**Get Free Regolo Credits →**](https://dashboard.regolo.ai)

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> *Built with ❤️ by the Regolo team. Questions? support@regolo.ai*