--- title: "MONAI Deploy" description: "MONAI Deploy is a framework for developing, packaging, testing, and deploying medical AI applications in clinical and research environments. Its modular architecture covers the App SDK, Workflow Manager, and Informatics Gateway." canonical: https://project-monai.github.io/deploy.html audience: [engineer] last_updated: 2026-06-11 source: deploy.html --- MONAI Deploy · Run # Your model, packaged for the clinic Wrap a trained model as a MONAI Application Package (MAP): a container that consumes DICOM, runs inference, and emits DICOM results. One artifact runs on a workstation, Holoscan, or Kubernetes. [Get Started](#quick-start) [Documentation](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/stable/) [GitHub](https://github.com/Project-MONAI/monai-deploy-app-sdk) package & run a MAP $ pip install monai-deploy-app-sdk ``` $ monai-deploy package my_app \ -c app.yaml -t my_app:latest \ --platform x64-workstation $ monai-deploy run my_app:latest \ -i input/ -o output/ ``` Why MONAI Deploy ## Deployment built for healthcare, not retrofitted to it Generic MLOps stacks stop at the model server. MONAI Deploy speaks DICOM and FHIR, packages reproducibly, and scales from a research workstation to a hospital fleet. ### Standardized Packaging One MAP container format for every target environment Reproducible multi-site deployments: same artifact, same behavior Dependencies pinned and bundled at package time ### Healthcare-Native I/O DICOM in, DICOM/FHIR out, with PACS integration via the Informatics Gateway Built-in operators for series selection, segmentation writing, and reports Designed for clinical-workflow and compliance requirements ### Scalable Architecture Horizontal scaling on Kubernetes or single-node workstations Workflow Manager orchestrates pipelines and monitors execution Load balancing and failover for production reliability Platform ## From trained model to PACS MONAI artifacts MONAI Deploy subsystems Third-party systems ### Researchers & developers build 1 #### Trained Model From MONAI Core, MONAI Label, or any PyTorch checkpoint you can wrap in an operator. 2 #### App SDK A Pythonic SDK to compose, test, and package inference apps from reusable operators. 3 #### MAP Container Model, pre/post-processing, and dependencies in one portable, deployment-ready image. MAP handoff ### Hospital operations run 4 #### Inference Engine Executes MAPs efficiently: local runner, Holoscan, or cluster runtimes. 5 #### Workflow Manager Routes studies to the right MAP and orchestrates multi-step pipelines. 6 #### Informatics Gateway Secure DICOM and FHIR exchange with hospital information systems. 7 #### PACS Results land back in the archive radiologists already use (third-party system). ### The three subsystems, in depth ### App SDK Build & package Compose apps from operators like `DICOMSeriesToVolumeOperator` and `MonaiSegInferenceOperator`, then package them into MAPs with the `monai-deploy` CLI. [SDK Documentation](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/stable/index.html) ### Workflow Manager Orchestrate The central orchestration service: routes incoming studies, triggers the right MAPs, tracks task state, and coordinates clinical pipelines end to end. [View Repository](https://github.com/Project-MONAI/monai-deploy-workflow-manager) ### Informatics Gateway Connect Standards-based DICOM and FHIR I/O between AI applications and hospital systems: the bridge to PACS, RIS, and EHR. [View Repository](https://github.com/Project-MONAI/monai-deploy-informatics-gateway) Quick Start ## Install, run, package, deploy Four steps take the bundled simple-imaging example from source to a runnable MAP container on your workstation. 1 ### Set up the environment Install the App SDK and clone the repository for the example apps. ``` $ pip install monai-deploy-app-sdk $ git clone https://github.com/Project-MONAI/monai-deploy-app-sdk.git $ cd monai-deploy-app-sdk $ pip install matplotlib Pillow scikit-image ``` [Setup guide](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/stable/getting_started/installing_app_sdk.html) 2 ### Run the app locally Execute the simple imaging example straight from Python, no container yet. ``` $ python examples/apps/simple_imaging_app/app.py \ -i examples/apps/simple_imaging_app/brain_mr_input.jpg \ -o output ``` [App tutorial](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/stable/getting_started/tutorials/simple_app.html) 3 ### Package it as a MAP Build the Docker image that runs identically in any environment. ``` $ monai-deploy package examples/apps/simple_imaging_app \ -c simple_imaging_app/app.yaml \ -t simple_app:latest \ --platform x64-workstation ``` [Packaging guide](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/stable/developing_with_sdk/packaging_app.html) 