Quickstart - OverControl

In this guide, you will create an API key, configure your environment, choose a model, and send your first request.

Already using an OpenAI-compatible client or developer tool? OverControl supports compatible provider configurations. See Integrations after completing this quickstart.

Before you start

You need:

an OverControl account;
an OverControl API key;
API credit available for your requests;
a terminal with curl.

Every new OverControl API account includes free quota, so you can make your first requests without adding credit.

Open the API dashboard

Create an API key, manage your API access, and review usage from the OverControl dashboard.

Step 1: Create an API key

Open the OverControl dashboard and create a new API key. Copy the key when it is created and store it somewhere secure. Your application will use this key to authenticate requests to OverControl.

Treat your API key like a password. Never commit it to source control, expose it in client-side code, or share it in screenshots, public chats, or support messages.

Step 2: Configure your environment

Store your API key in an environment variable instead of placing it directly in your application code.

export OVERCONTROL_API_KEY="your_api_key_here"

Set the OverControl Base URL used by your API account:

export OVERCONTROL_BASE_URL="<OVERCONTROL_BASE_URL>"

Your application can now read these values without hard-coding credentials into the source code.

Use a secrets manager or your deployment platform’s encrypted environment variables when moving from local development to production.

Step 3: Choose a model

For your first request, use:

lume-3

lume-3 is a strong starting point for general text generation, coding, analysis, and application workloads. Other models are available for workloads that need different capabilities or performance characteristics.

Step 4: Make your first request

OverControl supports an OpenAI-compatible API configuration. Send a request using your API key, Base URL, and model ID.

curl "$OVERCONTROL_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $OVERCONTROL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "lume-3",
    "messages": [
      {
        "role": "user",
        "content": "Explain what an API is in one sentence."
      }
    ]
  }'

A successful request returns a model response in JSON. The generated text is available in the assistant message returned by the API.

OverControl processes API content through European infrastructure. Prompts and responses are not retained as conversation history or reused for model training. See Privacy and Security for details about the request lifecycle.

Step 5: Try another prompt

Once the first request works, change the message content and send another request. For example:

{
  "role": "user",
  "content": "Give me three names for a developer tool that monitors API latency."
}

You can use the same request pattern for tasks such as:

text generation;
summarization;
information extraction;
coding assistance;
classification;
document processing workflows;
reasoning and analysis.

Your application controls the context sent with each request.

Use another model

Change the model value to route a request to another OverControl model. For example:

{
  "model": "lume-3-max"
}

Use the Models guide to compare models before selecting one for a production workload.

Choose models per workload rather than using the largest model for every request. This lets you balance capability, latency, and cost without changing the rest of your integration.

Use an OpenAI-compatible client

If your application already uses an OpenAI-compatible provider, you can connect it to OverControl by changing the provider configuration. You typically need four values:

Setting	Value
Provider	OpenAI compatible
Base URL	Your OverControl Base URL
API key	Your OverControl API key
Model	An OverControl model ID such as lume-3

This makes it possible to bring OverControl into existing applications and supported developer tools without rebuilding the surrounding workflow. Explore Integrations

Before going to production

Your first request proves that the connection works. Before using OverControl in a production application:

keep API keys on the server side;
store secrets outside your source code;
choose the appropriate model for the workload;
review current model pricing;
define application-level timeouts and error handling;
review how sensitive data is processed;
monitor API usage and spending.

Models

Compare models and choose the right one for your workload.

Privacy and security

Understand how API requests and sensitive data are handled.

Integrations

Connect OverControl to supported clients and developer tools.

Pricing

Review current input, cached input, and output token pricing.

Next steps

You have now completed the basic OverControl API flow:

Choose the right model

Compare model capabilities, context windows, and pricing for your workload.Explore models

Understand privacy and security

Review how requests are processed, protected, and removed after processing.Read privacy and security

Connect your existing tools

Configure supported developer tools and OpenAI-compatible clients to use OverControl.Explore integrations

​Before you start

Open the API dashboard

​Step 1: Create an API key

​Step 2: Configure your environment

​Step 3: Choose a model

​Step 4: Make your first request

​Step 5: Try another prompt

​Use another model

​Use an OpenAI-compatible client

​Before going to production

Models

Privacy and security

Integrations

Pricing

​Next steps

Before you start

Step 1: Create an API key

Step 2: Configure your environment

Step 3: Choose a model

Step 4: Make your first request

Step 5: Try another prompt

Use another model

Use an OpenAI-compatible client

Before going to production

Next steps