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Roboflow Deploy

Deploy custom or foundation models in minutes

Integrate vision models into your existing toolset and codebase

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Inference Optimized for Any Target Hardware

Once you've trained a model or selected a foundation model (CLIP, SAM, DETIC, DINOv2), your model runs everywhere you need it, automatically.

pip install roboflow

Automate and integrate any part of the Roboflow workflow into your codebase. See docs »
Deploy to edge devices, your own cloud, and more with device-optimized containers and field-tested SDKs.
Upload model weights, like YOLOv5, YOLOv7, and YOLOv8, to use our managed infrastructure and have your model as an API endpoint immediately available to use in production
Perform inference with specific model versions and save predictions that you've made on the model
Collect sample inferences whether at specific time intervals, at random, and with different confidence thresholds to improve your model's performance

Deploy to the Cloud

Your computer vision pipeline is automatically set up to handle demanding workloads
Hosted API to receive predictions from your model as fast as your network connection can handle sending the frames
Autoscaling infrastructure includes load balancing, supports burst and no burst, and is always on without any custom engineering work
Gather multiple data points from each piece of data to be use in applications or create insights in real-time
Roboflow adheres to strict security and privacy standards. Our organization holds SOC2 Type 1 certification and PCI compliance.
Roboflow Annotate Label Assist

Deploy to the Edge

Run your model on embedded devices for drones, robotics, IoT, offline scenarios, and more
Use Luxonis OAK devices via pip install roboflowoak or via Docker for a standardized device that combines a camera with a built in hardware accelerator
Optimized it to get maximum performance from the NVIDIA Jetson line of edge-AI devices by specifically tailoring the drivers, libraries, and binaries specifically to its CPU and GPU architectures. See docs »
Mobile iOS SDK is available for deploying your model directly to an iOS application running on iPhone or iPad

Deploy to the Browser

Seamlessly add computer vision functionality into web applications See docs »
Roboflow.js is a custom layer on top of Tensorflow.js to enable real-time inference via Javascript at 30+ FPS
A browser-based way to quickly test model predictions and share your model without a new environment to host a model, save weights, or any GPU hassle
Customize to match your brand, add logos, remove buttons, and add text. The source code for the HTML, CSS, and JavaScript for these pages can be hosted anywhere.

Self-hosting and Enterprise Deployments

Deploy our Docker container in your private cloud (or on your own metal) using our Inference Server and our License Server Docker containers. See docs »
Run using CPU on a self-contained server in multiple locations to ensure your models are firewalled and able to run independently for maximum security
Ensure that sensitive images never leave your private network using Inference Server to receive requests from client applications inside the private network, with no Internet connection
Configure the Roboflow Inference Server to cache weights for up to 30 days to run completely air-gapped or in locations where an Internet connection is not readily available
Install NVIDIA drivers and nvidia-container-runtime, allowing Docker to pass through your GPU to the inference server
Roboflow Annotate Label Assist

Your Models Improve Over Time

Use active learning to improve your models using real-world production data
Roboflow Annotate Label Assist

Programmatically sample data once you identify problems or areas for improvement with your model

Cache data on your deployment device or system for a future upload to Roboflow for annotation

Your model will quickly learn what to detect and what not to detect with increasing confidence in the environments it is present in

Explore Deployment Options

Browse our blog for resources on how to deploy models hosted on Roboflow.
Explore Tutorials

Deploy a Computer Vision Model: A How-To Guide

Explore all of the deployment options available on the Roboflow Deploy tab.

How to Deploy a YOLOv8 Model to a Raspberry Pi

In this guide, you'll learn how to deploy a YOLOv8 model onto a Raspberry Pi.

How to Train and Deploy Custom Models to Your OAK

A guide on how to train and deploy a custom computer vision model to your Luxonis OAK in one click.

Get Started with Pre-Built Templates

Use Roboflow Templates to begin connecting your model to business logic faster than ever.
Explore Templates
Count Objects
Count Objects

Count the number of objects that appear in an image.

Applications
  • Count people on a construction site
  • Monitor congestion at checkout lines
  • Count pills for prescriptions
View the guide
Measure Object Distance
Measure Object Distance

Measure the distance between two or more objects.

Applications
  • Identify the proximity of two vehicles
  • Measure the length of a material
  • Check if people are close to a vehicle
View the guide
Send Text Messages
Send Text Messages

Get a text message when criteria are met in your model.

Applications

Get a text when:

  • A dial shows above or below a certain value.
  • A defect is detected in a product.
  • A flame stops burning.
View the guide
Read Analog Dials
Read Analog Dials

Read the value on any analog dial with a camera.

Applications
  • Monitor the value of a pressure gauge.
  • Record values displayed on a temperature dial.
View the guide

Train and deploy your own state of the art computer vision model today.

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