Economics of Zero-Cost AI: 40 Platforms for $0.02/hour

const monthlyCosts = {
  hardwareAmortization: {
    totalCost: 7400, // $7,400 total hardware
    months: 60, // 5-year depreciation
    monthly: 123.33
  },
  colocation: 150,
  network: 200,
  electricity: {
    watts: 1200, // Total system draw
    rate: 0.08, // $0.08/kWh industrial rate
    hours: 730, // Monthly hours
    monthly: 70.08
  },
  monitoring: 10, // UptimeRobot + PagerDuty basic
  total: 553.41
};

interface Request {
  type: 'chat' | 'code' | 'embeddings' | 'image';
  complexity: 'simple' | 'medium' | 'complex';
  priority: 'low' | 'medium' | 'high';
}

class ModelRouter {
  private models = {
    'llama3-8b': { cost: 0.001, quality: 85, vram: 8 },
    'llama3-70b': { cost: 0.008, quality: 95, vram: 48 },
    'codellama-13b': { cost: 0.002, quality: 88, vram: 10 },
    'mixtral-8x7b': { cost: 0.006, quality: 92, vram: 48 }
  };

  route(request: Request): string {
    // Simple requests get smaller models
    if (request.complexity === 'simple') {
      return request.type === 'code' ? 'codellama-13b' : 'llama3-8b';
    }

    // High priority gets best model available
    if (request.priority === 'high') {
      return 'llama3-70b';
    }

    // Balance cost and quality
    return request.complexity === 'complex' ? 'mixtral-8x7b' : 'llama3-8b';
  }
}

from transformers import AutoModelForCausalLM, AutoTokenizer
from bitsandbytes import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3-70B",
    quantization_config=quantization_config,
    device_map="auto"
)

class RequestBatcher {
  private batch: Array<Request> = [];
  private batchTimeout: number = 50; // ms
  private maxBatchSize: number = 10;

  async process(request: Request): Promise<Response> {
    return new Promise((resolve) => {
      this.batch.push({ request, resolve });

      if (this.batch.length >= this.maxBatchSize) {
        this.flushBatch();
      } else if (this.batch.length === 1) {
        setTimeout(() => this.flushBatch(), this.batchTimeout);
      }
    });
  }

  private flushBatch() {
    if (this.batch.length === 0) return;

    const currentBatch = this.batch.splice(0);
    const requests = currentBatch.map(item => item.request);
    const resolves = currentBatch.map(item => item.resolve);

    // Process entire batch in single GPU call
    this.processBatch(requests).then(responses => {
      responses.forEach((response, i) => resolves[i](response));
    });
  }
}

const cacheEconomics = {
  totalRequests: 1000000, // 1M requests/month
  cacheHitRate: 0.75, // 75% overall hit rate
  costPerRequest: 0.001,

  withoutCache: 1000000 * 0.001, // $1,000
  withCache: (250000 * 0.001) + (750000 * 0.0001), // $325

  monthlySavings: 675, // 67.5% reduction
  annualSavings: 8100
};

class IntelligentCache {
  private l1Cache = new Map(); // Memory - fastest
  private l2Cache = new Redis(); // Redis - medium
  private l3Cache = new S3(); // Disk - slowest

  async get(key: string): Promise<any> {
    // L1: Memory cache (exact matches)
    if (this.l1Cache.has(key)) {
      return this.l1Cache.get(key);
    }

    // L2: Redis cache (semantic matches)
    const semantic = await this.l2Cache.get(this.semanticKey(key));
    if (semantic) {
      this.l1Cache.set(key, semantic); // Promote to L1
      return semantic;
    }

