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---
comments: true
description: Deploy YOLO models to dedicated endpoints in 43 global regions with auto-scaling and monitoring on Ultralytics Platform.
keywords: Ultralytics Platform, deployment, endpoints, YOLO, production, scaling, global regions
---
# Dedicated Endpoints
[Ultralytics Platform](https://platform.ultralytics.com) enables deployment of YOLO models to dedicated endpoints in 43 global regions. Each endpoint is a single-tenant service with auto-scaling, a unique endpoint URL, and independent monitoring.
![Ultralytics Platform Model Deploy Tab With Region Map And Table](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/model-deploy-tab-with-region-map-and-table.avif)
## Create Endpoint
### From the Deploy Tab
Deploy a model from its `Deploy` tab:
1. Navigate to your model
2. Click the **Deploy** tab
3. Select a region from the region table (sorted by latency from your location)
4. Click **Deploy** on the region row
The deployment name is auto-generated from the model name and region city (e.g., `yolo11n-iowa`).
### From the Deployments Page
Create a deployment from the global `Deploy` page in the sidebar:
1. Click **New Deployment**
2. Select a model from the model selector
3. Select a region from the map or table
4. Optionally customize the deployment name and resources
5. Click **Deploy Model**
![Ultralytics Platform New Deployment Dialog With Model Selector And Region Map](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/new-deployment-dialog-with-model-selector-and-region-map.avif)
### Deployment Lifecycle
```mermaid
stateDiagram-v2
[*] --> Creating: Deploy
Creating --> Deploying: Container starting
Deploying --> Ready: Health check passed
Ready --> Stopping: Stop
Stopping --> Stopped: Stopped
Stopped --> Ready: Start
Ready --> [*]: Delete
Stopped --> [*]: Delete
Creating --> Failed: Error
Deploying --> Failed: Error
Failed --> [*]: Delete
```
### Region Selection
Choose from 43 regions worldwide. The interactive region map and table show:
- **Region pins**: Color-coded by latency (green < 100ms, yellow < 200ms, red > 200ms)
- **Deployed regions**: Highlighted with a "Deployed" badge
- **Deploying regions**: Animated pulse indicator
- **Bidirectional highlighting**: Hover on the map highlights the table row, and vice versa
![Ultralytics Platform Deploy Tab Region Latency Table Sorted By Latency](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/deploy-tab-region-latency-table-sorted-by-latency.avif)
The region table on the model `Deploy` tab includes:
| Column | Description |
| ------------ | ---------------------------------------- |
| **Location** | City and country with flag icon |
| **Zone** | Region identifier |
| **Latency** | Measured ping time (median of 3 pings) |
| **Distance** | Distance from your location in km |
| **Actions** | Deploy button or "Deployed" status badge |
!!! note "New Deployment Dialog"
The `New Deployment` dialog (from the global `Deploy` page) shows a simpler region table with only Location, Latency, and Select columns.
!!! tip "Choose Wisely"
Select the region closest to your users for lowest latency. Use the **Rescan** button to re-measure latency from your current location.
## Available Regions
=== "Americas (14)"
| Zone | Location |
| ----------------------- | ---------------------- |
| us-central1 | Iowa, USA |
| us-east1 | South Carolina, USA |
| us-east4 | Northern Virginia, USA |
| us-east5 | Columbus, USA |
| us-south1 | Dallas, USA |
| us-west1 | Oregon, USA |
| us-west2 | Los Angeles, USA |
| us-west3 | Salt Lake City, USA |
| us-west4 | Las Vegas, USA |
| northamerica-northeast1 | Montreal, Canada |
| northamerica-northeast2 | Toronto, Canada |
| northamerica-south1 | Queretaro, Mexico |
| southamerica-east1 | Sao Paulo, Brazil |
| southamerica-west1 | Santiago, Chile |
=== "Europe (13)"
| Zone | Location |
| ----------------- | ---------------------- |
| europe-west1 | St. Ghislain, Belgium |
| europe-west2 | London, UK |
| europe-west3 | Frankfurt, Germany |
| europe-west4 | Eemshaven, Netherlands |
| europe-west6 | Zurich, Switzerland |
| europe-west8 | Milan, Italy |
| europe-west9 | Paris, France |
| europe-west10 | Berlin, Germany |
