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read [this guide](../getting-started/deploying-zenml/README.md). |
{% endhint %} |
A ZenML OSS deployment consists of the following moving pieces: |
* **ZenML OSS Server**: This is a FastAPI app that manages metadata of pipelines, artifacts, stacks etc. |
Note: In ZenML Pro, the notion of a ZenML server is replaced with what is known as a "Tenant". For |
all intents and purposes, consider a ZenML Tenant to be a ZenML OSS server that comes with more functionality. |
* **OSS Metadata Store**: This is where all ZenML tenant metadata is stored, including |
ML metadata such as tracking and versioning information about pipelines and |
models. |
* **OSS Dashboard**: This is a ReactJS app that shows pipelines, runs, etc. |
* **Secrets Store**: All secrets and credentials required to access customer |
infrastructure services are stored in a secure secrets store. The ZenML Pro |
API has access to these secrets and uses them to access customer |
infrastructure services on behalf of the ZenML Pro. The secrets store can be |
hosted either by the ZenML Pro or by the customer. |
 |
ZenML OSS is free with Apache 2.0 license. Learn how to deploy it [here](./deploying-zenml/README.md). |
{% hint style="info" %} |
To learn more about the core concepts for ZenML OSS, go [here](../getting-started/core-concepts.md). |
{% endhint %} |
## ZenML Pro (SaaS or Self-hosted) |
{% hint style="info" %} |
If you're interested in assessing ZenML Pro SaaS, you can create |
a [free account](https://cloud.zenml.io/?utm\_source=docs\&utm\_medium=referral\_link\&utm\_campaign=cloud\_promotion\&utm\_content=signup\_link). |
If would like to self-host ZenML Pro, please [book a demo](https://zenml.io/book-a-demo). |
{% endhint %} |
The above deployment can be augmented with the ZenML Pro components: |
* **ZenML Pro Control Plane**: This is the central controlling entity of all tenants. |
* **Pro Dashboard**: This is a dashboard that builds on top of the OSS dashboard, and |
add further functionality. |
* **Pro Metadata Store**: This is a PostgreSQL database where all ZenML Pro related metadata is stored such |
as roles, permissions, teams, and tenant management related data. |
* **Pro Add-ons**: These are Python modules injected into the OSS Server for enhanced functionality. |
* **Identity Provider**: ZenML Pro offers flexible authentication options. |
In cloud-hosted deployments, it integrates with [Auth0](https://auth0.com/), |
allowing users to log in via social media or corporate credentials. |
For self-hosted deployments, customers can configure their |
own identity management solution, with ZenML Pro supporting |
custom OIDC provider integration. This allows organizations to |
leverage their existing identity infrastructure for authentication |
and authorization, whether using the cloud service or deploying on-premises. |
 |
ZenML Pro offers many additional features to increase your teams |
productivity. No matter your specific needs, the hosting options for ZenML Pro |
range from easy SaaS integration to completely air-gapped deployments on your own |
infrastructure. |
You might have noticed this architecture builds on top of the ZenML OSS system architecture. |
Therefore, if you already have ZenML OSS deployed, it is easy to enroll it as part of a |
ZenML Pro deployment! |
The above components interact with other MLOps stack components, secrets, and data in |
the following scenarios described below. |
{% hint style="info" %} |
To learn more about the core concepts for ZenML Pro, go [here](../getting-started/zenml-pro/core-concepts.md) |
{% endhint %} |
### ZenML Pro SaaS Architecture |
 |
For the ZenML Pro SaaS deployment case, all ZenML services are hosted on infrastructure hosted by the ZenML Team. |
Customer secrets and credentials required to access customer infrastructure are |
stored and managed by the ZenML Pro Control Plane. |
On the ZenML Pro infrastructure, only ML _metadata_ (e.g. pipeline and |
model tracking and versioning information) is stored. All the actual ML data |
artifacts (e.g. data produced or consumed by pipeline steps, logs and |
visualizations, models) are stored on the customer cloud. This can be set up |
quite easily by configuring |
an [artifact store](../component-guide/artifact-stores/artifact-stores.md) |
with your MLOps stack. |
Your tenant only needs permissions to read from this data to display artifacts |
on the ZenML dashboard. The tenant also needs direct access to parts of the |
customer infrastructure services to support dashboard control plane features |
such as CI/CD, triggering and running pipelines, triggering model deployments |
and so on. |
The advantage of this setup is that it is a fully-managed service, and is |
very easy to get started with. However, for some clients even some metadata |
can be sensitive; these clients should refer to the other architecture diagram. |
<details> |
<summary>Detailed Architecture Diagram for SaaS deployment</summary> |
Subsets and Splits