ai-cost-optimizer

name	ai-cost-optimizer
description	Save 40-70% on AI costs with intelligent multi-LLM routing. Automatically selects the optimal model based on task complexity across 40+ models from 8 providers.
license	MIT

AI Cost Optimizer

🎯 Overview

Stop overspending on AI. The AI Cost Optimizer automatically routes your prompts to the most cost-efficient model based on task complexity, saving 40-70% on API costs while maintaining quality.

Why This Matters

• Developer Pain: AI costs can escalate from $50 to $5,000/month without warning
• Current Problem: Manual model selection is tedious and error-prone
• Our Solution: Intelligent routing that uses GPT-4 only when necessary, free models when possible

Key Features

✅ Smart Routing: Automatically analyzes prompt complexity (0.0-1.0 score) and selects optimal model ✅ Multi-Provider: Access 40+ models from Anthropic, Google, Cerebras, DeepSeek, OpenRouter, and more ✅ Cost Tracking: Real-time monitoring with per-request cost breakdown ✅ Budget Management: Set limits and receive alerts at 50%, 80%, 90% thresholds ✅ Transparent Pricing: See exact costs before and after each request ✅ Zero Lock-in: Works with your existing API keys, no proprietary wrappers

📋 Prerequisites

1. Deploy AI Cost Optimizer Service

The FastAPI service must be running (locally or on RunPod).

Quick Start (Local):

# Clone repository
git clone https://github.com/yourusername/ai-cost-optimizer.git
cd ai-cost-optimizer

# Setup environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Configure providers
cp .env.example .env
# Edit .env and add at least one API key

# Start service
python app/main.py

Service runs at http://localhost:8000

RunPod Deployment (Recommended for production): See Deployment Guide

2. API Keys (Choose At Least One)

• Free Option: Google Gemini (no API key cost, 1M free requests/month)
• Anthropic: Claude models ($3-15/M tokens)
• Cerebras: Ultra-fast Llama ($0.10-0.60/M tokens)
• DeepSeek: Chinese LLM specialist ($0.14-0.28/M tokens)
• OpenRouter: Gateway to 100+ models
• HuggingFace: Open source models

🚀 Installation

Step 1: Install the Skill

Install from Claude Desktop Skills Marketplace or download the .zip package.

Step 2: Configure MCP Server

Add to your Claude Desktop config:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "ai-cost-optimizer": {
      "command": "python",
      "args": ["/absolute/path/to/ai-cost-optimizer/mcp/server.py"],
      "env": {
        "COST_OPTIMIZER_API_URL": "http://localhost:8000"
      }
    }
  }
}

For RunPod deployment, update the URL:

"COST_OPTIMIZER_API_URL": "https://your-pod-id.runpod.io"

Step 3: Restart Claude Desktop

Completely quit and restart Claude Desktop.

💡 Usage

Once installed, Claude has access to 5 powerful tools:

1. Complete Prompt with Smart Routing

Command: Ask Claude to route your prompt through the optimizer

Example:

Please use the cost optimizer to answer: "What is quantum entanglement?"

Response:

Response: Quantum entanglement is a phenomenon where...

Cost Analysis:
• Provider: google
• Model: gemini-2.0-flash-exp
• Cost: $0.000000 (FREE!)
• Complexity Score: 0.25
• Tokens: 12 input / 87 output

💰 Saved ~$0.002 vs GPT-4

2. Check Model Costs

Command: View all available models and pricing

Example:

Show me all available models and their costs

Response:

# Available Models and Pricing

## GOOGLE
**gemini-2.0-flash-exp**
• Input: $0.00 per 1M tokens
• Output: $0.00 per 1M tokens
• Context Window: 1,000,000 tokens

**gemini-1.5-pro**
• Input: $1.25 per 1M tokens
• Output: $5.00 per 1M tokens
• Context Window: 2,000,000 tokens

## ANTHROPIC
**claude-3-5-sonnet**
• Input: $3.00 per 1M tokens
• Output: $15.00 per 1M tokens
• Context Window: 200,000 tokens

[... full model list ...]

Total Models Available: 24

3. Get Recommendation Without Executing

Command: Analyze complexity and get cost estimate before running

Example:

Analyze this prompt and recommend a model:
"Write a comprehensive technical architecture document for a distributed system"

Response:

Recommendation Analysis

Complexity Assessment:
• Complexity Score: 0.82
• Tier: Premium

Recommended Model:
• Provider: anthropic
• Model: claude-3-5-sonnet
• Estimated Cost: $0.045

Reasoning:
High complexity - using premium model for best quality

Token Estimate:
• Input: ~85 tokens
• Expected Output: 4000 tokens max

Note: This is an estimate. Use complete_prompt to execute.

4. Query Usage Statistics

Command: Monitor your spending and usage

Example:

What's my AI spending this month?

Response:

Usage Statistics (30 days)

Overview:
• Total Cost: $12.45
• Total Requests: 156
• Remaining Budget: $37.55
• Budget Utilization: 24.9%

Models Used:
• gemini-1.5-flash: 120 requests
• claude-3-5-sonnet: 24 requests
• deepseek-chat: 12 requests

5. Set Budget

Command: Configure spending limits and alerts

Example:

Set my monthly AI budget to $50 with alerts at 50%, 80%, and 90%

Response:

Budget Updated Successfully

• User: default
• Monthly Limit: $50.00
• Alert Thresholds: 50%, 80%, 90%

You will receive alerts when spending reaches these thresholds.

