---
title: How to create deployments
sidebarTitle: Create Deployments
description: Learn how to create deployments via the CLI, Python, or Terraform.
---

There are several ways to create deployments, each catering to different organizational needs.

## Create deployments with static infrastructure

### Create a deployment with `serve`

This is the simplest way to get started with deployments:

```python
from prefect import flow


@flow
def my_flow():
    print("Hello, Prefect!")


if __name__ == "__main__":
    my_flow.serve(name="my-first-deployment", cron="* * * * *")
```

The `serve` method creates a deployment from your flow and immediately begins listening for scheduled runs to execute. 
Providing `cron="* * * * *"` to `.serve` associates a schedule with your flow so it will run every minute of every day.

## Create deployments with dynamic infrastructure

For more configuration, you can create a deployment that uses a work pool.
Reasons to create a work-pool based deployment include:

- Wanting to run your flow on dynamically provisioned infrastructure
- Needing more control over the execution environment on a per-flow run basis
- Creating an infrastructure template to use across deployments

Work pools are popular with data platform teams because they allow you to manage infrastructure configuration across an organization. 

Prefect offers two options for creating deployments with [dynamic infrastructure](/v3/deploy/infrastructure-concepts/work-pools):
- [Deployments created with the Python SDK](/v3/deploy/infrastructure-concepts/deploy-via-python)
- [Deployments created with a YAML file](/v3/deploy/infrastructure-concepts/prefect-yaml)

### Create a deployment with `deploy`

To define a deployment with a Python script, use the `flow.deploy` method.

Here's an example of a deployment that uses a work pool and bakes the code into a Docker image.

```python
from prefect import flow


@flow
def my_flow():
    print("Hello, Prefect!")

if __name__ == "__main__":
    my_flow.deploy(
        name="my-second-deployment",
        work_pool_name="my-work-pool",
        image="my-image",
        push=False,
        cron="* * * * *",
    )
```

To learn more about the `deploy` method, see [Deploy flows with Python](/v3/deploy/infrastructure-concepts/deploy-via-python).

### Create a deployment with a YAML file

If you'd rather take a declarative approach to defining a deployment through a YAML file, use a [`prefect.yaml` file](/v3/deploy/infrastructure-concepts/prefect-yaml).

Prefect provides an interactive CLI that walks you through creating a `prefect.yaml` file:

```bash
prefect deploy
```

The result is a `prefect.yaml` file for deployment creation. 
The file contains `build`, `push`, and `pull` steps for building a Docker image, pushing code to a Docker registry, and pulling code at runtime.
Learn more about creating deployments with a YAML file in [Define deployments with YAML](/v3/deploy/infrastructure-concepts/prefect-yaml).

Prefect also provides [CI/CD options](/v3/deploy/infrastructure-concepts/deploy-ci-cd) for automatically creating YAML-based deployments. 

### Create a deployment with Terraform

import { TF } from "/snippets/resource-management/terraform.mdx"
import { deployments } from "/snippets/resource-management/vars.mdx"

<TF name="deployments" href={deployments.tf} />

### Create a deployment with the API

import { API } from "/snippets/resource-management/api.mdx"

<API name="deployments" href={deployments.api} />


<Tip>
    **Choosing between deployment methods**

    For many cases, `serve` is sufficient for scheduling and orchestration.
    The work pool / worker paradigm via `.deploy()` or `prefect deploy` can be great for complex infrastructure requirements and isolated flow run environments.
    You are not locked into one method and can combine approaches as needed.
</Tip>