Deployment

Ultralytics Platform provides comprehensive model deployment options for putting your YOLO models into production. Test models with browser-based inference, deploy to dedicated endpoints across 42 global regions, and monitor performance in real-time.

<strong>Watch:</strong> Get Started with Ultralytics Platform - Deploy

Overview

The Deployment section helps you:

• Test models directly in the browser with the Predict tab
• Deploy to dedicated endpoints in 42 global regions
• Monitor request metrics, logs, and health checks
• Scale to zero when idle (deployments currently run a single active instance)

<!-- screenshot -->

Deployment Options

Ultralytics Platform offers multiple deployment paths:

Workflow

graph LR
    A[✅ Test]:::start --> B[⚙️ Configure]:::proc
    B --> C[🌐 Deploy]:::proc
    C --> D[📊 Monitor]:::out

    classDef start fill:#4CAF50,color:#fff
    classDef proc fill:#2196F3,color:#fff
    classDef out fill:#9C27B0,color:#fff

Architecture

Shared Inference

The shared inference service runs in 3 key regions. Requests to a model are routed to the service in that model's data region, so results stay inside the region where the model is stored:

graph TB
    User[User Request]:::start --> API[Platform API]:::proc
    API --> Router{Model Data Region}:::decide
    Router -->|US models| US["US Predict Service
Iowa"]:::out
    Router -->|EU models| EU["EU Predict Service
Belgium"]:::out
    Router -->|AP models| AP["AP Predict Service
Taiwan"]:::out

    classDef start fill:#4CAF50,color:#fff
    classDef proc fill:#2196F3,color:#fff
    classDef decide fill:#FF9800,color:#fff
    classDef out fill:#9C27B0,color:#fff

{% include "macros/platform-data-regions.md" %}

Dedicated Endpoints

Deploy to 42 regions worldwide on Ultralytics Cloud:

• Americas: 14 regions
• Europe: 13 regions
• Asia-Pacific: 12 regions
• Middle East & Africa: 3 regions

Each endpoint is a single-tenant service with:

• Platform-managed sizing (not configurable today)
• Scale-to-zero when idle
• Unique endpoint URL with its own interactive API reference at /docs
• Its own API key binding, so only that key can call the endpoint
• 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; click a region to open the New Deployment dialog
• 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
• Refresh button and an Updated timestamp in the page header

<!-- screenshot --> !!! info "Automatic Polling"

The page refreshes automatically, polling faster while deployments are in a transitional state (`creating`, `deploying`, or `stopping`). See [Monitoring](monitoring.md) for details.

Key Features

Global Coverage

Deploy close to your users with 42 regions covering:

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

Scaling Behavior

Endpoints currently behave as follows:

• Scale to zero: idle endpoints scale down to zero and cold-start on the next request
• Single active instance: each endpoint currently serves from one instance on all plans
• Load shedding: requests receive 429 responses when the endpoint is temporarily at capacity — see Direct Endpoint Requests
• Request timeout: each request may run for up to 1 hour, which is enough for video inference

Regional Deployment

Use the measured region latency to place an endpoint near its callers. Actual inference latency depends on the model, input size, endpoint state, and network path.

Health Checks

Each running deployment includes an automatic health check with:

• Live status indicator (healthy/unhealthy)
• Response latency display
• Auto-retry when unhealthy, stopping once healthy
• Manual refresh button

Quick Start

Create a deployment:

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 and wait for the deployment status to become Ready

!!! example "Quick Deploy"

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

The deployment name is generated from the model name and the region city, so no naming step is required. Once deployed, use the endpoint URL with your API key to send inference requests from any application.

Quick Links

• Inference: Test models in browser
• Endpoints: Deploy dedicated endpoints
• Monitoring: Track deployment performance

FAQ

What's the difference between shared and dedicated inference?

How long does deployment take?

The deployment remains in a creating or deploying state while its service starts. It becomes usable when the status changes to Ready; timing varies by model and region, and typically takes a few minutes.

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. The quota is charged to the workspace that owns the model, and an endpoint serves exactly one model at a time — use model replacement to swap it without changing the URL.

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. Opening the deployment card runs a health check that warms the endpoint, so a test prediction right after it responds quickly.