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For the complete documentation index, see llms.txt. This page is also available as Markdown.

Deploy a Model or Workflow

Learn how to deploy workflows and models trained on or uploaded to Roboflow.

You can deploy models and Workflows on Roboflow-managed infrastructure or your own hardware. You reference a trained model by its model ID when you deploy it or run inference.

Managed Deployments

Roboflow runs the infrastructure for these deployments. For continuous live-camera processing, start with Cloud Streaming. Use the Serverless Cloud API for per-request inference and Batch Processing for stored files.

Cloud Streaming

Connect live cameras and run Workflows continuously on managed infrastructure.

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Serverless Cloud API

Run per-request image and Workflow inference.

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Dedicated Deployment

For large models and predictable workloads.

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Batch Processing

Cost-effective processing of stored data.

Self-Hosted Deployment

Run Inference on infrastructure you manage. Start with a runtime, then add Deployment Manager if you need to manage a fleet of devices.

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Run Inference

Choose Inference Server, Inference Library, or another SDK.

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Manage a Fleet

Deploy, update, and monitor Inference across devices.

Deployment Manager is available on Enterprise plans. Both options run on infrastructure you manage.

What is Inference?

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.

Roboflow Inference 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.

What is a Workflow?

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.

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 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 for error handling, retries, timeouts, rate limits, and scaling guidance.

NextChoosing a Deployment Option

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