> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/deployment/readme.md).

# Deploy a Model or Workflow

We support both managed deployments and self-hosted deployment of both models and workflows. You reference a trained model by its [model ID](https://docs.roboflow.com/models/model-ids) when you deploy it or run inference.

## Managed Deployments

These options leverage Roboflow's cloud infrastructure to run your models and workflows, eliminating the need for you to manage your own hardware or software.

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="image">Cover image</th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Serverless Hosted API</strong></td><td>Get started immediately and scale automatically.</td><td><a href="/files/oBxDWgzNx20UzFPemnhO">/files/oBxDWgzNx20UzFPemnhO</a></td><td><a href="/pages/YDvPagsBwGY9ldmqpxXC">/pages/YDvPagsBwGY9ldmqpxXC</a></td></tr><tr><td><strong>Dedicated Deployment</strong></td><td>For large models and predictable workloads.</td><td><a href="/files/N2niWbewcJb6scyXw2yI">/files/N2niWbewcJb6scyXw2yI</a></td><td><a href="/pages/UGKqNGNHDnIUVpNRK05O">/pages/UGKqNGNHDnIUVpNRK05O</a></td></tr><tr><td><strong>Batch Processing</strong></td><td>Cost-effective processing of stored data.</td><td><a href="/files/DKJw6zTa6yvNVyovWe3f">/files/DKJw6zTa6yvNVyovWe3f</a></td><td><a href="/pages/ocXQX5V8OVNd7YI33v7n">/pages/ocXQX5V8OVNd7YI33v7n</a></td></tr></tbody></table>

## Self-Hosted Deployment

You can also deploy your models and workflows on self-hosted [Roboflow Inference](https://inference.roboflow.com/), which provides greater control over your environment, resources, and latency.

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="image">Cover image</th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Edge Device</strong></td><td>Run Inference on a Jetson, Raspberry Pi, or other edge hardware.</td><td><a href="/files/M5yoKmK39CttrEyoGqsv">/files/M5yoKmK39CttrEyoGqsv</a></td><td><a href="/pages/G9Ti4Aopkc08bPfAbvmf">/pages/G9Ti4Aopkc08bPfAbvmf</a></td></tr><tr><td><strong>Cloud Server</strong></td><td>Run Inference on your own cloud VM or server.</td><td><a href="/files/B5j3KkqrFVGLxeIDvjVP">/files/B5j3KkqrFVGLxeIDvjVP</a></td><td><a href="/pages/e2cNydVwX3NG0MigtWxu">/pages/e2cNydVwX3NG0MigtWxu</a></td></tr></tbody></table>

{% hint style="info" %}
Self-hosted options give you full control but require you to manage your own infrastructure.
{% endhint %}

<details>

<summary>What is Inference?</summary>

{% hint style="info" %}
In computer vision, inference refers to the process of using a trained model to analyze new images or videos and make predictions. For example, an object detection model might be used to identify and locate objects in a video stream, or a classification model might be used to categorize images based on their content.
{% endhint %}

[***Roboflow Inference***](https://inference.roboflow.com/) is an open-source project that provides a powerful and flexible framework for deploying computer vision models and workflows. It is the engine that powers most of Roboflow's managed deployment services. You can also self host it or use it to deploy your vision workflows to edge devices. Roboflow Inference offers a range of features and capabilities, including:

* Support for various model architectures and tasks, including object detection, classification, instance segmentation, and more.
* Workflows, which lets you build computer vision applications by combining different models, pre-built logic, and external applications by choosing from hundreds of building Blocks.
* Hardware acceleration for optimized performance on different devices, including CPUs, GPUs, and edge devices like NVIDIA Jetson.
* Multiprocessing for efficient use of resources.
* Video decoding for seamless processing of video streams.
* HTTP interface, APIs and docker images to simplify deployment
* Integration with Roboflow's hosted deployment options and the Roboflow platform.

</details>

<details>

<summary>What is a Workflow?</summary>

[Workflows](https://docs.roboflow.com/workflows) enable you to build complex computer vision applications by combining different models, pre-built logic, and external applications. They provide a visual, low-code environment for designing and deploying sophisticated computer vision pipelines.

With Workflows, you can:

* Chain multiple models together to perform complex tasks.
* Add custom logic and decision-making to your applications.
* Integrate with external systems and APIs.
* Track, count, time, measure, and visualize objects in images and videos.

</details>

## Choosing the Right Deployment Option

The best deployment option depends on whether your workload is real-time or bulk, how much latency you can tolerate, where your data lives, and how much infrastructure you want to manage. See [Choosing a Deployment Option](/deployment/choosing-a-deployment.md) for a side-by-side comparison of every option and a short decision flow to help you pick.

## Going to Production

Before you ship, walk through the [Production Readiness Checklist](/deployment/production-checklist.md) - error handling, retries, timeouts, rate limits, and scaling guidance. To read or configure a project's deployment state programmatically (deployed model, Active Learning, collected-image filtering), see the [Project Deploy API](/deployment/project-deploy-api.md).