---
comments: true
description: Learn about model deployment options in Ultralytics Platform including inference testing, dedicated endpoints, and monitoring dashboards.
keywords: Ultralytics Platform, deployment, inference, endpoints, monitoring, YOLO, production, cloud deployment
---

# Deployment

[Ultralytics Platform](https://platform.ultralytics.com) provides comprehensive deployment options for putting your YOLO models into production. Test models with browser-based inference, deploy to dedicated endpoints across 43 global regions, and monitor performance in real-time.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/JjgQYPetX8w"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Get Started with Ultralytics Platform - Deploy
</p>

## Overview

The Deployment section helps you:

- **Test** models directly in the browser with the `Predict` tab
- **Deploy** to dedicated endpoints in 43 global regions
- **Monitor** request metrics, logs, and health checks
- **Scale to zero** when idle (deployments currently run a single active instance)

![Ultralytics Platform Deploy Page World Map With Overview Cards](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/deploy-page-world-map-with-overview-cards.avif)

## Deployment Options

Ultralytics Platform offers multiple deployment paths:

| Option                                  | Description                                              | Best For                |
| --------------------------------------- | -------------------------------------------------------- | ----------------------- |
| **[Predict Tab](inference.md)**         | Browser-based inference with image, webcam, and examples | Development, validation |
| **Shared Inference**                    | Multi-tenant service across 3 regions                    | Light usage, testing    |
| **[Dedicated Endpoints](endpoints.md)** | Single-tenant services across 43 regions                 | Production, low latency |

## Workflow

```mermaid
graph LR
    A[✅ Test] --> B[⚙️ Configure]
    B --> C[🌐 Deploy]
    C --> D[📊 Monitor]

    style A fill:#4CAF50,color:#fff
    style B fill:#2196F3,color:#fff
    style C fill:#FF9800,color:#fff
    style D fill:#9C27B0,color:#fff
```

| Stage         | Description                                                                 |
| ------------- | --------------------------------------------------------------------------- |
| **Test**      | Validate model with the [`Predict` tab](inference.md)                       |
| **Configure** | Select region and deployment name (deployments use fixed default resources) |
| **Deploy**    | Create a dedicated endpoint from the [`Deploy` tab](endpoints.md)           |
| **Monitor**   | Track requests, latency, errors, and logs in [Monitoring](monitoring.md)    |

## Architecture

### Shared Inference

The shared inference service runs in 3 key regions, automatically routing requests based on your data region:

```mermaid
graph TB
    User[User Request] --> API[Platform API]
    API --> Router{Region Router}
    Router -->|US users| US["US Predict Service<br/>Iowa"]
    Router -->|EU users| EU["EU Predict Service<br/>Belgium"]
    Router -->|AP users| AP["AP Predict Service<br/>Taiwan"]

    style User fill:#f5f5f5,color:#333
    style API fill:#2196F3,color:#fff
    style Router fill:#FF9800,color:#fff
    style US fill:#4CAF50,color:#fff
    style EU fill:#4CAF50,color:#fff
    style AP fill:#4CAF50,color:#fff
```

| Region | Location             |
| ------ | -------------------- |
| US     | Iowa, USA            |
| EU     | Belgium, Europe      |
| AP     | Taiwan, Asia-Pacific |

### Dedicated Endpoints

Deploy to 43 regions worldwide on Ultralytics Cloud:

- **Americas**: 14 regions
- **Europe**: 13 regions
- **Asia-Pacific**: 12 regions
- **Middle East & Africa**: 4 regions

Each endpoint is a single-tenant service with:

- Default resources of `1 CPU`, `2 GiB` memory, `minInstances=0`, `maxInstances=1`
- Scale-to-zero when idle
- Unique endpoint URL
- Independent monitoring, logs, and health checks

## Deployments Page

Access the global deployments page from the sidebar under `Deploy`. This page shows:

- **World map** with deployed region pins (interactive map)
- **Overview cards**: Total Requests (24h), Active Deployments, Error Rate (24h), P95 Latency (24h)
- **Deployments list** with three view modes: cards, compact, and table
- **New Deployment** button to create endpoints from any completed model

![Ultralytics Platform Deploy Page Overview Cards And Deployments List](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/platform/deploy-page-overview-cards-and-deployments-list.avif)

!!! info "Automatic Polling"

    The page polls every 15 seconds normally. When deployments are in a transitional state (`creating`, `deploying`, or `stopping`), polling increases to every 3 seconds for faster feedback.

## Key Features

### Global Coverage

Deploy close to your users with 43 regions covering:

- North America, South America
- Europe, Middle East, Africa
- Asia Pacific, Oceania

### Scaling Behavior

Endpoints currently behave as follows:

- **Scale to zero**: No cost when idle (default)
- **Single active instance**: `maxInstances` is currently capped at `1` on all plans

!!! tip "Cost Savings"

    Scale-to-zero is enabled by default (min instances = 0). You only pay for active inference time.

### Low Latency

Dedicated endpoints provide:

- Cold start: ~5-15 seconds (cached container), up to ~45 seconds (first deploy)
- Warm inference: 50-200ms (model dependent)
- Regional routing for optimal performance

### Health Checks

Each running deployment includes an automatic health check with:

- Live status indicator (healthy/unhealthy)
- Response latency display
- Auto-retry when unhealthy (polls every 20 seconds)
- Manual refresh button

## Quick Start

Deploy a model in under 2 minutes:

1. Train or upload a model to a project
2. Go to the model's **Deploy** tab
3. Select a region from the latency table
4. Click **Deploy** — your endpoint is live

!!! example "Quick Deploy"

    ```
    Model → Deploy tab → Select region → Click Deploy → Endpoint URL ready
    ```

    Once deployed, use the endpoint URL with your API key to send inference requests from any application.

## Quick Links

- [**Inference**](inference.md): Test models in browser
- [**Endpoints**](endpoints.md): Deploy dedicated endpoints
- [**Monitoring**](monitoring.md): Track deployment performance

## FAQ

### What's the difference between shared and dedicated inference?

| Feature     | Shared          | Dedicated                            |
| ----------- | --------------- | ------------------------------------ |
| **Latency** | Variable        | Consistent                           |
| **Cost**    | Free (included) | Free (basic), usage-based (advanced) |
| **Scale**   | Limited         | Scale-to-zero, single instance       |
| **Regions** | 3               | 43                                   |
| **URL**     | Generic         | Custom                               |
| **Rate**    | 20 req/min      | Unlimited                            |

### How long does deployment take?

Dedicated endpoint deployment typically takes 1-2 minutes:

1. Image pull (~30s)
2. Container start (~30s)
3. Health check (~30s)

### Can I deploy multiple models?

Yes, each model can have multiple endpoints in different regions. Deployment counts are limited by plan: Free `3`, Pro `10`, Enterprise `unlimited`.

### What happens when an endpoint is idle?

With scale-to-zero enabled:

- Endpoint scales down after inactivity
- First request triggers cold start
- Subsequent requests are fast

First requests after an idle period trigger a cold start.
