> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hivenet.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Get started with Hivenet Inference API

> Deploy a managed model endpoint, create an Inference API key, and send your first OpenAI-compatible Chat Completions request from the Hivenet Compute console.

This guide takes you from the Compute console to a working inference request. You’ll choose where your models run, select a model and serving variant, set replica capacity, deploy the endpoint, and call it with an Inference API key.

<Info>
  ## What you’ll need

  * A Compute account
  * Access to a Compute organization
  * Enough credits in the organization balance for the endpoint you want to run
  * Python, Node.js, or `curl` if you want to test the endpoint locally

  Inference API uses the same organization balance as your Compute resources.
</Info>

<Steps>
  <Step title="Open Inference API">
    1. Sign in to the [Compute console](https://console.hivecompute.ai/).
    2. Confirm the organization shown in the upper-left corner.
    3. Open **Inference API** from the left sidebar.
    4. Select **New deployment**.

    The endpoint and any API keys you create belong to the active organization.
  </Step>

  <Step title="Choose a location">
    Select where your Inference API models should run.

    The location selector shows the currently available regions and marks unavailable ones **Sold out**.

    <Warning>
      The location you choose currently applies across your Inference API models. Per-model location selection is not available yet.
    </Warning>

    If you choose **Global**, Hivenet assigns an available region. After deployment, open the endpoint details to see where it was placed.

    Read the data-handling notice shown for the selected location before continuing.
  </Step>

  <Step title="Choose a model and variant">
    Select a model from the catalog. Use **All models** if you want to browse beyond the initial set shown on the page.

    Model cards can include a short description, use-case tags, and a starting hourly price. After selecting a model, choose one of its available variants.

    A variant can show:

    * GPU and quantization
    * Context window
    * Approximate request capacity
    * Time to first token
    * Inter-token latency
    * Output speed
    * License
    * Current price

    <Info>
      The live variant card is the source of truth for the serving configuration and price you are about to deploy.
    </Info>
  </Step>

  <Step title="Choose the replica count">
    Choose how many replicas you want to keep running.

    A replica is one serving copy of the selected model variant. More replicas provide more request capacity and increase hourly cost.

    The current production flow supports a fixed replica count of up to four, subject to available capacity.

    <Note>
      Autoscaling is not available yet. You can change the replica count later when capacity is available.
    </Note>
  </Step>

  <Step title="Name your endpoint">
    Keep the suggested name or enter your own using lowercase letters, numbers, and hyphens.

    The endpoint name helps you identify the resource in the console. It is not necessarily the model value your application sends in API requests.
  </Step>

  <Step title="Review and deploy">
    In **Review your setup**, check the selected model, location, replicas, and estimated cost.

    The console shows:

    * Total cost per hour
    * Estimated cost per 24 hours
    * Estimated cost per week

    Select **Deploy model** when you’re ready.

    The endpoint enters **Starting** while Hivenet prepares it. The console says this takes about 90 seconds. Charges begin once the endpoint is **Running**.
  </Step>

  <Step title="Create an API key">
    You need an active Inference API key to call the endpoint.

    If your organization does not already have one you want to use, select **Create API key** from the endpoint flow when it is offered, or select **New key** in the **API keys** section.

    1. Enter a unique key label using lowercase letters, numbers, and hyphens.
    2. Select **Create**.
    3. Copy the full key and store it securely.
    4. Select **Copy and close** only after you have saved it.

    <Warning>
      The full API key is shown once. You won’t be able to view it again after closing the dialog.
    </Warning>
  </Step>

  <Step title="Send your first request">
    Open the endpoint details once its status is **Running**. The **Quick-start snippets** contain the endpoint URL and the exact model value expected by that endpoint.

    <Warning>
      Copy the endpoint URL exactly as shown. It already includes the API version path, such as `/v1`.
    </Warning>

    For Python, install the OpenAI SDK:

    ```bash theme={null}
    pip install openai
    ```

    Then use the values shown in your endpoint details:

    ```python theme={null}
    from openai import OpenAI

    client = OpenAI(
        base_url="YOUR_ENDPOINT_URL",
        api_key="YOUR_INFERENCE_API_KEY",
    )

    response = client.chat.completions.create(
        model="YOUR_SERVED_MODEL_NAME",
        messages=[
            {"role": "user", "content": "Hello!"}
        ],
    )

    print(response.choices[0].message.content)
    ```

    <Tip>
      Copy the exact `model` value from the generated snippet. Do not substitute your endpoint name or assume the catalog title is identical.
    </Tip>

    For JavaScript and `curl` examples, see [Call your Inference API endpoint](/documentation/inference-api/call-your-endpoint).
  </Step>
</Steps>

## When you’re done testing

A running endpoint continues using organization credits even when you are not sending requests.

* **Stop** it if you want to pause serving and start the same endpoint again later.
* **Terminate** it only when you no longer need the endpoint. Termination is permanent.
* **Clear** removes an already-terminated entry from the Deployments list; it does not terminate a running endpoint.

See [Manage an Inference API endpoint](/documentation/inference-api/manage-deployments) for the full lifecycle.

## If something goes wrong

* **Location is Sold out:** Choose another available location or try again later.
* **Endpoint remains Starting:** Give provisioning about 90 seconds before treating it as a problem.
* **Authentication fails:** Confirm you are using an active Inference API key from the same organization as the endpoint.
* **Model is not found:** Copy the exact `model` value from the endpoint’s generated quick-start snippet.
* **Not enough credits:** Ask the organization Owner to add credits if you cannot add them yourself.

For more cases, see [Troubleshoot Hivenet Inference API](/documentation/inference-api/troubleshooting).

## Next steps

* [Choose a model and variant](/documentation/inference-api/models-and-variants).
* [Call your endpoint](/documentation/inference-api/call-your-endpoint).
* [Manage replicas and capacity](/documentation/inference-api/replicas-and-capacity).
* [Understand Inference API billing](/documentation/inference-api/billing).