Get started with Hivenet Inference API

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.

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.

Open Inference API

Sign in to the Compute console.
Confirm the organization shown in the upper-left corner.
Open Inference API from the left sidebar.
Select New deployment.

The endpoint and any API keys you create belong to the active organization.

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.

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

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.

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

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

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.

Autoscaling is not available yet. You can change the replica count later when capacity is available.

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.

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.

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.

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

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

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.

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

For Python, install the OpenAI SDK:

pip install openai

Then use the values shown in your endpoint details:

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)

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

For JavaScript and curl examples, see Call your Inference API endpoint.

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 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.

Get Started

Essentials

Inference API

Organizations

Connectivity

File transfer

Troubleshooting

Account and billing

Reference

Get started with Hivenet Inference API

What you’ll need

When you’re done testing

If something goes wrong

Next steps

​What you’ll need

​When you’re done testing

​If something goes wrong

​Next steps

What you’ll need

When you’re done testing

If something goes wrong

Next steps