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Here’s how you can go from downloading the open source distribution of OpenOps to starting getting value out of your OpenOps deployment.

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Deploy OpenOps on your local machine

You don’t need a beefy machine for a test deployment. A modern OS like Windows 11, macOS Big Sur or later, or Ubuntu 20.04+ will do (see more on supported operating systems). You’ll also need Docker, so see Docker requirements to make sure you have a compatible version of a Docker client. Run the following command in your terminal to install, update, configure and run OpenOps: 
curl -fsS https://openops.sh/install | sh
Wait for the installation script to complete. When it finishes, it will display the URL of your OpenOps instance (typically http://localhost/), along with the admin username and an autogenerated password.

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Open OpenOps in your browser

Go ahead and open OpenOps in your browser: First start of OpenOps To log in, use the admin credentials that the installation script has displayed.

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Look around

After logging in, the first thing you’ll see is the OpenOps Overview view: Initial screen In the top-left corner, click the Open sidebar icon to expand the main OpenOps navigation menu. In a fresh installation, the following views contain little content, but knowing they exist can be helpful:
  • Workflows will let you keep track of the workflows that you create.
  • Runs will keep a history of executing your workflows, indicating start time and status.
  • Connections will list all permissions you provide to OpenOps for various cloud providers and services, as well as let you create new ones.
  • Tables will store data collected by workflows, as well as any information you enter or import to use in your workflows.
  • Analytics will let you visualize data from OpenOps tables and external data sources, such as CSV files or Google Sheets.

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Run a sample workflow

Back in Overview, OpenOps suggests three sample beginner-level workflows to help you get a feel of the product. Let’s use one of them to collect all available AWS Compute Optimizer recommendations for a variety of AWS resource types, and save the recommendations to an OpenOps table. Click AWS sample workflow: AWS sample workflow When the workflow preview pop-up displays, click Use template in the top right corner: AWS sample workflow - use template You will then see a workflow summary popup that tells you that the workflow requires a connection to AWS: AWS sample workflow summary

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Configure an AWS connection

You could add the connection later, but let’s do it now. Click + Add alongside the required connection. Create AWS connection In the AWS connection form, you need to enter three required parameters: Access Key ID, Secret Access Key and Default Region. To get an access key ID and a secret access key, create an IAM user in the AWS Management Console or use one of your existing IAM users. Make sure that your IAM user has the following permissions to run this workflow: 
ec2:Describe*
sts:GetCallerIdentity
organizations:Describe*
organizations:List*
compute-optimizer:Get*
compute-optimizer:Describe*
lambda:List*
autoscaling:Describe*
If your AWS account doesn’t already have a default region, you can set it in the AWS Management Console, and then enter it in the connection form. Once you’re done, click Save.

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Look around the workflow editor

When the workflow summary popup displays again, this time with a connection defined, click Create workflow. The new workflow now displays in the workflow editor: Workflow editor The steps of the workflow are visualized as a block diagram. The first step is a trigger: a schedule or event that initiates the workflow execution. In this sample workflow, the trigger schedules running the workflow every working day at 7AM UTC. All other steps are actions, and each action represents some kind of automation that the workflow performs. Feel free to click Tree view and Notes in the top menu bar to display a compact view of the workflow and a detailed description of what it does: Tree view and notes Explore the workflow by clicking on blocks that represent its steps. This will display properties of each step in the right pane of the workflow editor: Step properties in the workflow editor

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Test the workflow

Before your workflow goes live, you need to test it. Testing the workflow runs it in full, so you can treat it as an on-demand run in addition to its scheduled runs. At the top of the workflow editor, above the trigger, there’s a Test workflow button. However, right now it’s grayed out, and if you hover over it, OpenOps will ask you to test your trigger first: Test the trigger first To test the trigger, click on its block in the workflow graph. In the trigger’s properties pane, click the Test tab, then click Load Data in the Step output section. As soon as you do, OpenOps will display test results. It looks like it’s all good so far: Trigger test success Now, if you look back at the Test workflow button at the top of the workflow editor, you can see that it’s now active, so go ahead and click it! OpenOps will take a few seconds to run the workflow, and when it finishes, you’ll see the Run Details pane on the left of the workflow editor: Run Details pane This pane displays the status of each workflow step. If you click a step, you can see its inputs and outputs as JSON in the bottom part of the pane. As long as the status of each step is green, your workflow is ready to go, and you can publish it.

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Publish the workflow

To start running the workflow on schedule, you need to publish it. When testing, the workflow editor switches to read-only mode. To get back to editing mode, click Edit on the top right. Once back in the editing mode, the top-right button is now called Publish. Click it, and as soon as you do, your workflow is live! It will now run periodically on the schedule defined by its trigger.

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See the results of your test run

When you tested the workflow, it was a full on-demand run. Some of the workflow steps fetched AWS Compute Optimizer recommendations for various AWS resource types, and other steps recorded these recommendations to an OpenOps table. Let’s see what’s been recorded to the table. Click the book icon on the top left to show the sidebar. In the sidebar, click Tables: Tables in the sidebar You’ll see a list of all predefined tables under the heading OpenOps dataset. Click the Opportunities table. You’re going to see a table that lists the saving opportunities that the workflow run revealed, including USD estimates: Opportunities

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Get templates for your future workflows

The sample AWS workflow that you’ve just tested and published provides just a quick glance of what OpenOps can do. In real FinOps scenarios, you would want your workflows to do more: for example, find owners of unused resources, request their decision on whether to delete or keep resources, and if they decide to delete, then execute IaC automations and create GitHub pull requests. Fortunately, OpenOps provides dozens of real-world FinOps templates that you can base your workflows on. To get hold of them, in the sidebar, click Overview. Then click Explore templates in the top-right menu bar to see the Templates catalog: Templates catalog By default, the catalog contains six templates. To view more, click Explore more and sign up for a free OpenOps Cloud account. As a result, the Templates catalog in your OpenOps installation will be extended to show all available templates: OpenOps Templates Catalog after signing up with OpenOps Cloud You can click any template in the catalog to see its full description, a preview diagram visualizing the workflow steps defined in the template, and the integrations that the template uses: CloudWatch log retention enforcement template If you find a template useful, you can click Use template and create a workflow based on the template, just like you did earlier with the sample AWS workflow.

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Connect OpenOps to your AI provider

Building steps in OpenOps workflows is usually intuitive. Still, when you need to enter a cloud-specific command, write an SQL query, or add a custom script, it can be helpful to get assistance. OpenOps lets you connect your preferred LLM provider, such as OpenAI or Anthropic, so you can generate queries, scripts, and CLI commands directly within the workflow editor. At the bottom of the sidebar, click Settings, then AI. Choose your LLM provider, select a model, enter your API key, and click Save. Once connected, any time you edit a step that involves a command, query, or custom code, you can click Generate with AI to prompt your LLM and generate it for you: AI chat window

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Join the OpenOps community

We hope that you’ve appreciated what OpenOps can do for you to help automate your FinOps practices. As you continue to build new workflows, consider joining the OpenOps community Slack. By doing so, you’ll get direct support from the OpenOps team, collaborate with other FinOps professionals, and receive help in building your workflows. You’re also welcome to share your feedback to help shape the future of OpenOps.
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