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.

note

If no model has been installed for your endpoint yet, this list can be empty. In that case, contact us to complete model provisioning before sending inference requests.

Sending your first request

curl

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."}
    ]
  }'

Python

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)

JavaScript / TypeScript

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.

curl

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."}
    ]
  }'

Python

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)

JavaScript / TypeScript

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 ?? "");
}

note

If a streaming request is interrupted mid-response (for example, a network timeout or a server restart), the connection closes rather than returning an HTTP error code. The HTTP 200 is written when the stream starts, so a mid-stream failure looks like a connection reset on the client side. Handle this by detecting an incomplete stream and retrying the request.

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.