4 ### Run the packaged container Same inputs, same outputs, now from the deployable artifact. ``` $ mkdir -p input && cp examples/apps/simple_imaging_app/brain_mr_input.jpg input/ $ monai-deploy run simple_app-x64-workstation-dgpu-linux-amd64:latest \ -i input -o output ``` [Deployment guide](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/stable/developing_with_sdk/executing_packaged_app_locally.html) [ #### Example Applications Segmentation, classification, and DICOM-pipeline examples to copy from. ](https://github.com/Project-MONAI/monai-deploy-app-sdk/tree/main/examples)[ #### API Reference Operators, application classes, and packaging modules in detail. ](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/latest/modules/index.html)[ #### Community Support GitHub Discussions for deployment questions and architecture advice. ](https://github.com/Project-MONAI/monai-deploy/discussions) Case Studies ## Running in production today The same MAP format scales from a single research PACS to a marketplace reaching 10,000+ institutions. ![Siemens Healthineers](/assets/img/logos/siemens.png) ### Siemens Healthineers Enterprise AI Integration MONAI Deploy is integrated into the AI-Rad Companion platform, cutting model deployment time across Siemens' global healthcare network and improving performance monitoring. Enterprise Scale AI Platform Clinical Integration [Read Blog](https://blogs.nvidia.com/blog/rsna-siemens-healthineers-monai-medical-imaging-ai/) ![Mayo Clinic](/assets/img/logos/mayo-clinic.png) ### Mayo Clinic Clinical AI Integration The Center for Augmented Intelligence in Imaging at Mayo Clinic Florida runs MONAI-packaged models inside its clinical Radiology pipeline. The case study documents the pattern. Clinical Research AI Infrastructure Imaging [Read Case Study](mayo-case-study.html) ![AI Centre for Value Based Healthcare](/assets/img/logos/aicentre.jpg) ### AI Centre for Value Based Healthcare Research Platform AIDE, the AI Deployment Engine, safely deploys AI models into research workflows across the UK's AI Centre hospital network. Research Deployment Engine AI Safety [Learn More](https://www.aicentre.co.uk/our-platforms#tab-1) ![Mercure](/assets/img/logos/mercure.png) ### Mercure DICOM Orchestration Platform An open-source DICOM orchestrator with native MAP support: drop a MONAI Application Package into a radiology research workflow without custom glue code. Open Source DICOM MAP Integration [Read Blog](https://monai.medium.com/fe7cfd77acce?source=friends_link&sk=894711683f40c61b1116fc5097a24b0a) ![Mayo Clinic](/assets/img/logos/mayo-clinic.png) × ![Siemens Healthineers](/assets/img/logos/siemens.png) ### Mayo Clinic & Siemens Digital Marketplace Scalable Research Deployment Mayo Clinic AI applications built with MONAI are available to over 10,000 institutions through the Siemens Digital Marketplace: zero-code, globally scaled deployment of MONAI-powered solutions. Global Scale Zero-Code Research [Read Case Study](mayo-siemens-case-study.html) Reference ## How to cite MONAI Deploy If MONAI Deploy supports your research, please cite the JMIR AI paper: @article{gupta2024monai, title={Current State of Community-Driven Radiological AI Deployment in Medical Imaging}, author={Gupta, Vikash and Erdal, Barbaros and Ramirez, Carolina and Floca, Ralf and Genereaux, Bradley and Bryson, Sidney and Bridge, Christopher and Kleesiek, Jens and Nensa, Felix and Braren, Rickmer and Younis, Khaled and Penzkofer, Tobias and Bucher, Andreas Michael and Qin, Ming Melvin and Bae, Gigon and Lee, Hyeonhoon and Cardoso, M Jorge and Ourselin, Sebastien and Kerfoot, Eric and Choudhury, Rahul and White, Richard D and Cook, Tessa and Bericat, David and Lungren, Matthew and Haukioja, Risto and Shuaib, Haris}, journal={JMIR AI}, volume={3}, pages={e55833}, year={2024}, doi={10.2196/55833} } [View on DOI](https://doi.org/10.2196/55833) Connect ## Deploy with the community The Deploy Working Group meets in the open. Bring your integration questions, hospital constraints, and deployment war stories. ### Documentation Concepts, tutorials, and deployment strategies for the full Deploy stack. [Read the Docs](https://monai.readthedocs.io/projects/monai-deploy-app-sdk/en/latest/index.html) ### Deploy Working Group The group steering the MAP spec, subsystems, and clinical-integration patterns. [Join the Group](wg_deploy.html) ### GitHub Repository Source for the App SDK, Workflow Manager, and Informatics Gateway. [View Code](https://github.com/Project-MONAI/monai-deploy) ### Slack Community Real-time help from maintainers and other teams deploying to the clinic. [Join Slack](https://join.slack.com/t/projectmonai/shared_invite/zt-3hucgm02q-i8Bn9XofDZs2UGOH4jUl4w)