| europe-west12 | Turin, Italy |
| europe-north1 | Hamina, Finland |
| europe-north2 | Stockholm, Sweden |
| europe-central2 | Warsaw, Poland |
| europe-southwest1 | Madrid, Spain |
=== "Asia-Pacific (12)"
| Zone | Location |
| -------------------- | ---------------------- |
| asia-east1 | Changhua, Taiwan |
| asia-east2 | Kowloon, Hong Kong |
| asia-northeast1 | Tokyo, Japan |
| asia-northeast2 | Osaka, Japan |
| asia-northeast3 | Seoul, South Korea |
| asia-south1 | Mumbai, India |
| asia-south2 | Delhi, India |
| asia-southeast1 | Jurong West, Singapore |
| asia-southeast2 | Jakarta, Indonesia |
| asia-southeast3 | Bangkok, Thailand |
| australia-southeast1 | Sydney, Australia |
| australia-southeast2 | Melbourne, Australia |
=== "Middle East & Africa (4)"
| Zone | Location |
| ------------- | -------------------------- |
| africa-south1 | Johannesburg, South Africa |
| me-central1 | Doha, Qatar |
| me-central2 | Dammam, Saudi Arabia |
| me-west1 | Tel Aviv, Israel |
## Endpoint Configuration
### New Deployment Dialog
The `New Deployment` dialog provides:
| Setting | Description | Default |
| ------------------- | ---------------------------- | ------- |
| **Model** | Select from completed models | - |
| **Region** | Deployment region | - |
| **Deployment Name** | Auto-generated, editable | - |
| **CPU Cores** | CPU allocation (1-8) | 1 |
| **Memory (GB)** | Memory allocation (1-32 GB) | 2 |
![Ultralytics Platform New Deployment Dialog Resources Panel Expanded](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/new-deployment-dialog-resources-panel-expanded.avif)
Resource settings are available under the collapsible **Resources** section. Deployments use scale-to-zero by default (min instances = 0, max instances = 1) — you only pay for active inference time.
!!! note "Auto-Generated Names"
The deployment name is automatically generated from the model name and region city (e.g., `yolo11n-iowa`). If you deploy the same model to the same region again, a numeric suffix is added (e.g., `yolo11n-iowa-2`).
### Deploy Tab (Quick Deploy)
When deploying from the model's `Deploy` tab, endpoints are created with default resources (1 CPU, 2 GB memory) with scale-to-zero enabled. The deployment name is auto-generated.
## Manage Endpoints
### View Modes
The deployments list supports three view modes:
| Mode | Description |
| ----------- | --------------------------------------------------------- |
| **Cards** | Full detail cards with logs, code examples, predict panel |
| **Compact** | Grid of smaller cards with key metrics |
| **Table** | DataTable with sortable columns and search |
![Ultralytics Platform Deploy Tab Active Deployments Cards View](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/deploy-tab-active-deployments-cards-view.avif)
### Deployment Card (Cards View)
Each deployment card in the cards view shows:
- **Header**: Name, region flag, status badge, start/stop/delete buttons
- **Endpoint URL**: Copyable URL with link to API docs
- **Metrics**: Request count (24h), P95 latency, error rate
- **Health check**: Live health indicator with latency and manual refresh
- **Tabs**: `Logs`, `Code`, and `Predict`
The `Logs` tab shows recent log entries with severity filtering (All / Errors). The `Code` tab shows ready-to-use code examples in Python, JavaScript, and cURL with your actual endpoint URL and API key. The `Predict` tab provides an inline predict panel for testing directly on the deployment.
### Deployment Statuses
| Status | Description |
| ------------- | --------------------------------------- |
| **Creating** | Deployment is being set up |
| **Deploying** | Container is starting |
| **Ready** | Endpoint is live and accepting requests |
| **Stopping** | Endpoint is shutting down |
| **Stopped** | Endpoint is paused (no billing) |
| **Failed** | Deployment failed (see error message) |
### Endpoint URL
Each endpoint has a unique URL, for example:
```
https://predict-abc123.run.app
```
![Ultralytics Platform Deployment Card Endpoint Url With Copy Button](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/deployment-card-endpoint-url-with-copy-button.avif)
Click the copy button to copy the URL. Click the docs icon to view the auto-generated API documentation for the endpoint.
