index.md

# Deployment

In this section we provide guides for different deployment methods; your choice  will depend on a range of factors.

If you decide to deploy your Kedro project onto a single machine, you should consult our [guide to single-machine deployment](single_machine.md), and decide whether to:

* [use Docker for container-based deployment](./single_machine.md#container-based)
* [use package-based deployment](./single_machine.md#package-based)
* [use the CLI to clone and deploy your codebase to a server](./single_machine.md#cli-based)

If your pipeline is sizeable, you may want to run it across separate machines, so will need to consult our [guide to distributed deployment](distributed.md).

![Decision making diagram for deploying Kedro projects](../meta/images/deployment-diagram.png)


% Mermaid code, see https://github.com/kedro-org/kedro/wiki/Render-Mermaid-diagrams
% flowchart TD
%     A{Can your Kedro pipeline run on a single machine?} -- YES --> B[Consult the single-machine deployment guide];
%     B --> C{Do you have Docker on your machine?};
%     C -- YES --> D[Use a container-based approach];
%     C -- NO --> E[Use the CLI or package mode];
%     A -- NO --> F[Consult the distributed deployment guide];
%     F --> G["What distributed platform are you using?<br/><br/>Check out the guides for:<br/><br/><li>Airflow</li><li>Amazon SageMaker</li><li>AWS Step functions</li><li>Azure</li><li>Dask</li><li>Databricks</li><li>Kubeflow Workflows</li><li>Prefect</li><li>Vertex AI</li>"];
%     style G text-align:left

This following pages provide information for deployment to, or integration with, the following:

* [Airflow](airflow_astronomer.md)
* [Amazon SageMaker](amazon_sagemaker.md)
* [Amazon EMR Serverless](amazon_emr_serverless.md)
* [AWS Step functions](aws_step_functions.md)
* [Azure](azure.md)
* [Dask](dask.md)
* [Databricks](./databricks/index.md)
* [Kubeflow Workflows](kubeflow.md)
* [Prefect](prefect.md)
* [Vertex AI](vertexai.md)

``` {warning}
We also have legacy documentation pages for the following deployment targets, but these have not been tested against recent Kedro releases and we cannot guarantee them:

* for [Argo Workflows](argo.md)
* for [AWS Batch](aws_batch.md)
```



```{toctree}
:maxdepth: 1
:hidden:

single_machine
distributed
airflow_astronomer
amazon_sagemaker
amazon_emr_serverless
aws_step_functions
azure
dask
databricks/index
kubeflow
prefect
vertexai
argo
aws_batch
```