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Build Serverless SaaS MVP: Google AI Studio & Convex Guide

Build Serverless SaaS MVP: Google AI Studio & Convex Guide

Building a Serverless SaaS MVP with Google AI Studio and Convex

This guide details the process of constructing a Minimum Viable Product (MVP) for a Software as a Service (SaaS) application using a combination of free and low-cost tools. The focus is on rapid development and iteration, enabling engineers to quickly deploy a functional application for user testing and validation. The core technologies employed are Google AI Studio for AI model integration and application scaffolding, and Convex for a serverless backend and database. Deployment is facilitated through DigitalOcean for cost-effective hosting.

The objective is to demonstrate how to move from an initial concept to a deployable MVP within a compressed timeframe, emphasizing the ability to iterate based on early user feedback. This approach minimizes upfront investment, allowing for validation of product-market fit before committing significant resources.

Leveraging Google AI Studio for Application Scaffolding

Google AI Studio serves as the initial entry point for prototyping AI-driven applications. It provides a visual interface to design and experiment with generative AI models, specifically leveraging Google’s Gemini API.

Initial Prompting and Design Generation

The process begins with defining the core functionality of the application through a simple, descriptive prompt within Google AI Studio.

  1. Access Google AI Studio: Navigate to astudio.google.com.
  2. Initiate Project Creation: Click the “Build” button.
  3. Describe the Application: Provide a concise description of the desired MVP. For example, an application that generates product images based on user input.
  4. Define Core Features: Identify and specify the essential features required for the MVP. This could involve input fields for product descriptions, image generation parameters, and user authentication.
  5. Generate Application Structure: Google AI Studio translates the prompt into a foundational application structure. This typically involves generating a React-based frontend.

The emphasis at this stage is on simplicity. Overly complex prompts can lead to convoluted code generation. The generated code is a starting point, designed to be iterated upon.

Understanding Google AI Studio’s Architecture

It is important to recognize that Google AI Studio, while powerful, is a streamlined interface. It acts as a React wrapper around the Gemini API. This means the generated code is fundamentally a React application that interacts with Google’s AI models.

Integrating with Convex for Backend and Database

Convex provides a serverless backend-as-a-service (BaaS) solution that simplifies the development of real-time applications. It offers a managed database, authentication, and real-time data synchronization, all accessible through a unified API.

Setting Up a Convex Project

  1. Initialize a Convex Project:
    • Visit the Convex documentation for starting a new project. Search for “start new convex project next.js”.
    • Locate and follow the instructions for the Next.js quick start guide.
    • Execute the command to create a new Next.js project. For example: 
      
npx create-next-app@latest my-app --ts --eslint --app
    • Use recommended settings during the project creation process.
  2. Install Convex Dependencies:
    • Navigate into the newly created project directory: 
      
cd my-app
    • Install the Convex library: 
      
npm install convex
  3. Initialize Convex Development Environment:
    • Run the Convex development command: 
      
npx convex dev
    • When prompted to create a new project, provide a descriptive name, such as product-image-ai.
    • Select “Cloud deployment” if available. Convex provides a generous free tier, typically offering around 20 projects for free.

Integrating Authentication with Clerk

User authentication is a critical component of most SaaS applications. Clerk is a popular identity management solution that integrates seamlessly with Convex and Next.js.

  1. Set Up Clerk with Convex:
    • Search for “setup clerk convex” in your preferred search engine.
    • Navigate to the documentation for integrating Clerk with Next.js.
  2. Create a Clerk Application:
    • Sign up for a Clerk account at clerk.com if you do not already have one.
    • Create a new application within Clerk. Name it appropriately, for instance, AI Product Images.
    • Configure authentication methods. Email and Google authentication are common choices.
  3. Configure JWT Template:
    • Within your Clerk application settings, navigate to “Configure”.
    • Search for “JWT” and select the option to add a new template.
    • Name the template convex (lowercase).
    • Select the “Convex” template from the available options.
    • Save the template.
  4. Obtain Clerk Credentials:
    • After saving the JWT template, you will need to copy two essential pieces of information: the JWT public key and the JWT issuer URL. These are crucial for establishing the connection between Clerk and Convex.
    • It is recommended to store these credentials securely. For development, a local .env.local file is suitable.

