Dedicated Endpoints#

Ultralytics Platform enables deployment of YOLO models to dedicated endpoints in 42 global regions. Each endpoint is a single-tenant service with scale-to-zero behavior, a unique endpoint URL, and independent monitoring.

Create Endpoint#

From the Deploy Tab#

Deploy a model from its Deploy tab:

1. Navigate to your model
2. Click the Deploy tab
3. Review the world map and the region table, which is sorted by measured latency from your location
4. Click Deploy in the region row you want to use

The deployment name is auto-generated from the model name and region city (e.g., yolo26n-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. Review the editable, auto-generated deployment name and the fixed resource defaults
5. Click Deploy Model

Deployment Lifecycle#

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

    classDef proc fill:#2196F3,color:#fff
    classDef out fill:#9C27B0,color:#fff
    classDef error fill:#F44336,color:#fff
    classDef extern fill:#607D8B,color:#fff
    class Creating,Deploying,Stopping proc
    class Ready out
    class Failed error
    class Stopped extern

Connect Slack alerts to receive a message when a deployment becomes ready or fails to start.

Region Selection#

Choose from 42 regions worldwide. The interactive region map and table show:

• Region pins: Color-coded by latency on a green-to-red gradient (faster regions are greener, slower regions are redder)
• 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

The region table on the model Deploy tab includes:

Available Regions#

Endpoint Configuration#

New Deployment Dialog#

The New Deployment dialog provides:

The disabled Resources panel is marked Coming Soon and cannot currently be expanded or customized. Deployments use 1 CPU, 2 GiB memory, minInstances = 0, and maxInstances = 1.

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:

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#

Endpoint URL#

Each endpoint has a unique URL, for example:

https://predict-abc123.run.app

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:

graph LR
    R[Ready]:::out -->|Stop| S[Stopped]:::extern
    S -->|Start| R
    R -->|Delete| D[Deleted]:::error
    S -->|Delete| D

    classDef out fill:#9C27B0,color:#fff
    classDef error fill:#F44336,color:#fff
    classDef extern fill:#607D8B,color:#fff

Stop Endpoint#

Stop an endpoint when you do not want it to accept requests:

1. Click the pause icon on the deployment card
2. Endpoint status changes to "Stopping" then "Stopped"

Stopped endpoints:

• Don't accept requests
• 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

Using Endpoints#

Authentication#

Each deployment is created with an API key from your account. Include it in requests:

Authorization: Bearer YOUR_API_KEY

The API key prefix is displayed on the deployment card footer for identification. Generate keys from API Keys.

Direct Endpoint Requests#

Send production requests directly to the URL shown on the deployment card. These requests do not pass through the Platform API rate limiter. The endpoint currently runs one instance with the fixed resources described above.

Request Example#

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())

Request Parameters#

Response Format#

Same as shared inference with task-specific fields.

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 the model and whether the endpoint has scaled to zero. Platform's health-check request allows up to 55 seconds so an idle endpoint has time to start.

Each deployment currently uses the generated endpoint URL shown on its deployment card.