Monitoring#

Ultralytics Platform provides monitoring for deployed endpoints. Track request metrics, view logs, and check health status with automatic polling.

Deployments Dashboard#

The Deploy page in the sidebar serves as the monitoring dashboard for all your deployments. It combines the world map, overview metrics, and deployment management in one view. See Dedicated Endpoints for creating and managing deployments.

graph TB
    subgraph Dashboard
        Map[World Map]:::proc --- Cards[Overview Cards]:::proc
        Cards --- List[Deployments List]:::decide
    end
    subgraph "Per Deployment"
        Metrics[Metrics Row]:::out
        Health[Health Check]:::out
        Logs[Logs Tab]:::out
        Code[Code Tab]:::out
        Predict[Predict Tab]:::out
    end
    List --> Metrics
    List --> Health
    List --> Logs
    List --> Code
    List --> Predict

    classDef proc fill:#2196F3,color:#fff
    classDef decide fill:#FF9800,color:#fff
    classDef out fill:#9C27B0,color:#fff

Overview Cards#

Four summary cards at the top of the page show:

World Map#

The interactive world map shows:

• Region pins for all 42 available regions
• Green pins for deployed regions
• Animated blue pins for regions with active deployments in progress
• Pin size varies based on deployment status and latency

Deployments List#

Below the overview cards, the deployments list shows all endpoints across your projects. Use the view mode toggle to switch between:

Per-Deployment Metrics#

Each deployment card (in cards view) shows real-time metrics:

Metrics Row#

Metrics are fetched from the sparkline API endpoint and refresh every 60 seconds.

Health Check#

Running deployments show a health check indicator:

Health checks auto-retry every 20 seconds when unhealthy. Click the refresh icon to manually trigger a health check. The health check uses a 55-second timeout to accommodate cold starts on scale-to-zero endpoints.

Logs#

Each deployment card includes a Logs tab for viewing recent log entries:

Log Entries#

Each log entry shows:

The UI shows the 20 most recent entries. The API defaults to 50 entries per request (max 200).

Code Examples#

Each deployment card includes a Code tab showing ready-to-use API code with your actual endpoint URL and API key:

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())

Deployment Predict#

The Predict tab on each deployment card provides an inline predict panel — the same interface as the model's Predict tab, but running inference through the deployment endpoint instead of the shared service. This is useful for testing a deployed endpoint directly from the browser. See Inference for parameter details and response formats.

API Endpoints#

Monitoring Overview#

GET /api/monitoring

Returns aggregated metrics for all deployments owned by the authenticated user. Workspace-aware via optional owner query parameter.

Deployment Metrics#

GET /api/deployments/{deploymentId}/metrics?sparkline=true&range=24h

Returns sparkline data and summary metrics for a specific deployment. Refresh interval: 60 seconds.

Deployment Logs#

GET /api/deployments/{deploymentId}/logs?limit=50&severity=ERROR,WARNING

Returns recent log entries with optional severity filter and pagination.

Deployment Health#

GET /api/deployments/{deploymentId}/health

Returns health check status with response latency.

{
    "healthy": true,
    "status": 200,
    "latencyMs": 142,
    "serverTiming": { "db": 8, "ping": 142, "total": 150 }
}

Performance Optimization#

Use monitoring data to optimize your deployments:

FAQ#