    Example .env.local content:

    
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=pk_test_YOUR_PUBLISHABLE_KEY
CLERK_SECRET_KEY=sk_test_YOUR_SECRET_KEY
NEXT_PUBLIC_CLERK_JWT_ISSUER_URL=https://YOUR_CLERK_INSTANCE.clerk.dev
CONVEX_AUTH_URL=https://YOUR_CLERK_INSTANCE.clerk.dev
CONVEX_WORKSPACE_URL=YOUR_CONVEX_WORKSPACE_URL

    Note: Replace placeholders with your actual Clerk and Convex credentials.

  5. Configure Convex for Clerk Authentication:
    • The Convex documentation will guide you on how to configure your Convex backend to use the Clerk JWT. This typically involves setting environment variables within your Convex project’s configuration.
    • Ensure that the CLERK_SECRET_KEY and NEXT_PUBLIC_CLERK_JWT_ISSUER_URL are correctly set in your Convex environment.

Porting Generated Code to the Convex Project

The application structure generated by Google AI Studio needs to be integrated into the Convex-powered Next.js project.

  1. Download and Unzip AI Studio Project:
    • If the AI Studio generation process completes successfully, download the generated application.
    • Unzip the downloaded archive.
  2. Transfer Code:
    • Manually transfer the relevant frontend components, styles, and any utility functions from the unzipped Google AI Studio project into your my-app (Convex) project.
    • Pay close attention to how the AI Studio project structures its components and pages. Replicate this structure within your app directory (for Next.js App Router) or pages directory (for Next.js Pages Router).
  3. Adapt for Convex Integration:
    • Modify the transferred frontend code to interact with Convex. This involves:
      • Replacing any direct API calls to external AI services with calls to your Convex backend functions.
      • Integrating Convex’s Realtime API for data synchronization.
      • Using Convex’s authentication hooks and components provided by Clerk.

Addressing Potential Issues

  • Missing Convex Rules File: In some instances, the convex/rules.ts file might not be automatically generated. This file is crucial for defining access control for your Convex actions and queries. You may need to create it manually based on Convex documentation examples.
  • API Key Management: Ensure all necessary API keys (Clerk, potentially others) are correctly configured in your .env.local file and accessible by your Convex backend.

Iterative Development with AI Assistants

The development process can be significantly accelerated by using AI coding assistants like Gemini or Claude. These tools can help with code generation, debugging, and refactoring.

Planning with Opus 4.5 (or Equivalent)

For complex code migration or feature implementation, an advanced AI model can be utilized for planning.

  1. Initiate Planning Mode:
    • If using a tool that supports conversational planning (e.g., a chat interface with an AI model), initiate a planning session.
    • Clearly state the current project structure and the desired outcome. For example, “I have a React project generated by Google AI Studio and a Convex + Next.js project. I need you to plan the porting of the AI Studio project’s functionality into the Convex project, including the implementation of Clerk authentication.”
  2. Provide Context:
    • Feed the AI assistant with relevant code snippets, file structures, and specific requirements. This could include:
      • The structure of the Google AI Studio project.
      • The structure of the Convex project.
      • Details about the Clerk integration steps.
      • Specific functionalities to be migrated.
  3. Review the Plan:
    • Carefully review the AI’s generated plan. Ensure it addresses all requirements and proposes a logical implementation strategy.
    • If the plan is insufficient or inaccurate, refine the prompt and iterate.

Executing with Gemini 3 Flash (or Equivalent)

Once a plan is established, a faster, more efficient AI model can be used for code generation and implementation. Gemini 3 Flash is noted for its speed and coding capabilities.

  1. Continue with a Faster Model:
    • If the planning phase used a more resource-intensive model, switch to a faster one like Gemini 3 Flash for the actual coding. This optimizes for development velocity.
  2. Prompt for Code Generation:
    • Provide specific instructions to the AI model for generating code. For example:
      • “Port the image generation logic from the following component into a Convex action.”
      • “Implement user signup and login using Clerk and Convex mutations.”
      • “Refactor the frontend components to use Convex’s useQuery and useMutation hooks.”
  3. Integrate Generated Code:
    • Copy the generated code snippets into your Convex project.
    • Thoroughly test the integrated code to ensure it functions as expected.

