Stop burning cash! Claude Code and Gemini CLI should not be competitors; if you are smart, you can see that.
Instead of wondering which one to choose, Claude is super fast and smart, and Gemini CLI is deep and free.
Why not use them together?
You can make them collaborate on your project and be ten times more productive without burning money.
This idea crossed my mind, and I did a project using them together. It was a huge success.
Then I realized it’s better to use either for specific tasks. I even looped in GitHub Copilot, and I was able to do much more work for far less.
In this post, I will guide you through what you can achieve by combining these two and the mindset you should adopt when approaching AI coding.
Choosing the tool should be the least of your worries, as these tools are essentially the same, with subtle differences in speed and performance.
What matters is saving your money.
The Cost Problem With AI Coding
Most developers treat AI coding tools like a marriage. You pick one and stay loyal.
That’s expensive and limiting.
Claude Code costs money per request, whereas Gemini CLI is free, but it can sometimes be slower when handling complex frontend logic.
GitHub Copilot sits in your editor but isn’t comparable to Claude Code, although it’s decent for AI coding and is getting better.
All these tools have superpowers in different areas.
Claude Code crushes frontend work. Fast responses, clean code, and understands UI patterns instantly.
The Gemini CLI excels in backend logic and API design. It thinks deeper about architecture and it’s completely free.
GitHub Copilot fills gaps with documentation and code reviews.
Why wouldn’t you use all three?
Let’s Build Something
I’m going to show you how this works by building a real project called ASE Prompts Manager.
It’s a prompt storage and management app with a FastAPI backend and vanilla JavaScript frontend.
Real enough to demonstrate how these tools collaborate on actual development tasks.
One of my best workflows looks like this: Claude Sonnet 4.5 handles the frontend, Google Gemini 2.0 Flash handles the backend, and Copilot with GPT-4 handles documentation.
My other favourite alternative is to combine both backend and frontend code generation using Claude Sonnet 4.5, while I let Google Gemini handle the documentation and code review.
The strategy depends on your exact situation and budget, but the principle stays the same. Use each tool where it performs best and costs least.
Let me show you how.
Building ASE Prompts Manager
ASE Prompts Manager is a prompt storage and management app with a Vanilla JavaScript frontend.
CRUD operations for prompts — create, read, update, delete.
Claude Code Frontend Development
I fired up Claude Code in the terminal.
Gave it the full prompt with project structure, features, and design requirements.
I'm building ASE Prompts Manager - a web app to store and manage AI prompts.
Build the frontend using vanilla JavaScript with these requirements:
PROJECT STRUCTURE:
- index.html: Main page with header "ASE Prompts Manager"
- styles.css: Modern, clean design with good spacing
- app.js: All client-side logic
FEATURES NEEDED:
1. Add Prompt Form (at top):
- Title input field
- Content textarea (multi-line)
- Category dropdown (select from: Coding, Writing, Analysis, Other)
- "Add Prompt" submit button
2. Prompts Display Section (below form):
- Show all prompts in cards/list format
- Each prompt card displays: title, content preview, category, timestamp
- Edit button (opens form with existing data)
- Delete button (removes prompt)
3. Client-side Logic in app.js:
- Fetch prompts from API endpoint: GET http://localhost:8000/prompts
- Add new prompt: POST http://localhost:8000/prompts
- Update prompt: PUT http://localhost:8000/prompts/{id}
- Delete prompt: DELETE http://localhost:8000/prompts/{id}
- Handle form validation and user feedback
DESIGN:
- Clean, professional interface
- Responsive layout
- Good color contrast
- Smooth transitions
- Mobile-friendly
Keep code simple, well-organized, and production-ready.
Include all these files inside a folder named frontend since we will
build the backend later.
Claude Code got to work immediately. Created a to-do list to track the build. Started generating files.
First came index.html. Clean structure with a header, add a prompt form, and a display section for prompt cards.
Then styles.css — modern design with CSS variables, responsive grid layout, and color-coded category badges. Professional card-based design with smooth transitions.
Finally app.js. Complete client-side logic with full CRUD operations, API integration, form validation, user feedback messages, and XSS protection.
Claude Code finished in under two minutes. Three production-ready files in the frontend folder.
The output was detailed. It listed every feature it implemented:
- Add new prompts with validation
- Edit existing prompts inline
- Delete prompts with confirmation
- Category filtering with color-coded badges
- Responsive grid layout
- Loading states and error messages
I opened the files. The code was clean and well-organized.
Gemini CLI Backend Development
I switched to the Gemini CLI terminal so that we can generate the backend code.
I created this prompt to help me build this within the backend folder.
I'm building ASE Prompts Manager - a web app to store and manage AI prompts.
The frontend is already built in the 'frontend' folder. Now build the FastAPI backend.
