--- title: "AI Deploy - Features, capabilities, and limitations" description: "Learn about the technical capabilities and limitations of the Public Cloud AI Deploy offer." url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-capabilities lang: en --- > For AI agents: the complete documentation index is available at https://docs.us.ovhcloud.com/en/llms.txt, the full documentation bundle is available at https://docs.us.ovhcloud.com/en/llms-full.txt. # AI Deploy - Features, capabilities, and limitations **Learn about the technical capabilities and limitations of the Public Cloud AI Deploy offer.** **Note:** AI Deploy is covered by [**OVHcloud Public Cloud Special Conditions**](https://us.ovhcloud.com/legal/service-specific-terms/).   ## Features ### Available features AI Deploy allows you to deploy your AI applications and models with compute resources (CPUs, GPUs) and high availability. | Feature | Details | | ------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **AI environments** | | | Easy customization | AI Deploy allows the installation of almost any Conda or Pip packages. You can easily customize your environment to suit your needs. It also allows you to use your own environment by specifying your Docker image. | | **Management** | | | Multiple ways to manage your deployed apps | You can manage your AI Deploy apps through the OVHcloud Control Panel, API or CLI. Depending on your needs, you can easily automate their creation and deletion as well. | | Easy start and Stop | You can start and stop an app in one click. | | Check the logs of your app | You can also check the logs of your deployed app through the OVHcloud Control Panel, API or CLI. | | **Compute resources** | | | Guaranteed compute resources | Select the number of CPUs or GPUs required during the creation of the AI Deploy app. Once launched, you will keep these resources as long as your app is running. | | Background execution | Your tasks can be executed in the background, meaning that closing your web browser will not affect your work. | | No maximum runtime | Your tasks can last as long as your app is running. | | Monitoring tools | Each AI Deploy app comes with a native Grafana dashboard, allowing you to track and monitor your CPU, GPU, RAM, and storage resources. | | **Storage** | | | Fast and flexible storage | Each AI Deploy service comes with local storage but also the ability to attach remote storage from Object Storage. From a few GiB to multiple TiB, we push your data near our compute power on fast SSD storage for better performances. | | Git repositories importation | During the creation of your AI Deploy app, you can specify one or multiple Git repositories to download inside your app environment. | | **Security** | | | Open or restricted authentication | During the creation of your AI Deploy app, you can either select Public Access, allowing anyone to access your application, or Restricted Access, allowing access via security tokens or credentials. | | **Billing** | | | Easy billing | You only pay for what you consume, billed per minute with per-second granularity. |   #### Logs and Monitoring tools ##### **Command line interface (CLI)** AI Deploy is compliant with the OVHcloud AI CLI. Discover how to [install the OVHcloud AI CLI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-install-client.md). ##### **Logs** To check the logs of your app, you can do it via the `ovhai` CLI using the following command: ``` ovhai app logs ``` ##### **Monitoring tools** To see information about your app, you can do so with the `ovhai` CLI using this command: ``` ovhai app get ``` You can then access your metrics through the `Monitoring Url`. You are also able to check it from the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/) in your app information by clicking the `Go to Graph Dashboard` button.   #### API management API endpoints to manage your AI Deploy apps can be found here: [GET /cloud/project/{serviceName}/ai/app](https://api.us.ovhcloud.com/console/?section=/cloud&branch=v1#get-/cloud/project/-serviceName-/ai/app)   ### Planned features We continuously improve our offers. You can follow, vote, and submit ideas to add to [our roadmap](https://github.com/orgs/ovh/projects/16/views/1?pane=info\&sliceBy%5Bvalue%5D=AI+Deploy).   ## Capabilities and limitations ### Supported regions AI Notebooks can be used from any country in the world, as long as you have an OVHcloud account. Our AI & Machine Learning services are based in the `US-EAST-VA` (Vint Hill, Virginia) region.   ### Attached resources #### Compute resources ##### **Replica type** You can either choose the number of `GPUs` or `CPUs` for an AI Deploy app, not both. By default, an app uses one CPU instance. The memory resource is not customizable. ##### **Available replica ranges** If you choose `GPU`: - CPU, memory, and local storage resources are not customizable but scaled linearly with each additional GPU. If you choose `CPU`: - Memory and local storage resources are not customizable but scaled linearly with each additional CPU. The maximum number of CPUs/GPUs, memory per CPU/GPU, and local storage is available on the [OVHcloud website](https://us.ovhcloud.com/public-cloud/prices/#ai-&-machine-learning), Control Panel and the `ovhai` CLI. ``` ovhai capabilities flavor list ``` For your information, the current limits are: - CPU: 12 per app. - GPU: 4 per app. ##### **Available hardware for AI Deploy** Currently, we provide: - `ai1-1-cpu` - `L4-1-gpu` - `L40s-1-gpu` ##### **Scaling** AI Deploy offers the possibility to choose between two [scaling strategies](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-apps-deployments.md): **static** or **automatic**. If you choose `static scaling`: - You can go from 1 to 10 replicas. If you choose `autoscaling`: - You can choose both the **minimum number** of replicas (1 by default) and the **maximum number** of replicas. - You can define the metric monitored that will act as a trigger for autoscaling: `CPU` or `RAM`. - You can choose the threshold for the percentage of average use: integer between 1 and 100%.   #### Available storage ##### **Local storage** Each AI Deploy app comes with a local storage space, which is ephemeral. When you delete your app, this storage space is also deleted. This storage space depends on the selected instances during AI Deploy app deployment. Please refer to the compute resources section for more information. **Note:** **Local storage** is limited and not the recommended way to handle data, see the [OVHcloud documentation on data](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/data-concepts-best-practices.md) for more information. ##### **Attached storage** You can attach data volumes from Public Cloud Object Storage. For best performance, we recommend keeping the Object Storage bucket in the same region as your AI Deploy app. Mounting a bucket from a different region also works, but adds cross-region network latency. Attached storage allows you to work on several TB of data, while being persistent when you delete your AI Deploy app.   #### Maximum execution time There is no duration limitation on AI Deploy apps execution.   #### Environment customization & Permitted deployment Each environment can be customized directly with PIP or CONDA (we support almost any package and library). AI Deploy authorizes the deployment of your own Docker images and applications from the [OVHcloud portfolio](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-apps-portfolio.md). Docker images can be hosted in a public or private registry. The use of `docker-compose` is not possible. Please be aware that images need to be built with an **AMD architecture**. For more information, see [this guide](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-build-use-custom-image.md).   #### High-availability Your application is deployed simultaneously on the amount of selected instances. To benefit from high availability, a minimum of two instances is required. In case of instance failure, a new one is automatically created.   #### Rolling upgrade You can update the Docker image of your application to provide an updated version of your service. Updates are incremental and will not cause any downtime. Your current configuration will also be preserved, such as your HTTP endpoint and deployment policy. There is no need to stop and restart the application for the image update to take effect.   ### Network - **Public networking** can be used for all the AI Tools. - **Private networking (vRack)** is not supported. - **Ports**: you can map your AI Deploy app to only one port. Default port is 8080. - **Ingress and Egress traffic** are included in the service plans and not metered.   ### Quotas per Public Cloud project Each Public Cloud project grants a customer by default a maximum of 4 GPUs used simultaneously. Reach out to our support if you need to increase this limitation.   ## Go further Browse all the [AI & Machine Learning guides](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/landing-page-ai-machine-learning.md). Browse the rest of the [OVHcloud US documentation](https://docs.us.ovhcloud.com/en/index.md) for guides on our other products and services. Learn more about [AI & Machine Learning](https://us.ovhcloud.com/public-cloud/ai-machine-learning/) on the OVHcloud US website. If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://us.ovhcloud.com/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.