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

The error rate card highlights in red when the rate exceeds 5%. Check the `Logs` tab on individual deployments to diagnose errors.

The dashboard polls every 15 seconds for deployment status updates. When deployments are in a transitional state (`creating`, `deploying`, or `stopping`), polling increases to every 3 seconds. Per-deployment metrics refresh every 60 seconds. Click the refresh button for immediate updates.

The health-check request allows up to 55 seconds so a scale-to-zero endpoint has time to start.

Filter logs by severity using the filter buttons:

| Level        | Color    | Description         |
| ------------ | -------- | ------------------- |
| **DEBUG**    | Gray     | Debug messages      |
| **INFO**     | Blue     | Normal requests     |
| **WARNING**  | Yellow   | Non-critical issues |
| **ERROR**    | Red      | Failed requests     |
| **CRITICAL** | Dark Red | Critical failures   |

| Control     | Description                         |
| ----------- | ----------------------------------- |
| **Errors**  | Filter to ERROR and WARNING entries |
| **All**     | Show all log entries                |
| **Copy**    | Copy all visible logs to clipboard  |
| **Refresh** | Reload log entries                  |

When investigating errors: first click **Errors** to filter to ERROR and WARNING entries, then review timestamps and HTTP status codes. Copy logs to clipboard for sharing with your team.

```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
// 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);
```

```bash
# Send image for inference
curl -X POST "https://predict-abc123.run.app/predict" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "[email protected]" \
  -F "conf=0.25" \
  -F "iou=0.7" \
  -F "imgsz=640"
```

When viewing the `Code` tab in the platform, your actual endpoint URL and API key are automatically filled in. Copy the code and run it directly. See [API Keys](../account/api-keys.md) to generate a key.

GET /api/monitoring

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

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

GET /api/deployments/{deploymentId}/health

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

If latency is too high:

1. Verify the model size is appropriate
2. Consider a closer region
3. Check the image size sent with each request

!!! example "Reducing Latency"

    Try a smaller `imgsz` value and compare the resulting latency and accuracy for your model. Deploy to a region
    closer to callers to reduce network latency.

If errors are occurring:

1. Review error logs in the `Logs` tab
2. Check request format (multipart form required)
3. Verify API key is valid
4. Retry a request and compare its timestamp with the deployment logs

If hitting capacity:

1. Reduce the inference image size or use a smaller model
2. Deploy additional endpoints and distribute requests between them
3. Retry transient failures with backoff