PROJECT STRUCTURE:
Create a 'backend' folder containing:
- main.py: FastAPI application with all routes
- models.py: Pydantic models for data validation
- requirements.txt: List all dependencies
BACKEND REQUIREMENTS:
1. Data Model (Pydantic):
- id: unique identifier (UUID or integer)
- title: string (min 3 characters)
- content: string (min 10 characters)
- category: string (Coding, Writing, Analysis, Other)
- created_at: datetime timestamp
- updated_at: datetime timestamp
2. API Endpoints:
- GET /prompts - Return all prompts (sorted by created_at, newest first)
- GET /prompts/{id} - Return single prompt by ID
- POST /prompts - Create new prompt (validate data)
- PUT /prompts/{id} - Update existing prompt
- DELETE /prompts/{id} - Delete prompt by ID
3. Storage:
- Use in-memory storage (Python list or dict)
- Auto-generate IDs for new prompts
- Auto-generate timestamps on create/update
4. CORS Configuration:
- Enable CORS for frontend at http://localhost:8080
- Allow all standard HTTP methods
- Allow credentials
5. Response Format:
- Success responses with proper HTTP status codes (200, 201, 204)
- Error responses with clear messages (404, 400, 422)
- JSON response format for all endpoints
6. Error Handling:
- Validate all incoming data
- Handle missing prompts (404)
- Handle invalid data (422)
- Proper error messages
ADDITIONAL FEATURES:
- Add API documentation (automatic via FastAPI)
- Include startup message with available endpoints
- Add basic logging for requests
Keep code clean, well-organized, and production-ready.
Include all files inside a 'backend' folder since the frontend is already in 'frontend' folder.
I pasted the backend prompt—comprehensive requirements for the FastAPI structure, data models, endpoints, CORS, and error handling.
Gemini CLI processed the request. Then it outlined its plan.
Creating a backend folder with main.py, models.py, and requirements.txt.
It executed step by step:
Step 1: Created the backend directory.
Step 2: Generated three empty files using PowerShell commands.
Step 3: Wrote requirements.txt with fastapi and uvicorn[standard].
Step 4: Built models.py with Pydantic schemas. Category enum, PromptBase, PromptCreate, PromptUpdate, and Prompt models. UUID for IDs, datetime for timestamps, field validation for title and content.
Step 5: Created main.py with the full FastAPI application. CORS middleware configured for localhost:8080. In-memory database using a Python dict. All CRUD endpoints with proper HTTP status codes. A startup event that logs available endpoints. Request logging middleware. Custom exception handlers.
The code was thorough, including error handling for missing prompts, validation for all incoming data, and a sample prompt added on startup.
Step 6: Installed dependencies. Gemini CLI ran pip install automatically. Downloaded and installed FastAPI, Uvicorn, Pydantic, and all required packages.
Step 7: Started the server. Uvicorn launched with a——reload for auto-restarts.
The terminal showed all available endpoints:
GET /prompts
POST /prompts
GET /prompts/{prompt_id}
PUT /prompts/{prompt_id}
DELETE /prompts/{prompt_id}
Application startup complete.
The entire backend build took less than three minutes. Gemini CLI handled everything from file creation to server deployment.
Complete Project Structure
I looked at VS Code explorer. Two folders sitting side by side:
frontend/ with index.html, styles.css, app.js
backend/ with main.py, models.py, requirements.txt
Claude Code built the frontend and Gemini CLI built the backend
My Terminal Workflow
For this kind of approach, I keep three terminal windows open:
Terminal 1: Where I ran Claude Code and later Gemini CLI. Switched between them as needed.
Terminal 2: Backend server running uvicorn. Logs showing API requests in real-time.
Terminal 3: Frontend server serving the HTML files.
This setup lets me see everything happening at once.
Documentation with GitHub Copilot
With the frontend and backend complete, I turned to GitHub Copilot for documentation.
Copilot sits directly in VS Code.
As an example, I opened main.py and highlighted the
log_requestsmiddleware function. Asked Copilot in the chat panel: “Can you add a comment at the top of this line that explains what the code does?”
Copilot analyzed the function and generated a clear explanatory comment. One click to accept. Comment added.
I moved to the get_all_prompts() endpoint. Asked for documentation on what the endpoint returns and how it sorts the data.
Copilot provided a detailed docstring explaining the function’s purpose, return type, and sorting logic. Accepted. Done.
Within minutes, the entire codebase had proper documentation: function descriptions, parameter explanations, and return type annotations.
And that’s how I use Copilot for other tasks where I don’t want to burn my Claude tokens.
Final Thoughts
This workflow flips the AI coding paradigm; instead of committing to one expensive tool and watching costs pile up, you orchestrate multiple tools like a team.
Claude Code builds your frontend in two minutes with production-ready code. Gemini CLI architects your backend in three minutes without charging a cent. GitHub Copilot handles documentation while you work.
The ASE Prompts Manager proved this works — a fully functional app in under 10 minutes for $0.50 or less. This applies to a huge project, which means more cost savings
Stop treating AI coding tools as competitors you must choose between. Start treating them as specialists you assign work to. Connect the pieces, test the integrations, ship the product.
That’s the workflow that saves money and builds faster.
I know you have your unique workflow that includes using multiple tools together. I would like to hear how you are doing it, share your thoughts, and experience in the comments below.
Claude Course Course
Every day, I’m working hard to build the ultimate Claude Code course, which demonstrates how to create workflows that coordinate multiple agents for complex development tasks. It’s due for release soon.
It will take what you have learned from this article to the next level of complete automation.
New features are added to Claude Code daily, and keeping up is tough.
The course explores Agents, Hooks, advanced workflows, and productivity techniques that many developers may not be aware of.
Once you join, you’ll receive all the updates as new features are rolled out.
This course will cover:
- Advanced subagent patterns and workflows
- Production-ready hook configurations
- MCP server integrations for external tools
- Team collaboration strategies
- Enterprise deployment patterns
- Real-world case studies from my consulting work
