1. Server-Side Rendered AI Dashboards for Analytics on Vercel


    Creating server-side rendered (SSR) AI dashboards for analytics on Vercel involves setting up a project on Vercel that responds to HTTP requests by rendering dashboards dynamically, often combining backend data processing with frontend rendering.

    For this purpose, we will need to create a Vercel project configured to deploy a server-side application. We'll assume the SSR application is in a Node.js/Express environment and that it will serve the rendered dashboards. For the sake of this explanation, let's say the code resides in a GitHub repository.

    Here are the steps we'll follow in the Pulumi program:

    1. Creating a Vercel Project: We first declare a Vercel project, specifying the framework and build settings.
    2. Configuring Deployment: Ensure the correct build and output settings for SSR.
    3. Setting Environment Variables: If the project requires any, we can set environment variables for different stages (development, preview, production).
    4. Defining Analytics Dashboards: The actual implementation of AI dashboards isn't within Pulumi’s scope. Still, we lay the infrastructure necessary to host them, considering you have an application that serves these dashboards.

    Below, I'll provide a Pulumi program that sets up the necessary components on Vercel. Remember that to deploy and manage Vercel resources with Pulumi, you will need to have the Vercel CLI installed and configured with your Vercel account.

    I will now demonstrate the Pulumi program to achieve this setup:

    import pulumi import pulumi_vercel as vercel # Replace with the appropriate values github_repository = "your-github-repo" vercel_organization_id = "your-vercel-org-id" vercel_project_name = "ai-dashboards" # Create a Vercel Project vercel_project = vercel.Project("my-vercel-project", name=vercel_project_name, team_id=vercel_organization_id, git_repository={ "repo": github_repository, "type": "github" # You can change this to "gitlab" or "bitbucket" as necessary }, framework="nextjs", # Change this to the appropriate SSR framework you're using build_command="npm run build", # The build command necessary for your project dev_command="npm run dev", # The command used to run a development server output_directory="public", # The output directory of your SSR application after the build root_directory="" # If your project is in a subdirectory of your repo, specify it here ) # From the project creation, we can export the domain names and visibility for reference pulumi.export("vercel_project_name", vercel_project.name) pulumi.export("vercel_project_live_domain", vercel_project.live_domain) pulumi.export("vercel_preview_domain", vercel_project.preview_domain)

    This Pulumi program will create a new Vercel project linked to a specified repository. It is prepared to host a server-side rendering application with the mentioned properties like build command, development command, and output directory. You can add additional configurations, like environment variables, as required for your particular analytics dashboards application.

    Please note that actual coding for the AI dashboards, including the data fetching and rendering logic, will need to be implemented in the application code hosted on GitHub and will be built and deployed through Vercel's CI/CD pipeline when changes are pushed to the configured repository.

    Once this infrastructure is set up, accessing the analytics dashboards will involve sending HTTP requests to the deployed Vercel application, which will render the dashboards server-side and send back the finished HTML for the client to display.