Leveraging Stitch for Design and Code Snippets

Google Stitch (stitch.withgoogle.com) can be used to generate UI designs and corresponding code snippets. This can be a valuable resource for quickly prototyping the frontend.

  1. Generate Design with Stitch:
    • Use the same prompt provided to Google AI Studio in Stitch.
    • Stitch will generate a visual design and potentially corresponding code.
  2. Extract Code:
    • If Stitch provides code, extract relevant components or styling.
    • Integrate these snippets into your Next.js application.
  3. Feed Code to AI Assistants:
    • The code generated by Stitch can be fed to AI coding assistants for further refinement or integration with your Convex backend.

Deployment to DigitalOcean

For deploying the MVP to a live environment, DigitalOcean offers a cost-effective and scalable solution.

Setting Up DigitalOcean Droplets

  1. Create a DigitalOcean Account: If you do not have one, sign up for a DigitalOcean account.
  2. Generate API Token:
    • Navigate to the API section of your DigitalOcean account (usually found in the bottom left corner).
    • Generate a new API token. Assign a descriptive name (e.g., mvp-deployer).
    • Grant “Full access” to the token.
    • Crucially, copy the generated token immediately. This token will not be shown again. Store it securely.
  3. Deploy with CLI:
    • DigitalOcean’s CLI tools can be used to automate droplet creation and application deployment. The specific commands will depend on your application’s build process and infrastructure requirements.
    • The process typically involves configuring the CLI with your API token and then running deployment commands.

    Example conceptual CLI command (actual command may vary):

    
doctl app create --name product-image-ai --region nyc3 --repo-type github --repo-url YOUR_GITHUB_REPO_URL

    Note: This is a simplified example. A more robust deployment would involve setting up a CI/CD pipeline.

Configuring Environment Variables for Deployment

Ensure that all necessary environment variables are configured in your DigitalOcean deployment environment. This includes:

  • Convex Credentials: CONVEX_DEPLOYMENT_KEY
  • Clerk Credentials: NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY, CLERK_SECRET_KEY, NEXT_PUBLIC_CLERK_JWT_ISSUER_URL
  • Database Connection Strings (if applicable): Any other credentials required by your backend services.

Monitoring Deployment Logs

After initiating the deployment, monitor the build logs in DigitalOcean. This will help identify any errors during the build or deployment process.

  • Navigate to the application’s dashboard in DigitalOcean.
  • Select “Build Logs” to view the real-time build output.
  • If the application was built locally and tested thoroughly, deployment issues are less likely, but it is still essential to verify the build process.

Post-Deployment and Iteration

Once the MVP is deployed, the focus shifts to gathering user feedback and iterating on the product.

User Feedback Loop

  • Share the MVP: Provide the deployed application URL to potential users, friends, and testers.
  • Collect Feedback: Actively solicit feedback on functionality, usability, and desired features.
  • Analyze Usage: Monitor user engagement and identify areas for improvement.

Iterative Development Cycle

  • Bug Fixing: Address any bugs or issues reported by users.
  • Feature Enhancement: Implement new features based on user feedback and market analysis.
  • Refinement: Continuously improve the user experience, performance, and design.

Considerations for Future Development

  • Payments: Once user adoption and engagement are validated, integrate payment processing to monetize the application.
  • Domain Name: Purchase a custom domain name to enhance branding and SEO.
  • Advanced SEO: Implement a comprehensive SEO strategy to improve organic visibility.
  • Scalability: As user base grows, ensure the infrastructure can scale accordingly.

Cost Considerations

The primary goal of this approach is to minimize initial costs.

  • Google AI Studio: Free for development and prototyping.
  • Convex: Offers a generous free tier that is typically sufficient for an MVP. Paid tiers are available for scaling.
  • Clerk: Provides a free tier suitable for development and early-stage applications.
  • DigitalOcean: Costs are variable based on droplet size and services used. A basic droplet for an MVP can be as low as $5-$10 per month.

The total cost for hosting a functional MVP can be as low as $5-$10 per month, making it an extremely cost-effective way to validate a business idea. For further insights into building AI-driven applications, consider this guide on building a SaaS MVP with Google AI Studio.