Getting started with Serverless AI endpoints: Deploy an nginx server with authentication

Serverless AI lets you deploy and manage endpoints without handling infrastructure yourself. This quickstart shows how to validate basic endpoint functionality with minimal setup. In the web console, Quick start Serverless endpoint deploys a ready-made example with preconfigured settings.

Prerequisites

Web console
CLI

Make sure you are in a group that has at least the editor role within your tenant or project; for example, the default editors group. You can check this in the Administration → IAM section of the web console.
In the Administration → Limits → Quotas section, check the following quotas:
- Number of virtual machines (VMs) under Compute: make sure that at least one VM is available.
- Total number of allocations under Virtual Private Cloud: make sure that at least one allocation is available.
If necessary, increase the quotas.

Make sure you are in a group that has at least the editor role within your tenant or project; for example, the default editors group. You can check this in the Administration → IAM section of the web console.
In the Administration → Limits → Quotas section, check the following quotas:
- Number of virtual machines (VMs) under Compute: make sure that at least one VM is available.
- Total number of allocations under Virtual Private Cloud: make sure that at least one allocation is available.
If necessary, increase the quotas.
Install and configure the Nebius AI Cloud CLI. Check that your project ID is saved in the Nebius AI Cloud CLI profile configuration:
```
cat ~/.nebius/config.yaml
```
Install jq to parse JSON output:
sudo apt-get install jq

Steps

Create an endpoint

Web console
CLI

In the sidebar, go to Serverless AI → Endpoints.
Click Quick start Serverless endpoint. The flow uses a preconfigured container image and default settings.
Click Create endpoint.

Wait for the endpoint to start running.

Use nebius ai create to create a step by step in the terminal. The command prompts you to enter values to pass as parameters, shows the resulting configuration and then creates the .

Create a token for authorization and save it to an environment variable:
```
export AUTH_TOKEN=$(openssl rand -hex 32)
```
Use this token in the --token parameter when you create the endpoint. You will also need it later to test access to the endpoint.
Run the following command:
```
nebius ai endpoint create \
  --name qs-endpoint-nginx \
  --image nginx:alpine \
  --platform cpu-d3 \
  --preset 4vcpu-16gb \
  --public \
  --container-port 80 \
  --auth token \
  --token "$AUTH_TOKEN" \
  --subnet-id <subnet_ID>
```
Alternatively, you can run nebius ai create and specify each value step by step with CLI prompts. The command pre-fills the following values:
- --name: Endpoint name.
- --image: Container image to run. In the given example, the nginx:alpine image is used.
- --platform: VM platform for the endpoint. As Serverless AI endpoints are based on containers over VMs, every endpoint uses Compute platforms and presets.
- --preset: Number of vCPUs and RAM allocated to the container. The preset must match the selected platform.
- --container-port: Port on which the application listens in the container. Each HTTP port is available through the endpoint’s managed HTTPS URL.
- --auth: Authentication method for the endpoint.
- --token: Token used for endpoint authentication.
- --public: Assigns a public IP address to the endpoint. Not required to reach the endpoint from the internet, because each HTTP port is available through the endpoint’s managed HTTPS URL.
When the CLI prompts you to select a project, region or subnet, select from the values that are available.
Review the resulting configuration, then confirm creation.

Wait for the endpoint to start running.

Test the endpoint

Web console
CLI

In the sidebar, go to Serverless AI → Endpoints.
Click the endpoint name to open its details. Wait until the endpoint is running. Then copy its managed HTTPS URL (https://...) from the Public endpoints field.
In a terminal, send a request to the endpoint:
```
curl -v <endpoint_URL>
```
In the command, specify the managed HTTPS URL that you copied earlier. The response should have HTTP status 200 OK. The body is the default nginx welcome page, which confirms that the endpoint is serving traffic over the managed HTTPS URL.

Save the endpoint ID to an environment variable:

export ENDPOINT_ID=$(nebius ai endpoint get-by-name \
  --name qs-endpoint-nginx --format jsonpath='{.metadata.id}')

Get the endpoint’s managed HTTPS URL from the Public endpoints field:

export ENDPOINT_URL=$(nebius ai endpoint get "$ENDPOINT_ID" --format json \
  | jq -r '.status.public_endpoints[] | select(startswith("https://"))' | head -1)

Test the endpoint with authentication:

curl -v "$ENDPOINT_URL" -H "Authorization: Bearer $AUTH_TOKEN"

Test the endpoint without a token:
```
curl -v "$ENDPOINT_URL"
```
The test should fail with the error 401 Unauthorized or 403 Forbidden.

View logs

Web console
CLI

In the sidebar, go to Serverless AI → Endpoints.
In the endpoint list, next to the endpoint, click View logs. Alternatively, click → View logs.

View endpoint logs:

nebius ai endpoint logs $ENDPOINT_ID

Delete the endpoint

If you no longer need the endpoint, delete it. Once you delete the endpoint, you will not be charged for it.

Web console
CLI

In the sidebar, go to Serverless AI → Endpoints.
In the endpoint list, find the endpoint and click → Delete.
Confirm the endpoint deletion.

nebius ai endpoint delete $ENDPOINT_ID

Expected results

The endpoint is reachable over its managed HTTPS URL.
A request without a token in the web console returns 200 OK.
A request without a token in the CLI returns 401 Unauthorized or 403 Forbidden.
An endpoint can be deleted successfully.

​Prerequisites

​Steps

​Create an endpoint

​Test the endpoint

​View logs

​Delete the endpoint

​Expected results

​See also