> For the complete documentation index, see [llms.txt](https://docs.pipekit.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pipekit.io/get-started/evaluate-cloud.md).

# Evaluate Pipekit Cloud

A linear 5-minute path from "I want to try Pipekit" to "my first workflow is running."

This quickstart uses the **free-trial cluster** Pipekit provisions for new accounts. If you'd rather use your own cluster, follow [Operate Pipekit](/get-started/operate.md) instead.

## Prerequisites

* A computer running macOS, Linux, or Windows (for the CLI).
* A browser (for the UI).

## 1. Create an account

1. Go to <https://pipekit.io> and click `Get started free`.
2. Complete the signup form, or use SSO.
3. Check your email for the welcome message and click the confirmation link.
4. Log in.

## 2. Create an Organization

Pipekit will take you to the `Org` page on first login.

1. Click `Create an Org`.
2. Give the [Organization](/concepts/organization.md) a name and description.
3. Click `Submit`.

## 3. Provision the free-trial cluster

When prompted to connect a cluster, confirm that you want a Pipekit-hosted free-trial cluster. Provisioning takes 2 to 3 minutes; you'll be redirected to the Pipes page when it's ready.

The free-trial cluster comes pre-configured with Argo Workflows, the [Pipekit Agent](/concepts/pipekit-agent.md), and a working MinIO artifact store. For its limits and configuration, see [Free Trial Cluster](/get-started/free-trial-cluster.md).

## 4. Install the CLI

```bash
# macOS / Linux (Homebrew)
brew install pipekit/tap/cli

# Windows (Scoop)
scoop bucket add pipekit https://github.com/pipekit/scoop.git
scoop install pipekit/cli
```

For other install methods (NixOS, Docker), see [Reference > CLI > Install](/reference/cli.md#installation).

## 5. Log in from the CLI

```bash
pipekit login
```

Follow the prompts to authenticate against the same account you created in step 1.

## 6. Submit your first workflow

Use the [Pipekit Examples](https://github.com/pipekit/examples) repository for a tested sample workflow:

```bash
git clone https://github.com/pipekit/examples.git
cd examples
pipekit submit --cluster-name=<your-free-trial-cluster-name> examples/dag-diamond/workflow.yaml
```

Pipekit creates a [Pipe](/concepts/pipe.md) named after the workflow's `generateName` and submits a [Run](/concepts/run.md). The CLI prints a link to the Run in the UI.

## 7. View the Run

Open the link from step 6 (or go to <https://pipekit.io/pipes>). You'll see:

* The [Run Graph](/using-pipekit/runs/run-graph.md): a DAG view of the workflow's tasks.
* [Pod Logs](/using-pipekit/runs/pod-logs.md) per container, with level detection and search.
* The [submitted YAML](/using-pipekit/runs/workflow-yaml.md).

## What's next

* **Trigger Workflows from Git** instead of the CLI: [Pipes](/using-pipekit/pipes.md) covers GitHub, GitLab, and Bitbucket integration.
* **Schedule Workflows**: see [Cron](/using-pipekit/pipes/cron.md).
* **Author with Python**: see [Reference > Python SDK](/reference/python-sdk.md) (works with [Hera](https://github.com/argoproj-labs/hera)).
* **Add your own cluster** alongside the free trial: [Operate Pipekit](/get-started/operate.md).
* **Set up access control** before inviting teammates: [Access Control](/concepts/access-control.md).

## Best practice

Set a `namespace` and `serviceAccountName` on your workflows. The [Pipekit Examples](https://github.com/pipekit/examples) workflows use `namespace: argo` and `serviceAccountName: argo-workflow`, which match the free-trial cluster's configuration. If you submit a workflow without these set, Pipekit submits to `default` and your workflow will likely fail with permission errors.