## Lifecycle Management
Control your endpoint state:
```mermaid
graph LR
R[Ready] -->|Stop| S[Stopped]
S -->|Start| R
R -->|Delete| D[Deleted]
S -->|Delete| D
style R fill:#4CAF50,color:#fff
style S fill:#9E9E9E,color:#fff
style D fill:#F44336,color:#fff
```
| Action | Description |
| ---------- | ------------------------------- |
| **Start** | Resume a stopped endpoint |
| **Stop** | Pause the endpoint (no billing) |
| **Delete** | Permanently remove endpoint |
### Stop Endpoint
Stop an endpoint to pause billing:
1. Click the pause icon on the deployment card
2. Endpoint status changes to "Stopping" then "Stopped"
Stopped endpoints:
- Don't accept requests
- Don't incur charges
- Can be restarted anytime
### Delete Endpoint
Permanently remove an endpoint:
1. Click the delete (trash) icon on the deployment card
2. Confirm deletion in the dialog
!!! warning "Permanent Action"
Deletion is immediate and permanent. You can always create a new endpoint.
## Using Endpoints
### Authentication
Each deployment is created with an API key from your account. Include it in requests:
```bash
Authorization: Bearer YOUR_API_KEY
```
The API key prefix is displayed on the deployment card footer for identification. Generate keys from [API Keys](../account/api-keys.md).
### No Rate Limits
Dedicated endpoints are **not subject to the Platform API rate limits**. Requests go directly to your dedicated service, so throughput is limited only by your endpoint's CPU, memory, and scaling configuration. This is a key advantage over [shared inference](inference.md), which is rate-limited to 20 requests/min per API key.
### Request Example
=== "Python"
```python
import requests
# Deployment endpoint
url = "https://predict-abc123.run.app/predict"
# Headers with your deployment API key
headers = {"Authorization": "Bearer YOUR_API_KEY"}
# Inference parameters
data = {"conf": 0.25, "iou": 0.7, "imgsz": 640}
# Send image for inference
with open("image.jpg", "rb") as f:
response = requests.post(url, headers=headers, data=data, files={"file": f})
print(response.json())
```
=== "JavaScript"
```javascript
// Build form data with image and parameters
const formData = new FormData();
formData.append("file", fileInput.files[0]);
formData.append("conf", "0.25");
formData.append("iou", "0.7");
formData.append("imgsz", "640");
// Send image for inference
const response = await fetch(
"https://predict-abc123.run.app/predict",
{
method: "POST",
headers: { Authorization: "Bearer YOUR_API_KEY" },
body: formData,
}
);
const result = await response.json();
console.log(result);
```
=== "cURL"
```bash
curl -X POST \
"https://predict-abc123.run.app/predict" \
-H "Authorization: Bearer YOUR_API_KEY" \
-F "file=@image.jpg" \
-F "conf=0.25" \
-F "iou=0.7" \
-F "imgsz=640"
```
### Request Parameters
| Parameter | Type | Default | Description |
| ----------- | ------ | ------- | ----------------------------- |
| `file` | file | - | Image file (required) |
| `conf` | float | 0.25 | Minimum confidence threshold |
| `iou` | float | 0.7 | NMS IoU threshold |
| `imgsz` | int | 640 | Input image size |
| `normalize` | string | - | Return normalized coordinates |
### Response Format
Same as [shared inference](inference.md#response) with task-specific fields.
## Pricing
Dedicated endpoints bill based on:
| Component | Rate |
| ------------ | -------------------- |
| **CPU** | Per vCPU-second |
| **Memory** | Per GB-second |
| **Requests** | Per million requests |
!!! tip "Cost Optimization"
- Use scale-to-zero for development endpoints
- Set appropriate max instances
- Monitor usage in the [Monitoring](monitoring.md) dashboard
- Review costs in [Settings > Billing](../account/billing.md)
## FAQ
### How many endpoints can I create?
Endpoint limits depend on plan:
- **Free**: Up to 3 deployments
- **Pro**: Up to 10 deployments
- **Enterprise**: Unlimited deployments
Each model can still be deployed to multiple regions within your plan quota.
### Can I change the region after deployment?
No, regions are fixed. To change regions:
1. Delete the existing endpoint
2. Create a new endpoint in the desired region
### How do I handle multi-region deployment?
For global coverage:
1. Deploy to multiple regions
2. Use a load balancer or DNS routing
3. Route users to the nearest endpoint
### What's the cold start time?
Cold start time depends on model size and whether the container is already cached in the region. Typical ranges:
| Scenario | Cold Start |
| ------------------- | -------------- |
| Cached container | ~5-15 seconds |
| First deploy/region | ~15-45 seconds |
The health check uses a 55-second timeout to accommodate worst-case cold starts.
### Can I use custom domains?
Custom domains are coming soon. Currently, endpoints use platform-generated URLs.