🎨 Cost Tiers

The optimizer automatically categorizes prompts into tiers:

Tier	Complexity	Example Tasks	Models	Cost/M Tokens
Free	0.0-0.2	Simple facts, definitions, basic Q&A	Gemini 2.0 Flash	$0
Cheap	0.2-0.4	Code snippets, summaries, translations	Cerebras, DeepSeek, Haiku	$0.1-1
Medium	0.4-0.7	Analysis, complex explanations, refactoring	Gemini Pro, Sonnet	$1-5
Premium	0.7-1.0	Architecture design, research papers, complex code	Opus, GPT-4	$5-75

📊 Real-World Savings Examples

Example 1: Daily Q&A (100 requests/day)

Without Optimizer:

• All requests to GPT-4
• Cost: ~$150/month

With Optimizer:

• 80% routed to free/cheap models
• 20% to premium models
• Cost: ~$30/month
• Savings: $120/month (80%)

Example 2: Code Assistant (50 requests/day)

Without Optimizer:

• All requests to Claude Sonnet
• Cost: ~$90/month

With Optimizer:

• Simple syntax: Free models
• Complex architecture: Premium models
• Cost: ~$35/month
• Savings: $55/month (61%)

Example 3: Content Writing (200 requests/day)

Without Optimizer:

• Mix of GPT-3.5 and GPT-4
• Cost: ~$200/month

With Optimizer:

• Intelligent routing by complexity
• Cost: ~$60/month
• Savings: $140/month (70%)

⚙️ Configuration

Environment Variables (Service)

Configure in the AI Cost Optimizer service .env:

# Provider API Keys (configure at least one)
GOOGLE_API_KEY=your-key-here
ANTHROPIC_API_KEY=sk-ant-...
CEREBRAS_API_KEY=...
DEEPSEEK_API_KEY=...
OPENROUTER_API_KEY=sk-or-v1-...

# Budget
DEFAULT_MONTHLY_BUDGET=100.00

# Database (automatic in Docker)
DATABASE_URL=sqlite:///./optimizer.db

MCP Server Configuration

Adjust in claude_desktop_config.json:

{
  "mcpServers": {
    "ai-cost-optimizer": {
      "env": {
        "COST_OPTIMIZER_API_URL": "http://localhost:8000",
        "COST_OPTIMIZER_API_KEY": ""
      }
    }
  }
}

🔧 Troubleshooting

Issue: "Cannot connect to AI Cost Optimizer service"

Solution: Ensure the service is running:

cd ai-cost-optimizer
python main.py

Verify at: http://localhost:8000/health

Issue: "No providers available"

Solution: Add at least one API key to .env:

GOOGLE_API_KEY=your-key-here

Restart the service.

Issue: MCP server not appearing in Claude Desktop

Solutions:

1. Use absolute paths, not relative
2. Verify Python is in PATH
3. Check Claude Desktop logs
4. Restart Claude Desktop completely

Issue: "Budget exceeded"

Solution: Increase budget or check usage:

curl http://localhost:8000/v1/usage

Or ask Claude: "What's my current AI spending?"

🚢 Deployment to RunPod

For production deployment:

1. Build Docker Image:

docker build -t ai-cost-optimizer:latest .
docker tag ai-cost-optimizer:latest your-dockerhub-username/ai-cost-optimizer:latest
docker push your-dockerhub-username/ai-cost-optimizer:latest

2. Deploy on RunPod:

• Go to https://www.runpod.io/
• Deploy Custom Container
• Use your Docker image
• Configure environment variables (API keys)
• Add 5GB persistent volume at /data

3. Update Claude Desktop Config:

"COST_OPTIMIZER_API_URL": "https://your-pod-id.runpod.io"

See full deployment guide.

🎯 Advanced Usage

Force Specific Provider

Use Claude Opus specifically for this complex analysis:
[your prompt]

The tool will respect your override while still tracking costs.

Per-Request Budget Limits

Complete this with a maximum budget of $0.01:
[your prompt]

Multiple Users

Track spending per user by setting user_id in requests.

📚 Resources

• GitHub: https://github.com/yourusername/ai-cost-optimizer
• Documentation: Full README and API docs
• Issues: Bug reports and feature requests
• Deployment Guide: RunPod and Railway instructions

🤝 Contributing

Contributions welcome! Areas of interest:

• Additional provider integrations
• Improved complexity analysis algorithms
• Cost prediction models
• UI dashboard (Streamlit/React)

📄 License

MIT License - Free for personal and commercial use

🔮 Roadmap

v1.1 (Next Month):

• Streamlit dashboard for usage analytics
• Cost prediction before execution
• Provider health checking and failover

v1.2 (Q2):

• Advanced routing with ML-based complexity analysis
• Team collaboration features
• Webhook alerts for budget thresholds

v2.0 (Q3):

• Response quality scoring
• A/B testing across models
• Cost vs quality optimization curves

💬 Community

Join our community:

• Discord: [Your Discord Server]
• Twitter: [@ai_cost_optimizer]
• LinkedIn: [Your Profile]

🙏 Acknowledgments

Built with:

• FastAPI for the API backend
• MCP SDK for Claude Desktop integration
• Anthropic, Google, Cerebras, and other LLM providers

Special thanks to the AI developer community for feedback and testing!

---

Save money. Ship faster. Let AI routing handle the complexity.

Install now and start optimizing your AI costs! 🚀