Deploy on Azure

Deploy a Roboflow Inference server on an Azure virtual machine with the Inference CLI and SkyPilot.

You can run Roboflow Inference on machines hosted on Azure. This is a good fit when you want the features of Inference while managing your own cloud infrastructure.

Set up an Azure compute VM

To get started you need a compute instance running on Azure. For provisioning instances we recommend SkyPilot, a tool designed to help you set up cloud instances for AI projects.

Run the following command on your own machine:

pip install inference "skypilot[azure]"

Follow the SkyPilot cloud account setup documentation to authenticate with Azure, then run:

inference cloud deploy --provider azure --compute-type gpu

This provisions a GPU-capable instance in Azure with the latest version of Roboflow Inference installed automatically.

When the command finishes you should see a message like:

Deployed Roboflow Inference to azure on gpu, deployment name is ...
To get a list of your deployments: inference cloud status
To delete your deployment: inference cloud undeploy ...
To ssh into the deployed server: ssh ...
The Roboflow Inference Server is running at http://34.66.116.66:9001

You can then use that endpoint to run models: object detection, segmentation, classification, and keypoint models from your Roboflow workspace, plus foundation models like CLIP, PaliGemma, and SAM2.

Next steps

Point your client at the new server by setting api_url to the IP address of your VM and the port the server is running on (9001 by default). See Run a model for the first request, and the Inference CLI cloud commands for managing deployments.

Before you expose the server beyond a private network, review Securing a Self-Hosted Server.