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Cloud LLM Hosting: Scalable, Private, and Green Infrastructure

Curated byChiara Passarelli·Fresh as of
Cloud LLM Hosting

👉Try Regolo Green LLM Hosting

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 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)

Step-by-Step (Code Blocks)

1) Get Your API Key (EU Region)

Create a key in the Regolo 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

FeatureRegolo (Green Cloud)US Hyperscalers
LocationItaly (EU).US / Global Regions.
SustainabilityGreen Energy + Watt Reporting.​Carbon offsets (opaque).
PrivacyZero Retention Default.“Opt-out” often required.
ArchitectureServerless K8s.​VM / Instance allocation.
CostPay-per-token/Watt.Pay-per-hour (often idle).

👉Try Regolo Green LLM Hosting

Resources & Community

Official Documentation:

  • Regolo Platform – European LLM provider, Zero Data-Retention and 100% Green

Related Guides:

Join the Community:

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