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Getting started with Dedicated AI Hosting

‍

After we set up your dedicated instance, you will receive:

  • API base URL - your dedicated HTTPS endpoint, e.g. https://your-company.llm.aihosting.mittwald.de
  • API key - a bearer token that authenticates your requests

Keep your API key confidential. Store it in an environment variable or secrets manager — never hardcode it in source files or commit it to version control. If a key is exposed, contact us to rotate it.

Checking available models​

user@local $ curl https://your-company.llm.aihosting.mittwald.de/v1/models \
  -H "Authorization: Bearer YOUR_API_KEY"

Use one of the returned model IDs as YOUR_MODEL_ID in requests.

Sending your first request​

user@local $ curl https://your-company.llm.aihosting.mittwald.de/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "YOUR_MODEL_ID",
    "messages": [
    {"role": "user", "content": "Explain retrieval-augmented generation in two sentences."}
    ]
  }'
user@local $ pip install openai python-dotenv
OPENAI_API_KEY=YOUR_API_KEY
OPENAI_BASE_URL=https://your-company.llm.aihosting.mittwald.de/v1
from openai import OpenAI
from dotenv import load_dotenv

load_dotenv()

client = OpenAI()

response = client.chat.completions.create(
    model="YOUR_MODEL_ID",
    messages=[
        {"role": "user", "content": "Explain retrieval-augmented generation in two sentences."}
    ]
)

print(response.choices[0].message.content)
user@local $ npm install openai dotenv
OPENAI_API_KEY=YOUR_API_KEY
OPENAI_BASE_URL=https://your-company.llm.aihosting.mittwald.de/v1
import OpenAI from "openai";
import "dotenv/config";

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: process.env.OPENAI_BASE_URL,
});

const response = await client.chat.completions.create({
  model: "YOUR_MODEL_ID",
  messages: [
    {
      role: "user",
      content: "Explain retrieval-augmented generation in two sentences.",
    },
  ],
});

console.log(response.choices[0].message.content);

Streaming responses​

Add "stream": true to receive tokens as they are generated instead of waiting for the full response.

user@local $ curl https://your-company.llm.aihosting.mittwald.de/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "YOUR_MODEL_ID",
    "stream": true,
    "messages": [
      {"role": "user", "content": "Explain retrieval-augmented generation in two sentences."}
    ]
  }'
from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://your-company.llm.aihosting.mittwald.de/v1")

with client.chat.completions.stream(
    model="YOUR_MODEL_ID",
    messages=[{"role": "user", "content": "Explain retrieval-augmented generation in two sentences."}],
) as stream:
    for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://your-company.llm.aihosting.mittwald.de/v1",
});

const stream = await client.chat.completions.create({
  model: "YOUR_MODEL_ID",
  stream: true,
  messages: [
    {
      role: "user",
      content: "Explain retrieval-augmented generation in two sentences.",
    },
  ],
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

Request parameters​

Parameter recommendations can be model-specific. Use the defaults from your chosen SDK first, then tune based on your model behavior and use case.

Drop-in replacement​

Because the endpoint is OpenAI-compatible, you can use it as a drop-in replacement in frameworks that accept a custom base URL. See OpenAI API compatibility‍ for the full list of supported endpoints and parameters, including tool calling and structured outputs.

Managing multiple API keys​

If you want separate keys per app/team, usage tracking, or per-key rate limits, run LiteLLM‍ as a self-hosted proxy.

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