    // L3: Disk cache (similar patterns)
    const pattern = await this.l3Cache.get(this.patternKey(key));
    if (pattern) {
      this.l2Cache.set(this.semanticKey(key), pattern); // Promote to L2
      return pattern;
    }

    return null;
  }
}

const resourceAllocation = {
  consumer: {
    platforms: 15,
    trafficShare: 0.60, // 60% of total traffic
    avgConcurrentUsers: 500,
    resources: {
      gpu: '40%', // 40% of GPU resources
      ram: '30%',
      storage: '25%'
    }
  },

  business: {
    platforms: 12,
    trafficShare: 0.25, // 25% of total traffic
    avgConcurrentUsers: 100,
    resources: {
      gpu: '35%',
      ram: '40%',
      storage: '50%'
    }
  },

  developer: {
    platforms: 8,
    trafficShare: 0.10, // 10% of total traffic
    avgConcurrentUsers: 50,
    resources: {
      gpu: '15%',
      ram: '20%',
      storage: '20%'
    }
  },

  internal: {
    platforms: 5,
    trafficShare: 0.05, // 5% of total traffic
    avgConcurrentUsers: 25,
    resources: {
      gpu: '10%',
      ram: '10%',
      storage: '5%'
    }
  }
};

class ResourceManager:
    def __init__(self):
        self.total_gpu_memory = 24 * 1024  # MB (2x RTX 3090)
        self.platforms = {}

    def allocate_resources(self, platform_name: str, request_load: float):
        # Calculate required resources based on load
        if request_load < 0.1:  # < 10% capacity
            self.use_small_model(platform_name)
        elif request_load < 0.5:  # < 50% capacity
            self.use_medium_model(platform_name)
        else:  # High load
            self.use_large_model(platform_name)

    def optimize_allocation(self):
        # Rebalance resources every 5 minutes
        total_load = sum(p.current_load for p in self.platforms.values())

        if total_load < 0.5:  # Underutilized
            self.consolidate_platforms()  # Share resources
        elif total_load > 0.9:  # Overutilized
            self.scale_out_platforms()  # Distribute load

const marketAnalysis = {
  totalAddressableMarket: {
    developers: 45000000, // 45M developers worldwide
    businesses: 20000000, // 20M businesses
    conversionRate: 0.02, // 2% expected conversion
    potentialCustomers: 1300000 // 1.3M potential customers
  },

  revenueProjections: {
    year1: {
      customers: 13000, // 1% conversion
      avgARPU: 240, // Weighted average
      revenue: 3120000 // $3.12M
    },
    year2: {
      customers: 65000, // 5% conversion
      avgARPU: 280,
      revenue: 18200000 // $18.2M
    },
    year3: {
      customers: 195000, // 15% conversion
      avgARPU: 320,
      revenue: 62400000 // $62.4M
    }
  }
};

const profitability = {
  fixedCosts: {
    monthly: 553.41, // Infrastructure only
    annually: 6640.92
  },

  variableCosts: {
    perCustomer: 0.50, // Support, billing, etc.
    percentageOfRevenue: 0.001 // 0.1% of revenue
  },

  breakEvenPoint: {
    customersNeeded: 2, // Just 2 customers to break even
    monthlyRevenue: 98, // $49/month plan
    annualRevenue: 1176
  }
};

const environmentalImpact = {
  dailyEnergyUsage: {
    servers: 28.8, // kWh per day
    cooling: 8.6, // 30% additional for cooling
    total: 37.4 // kWh total
  },

  monthlyCarbonFootprint: {
    kwh: 1122, // kWh per month
    carbonIntensity: 0.4, // kg CO2/kWh (US grid average)
    co2Kg: 449, // kg CO2 per month
    co2Tons: 0.449 // Tons CO2 per month
  },

  comparison: {
    cloudAlternative: 1500, // kWh for equivalent cloud setup
    reduction: 378, // kWh saved
    percentageReduction: 25.2 // % reduction vs cloud
  }
};

const growthModel = {
  platforms: {
    current: 40,
    year1: 80, // 2x growth
    year2: 200, // 2.5x growth
    year3: 500 // 2.5x growth
  },

  costs: {
    current: 553.41,
    year1: 653.41, // +$100 for scaling
    year2: 853.41, // +$200 for scaling
    year3: 1253.41 // +$400 for scaling
  },

  costPerPlatform: {
    current: 13.84,
    year1: 8.17, // 41% reduction
    year2: 4.27, // 48% reduction
    year3: 2.51 // 41% reduction
  }
};

# docker-compose.yml for minimal setup
version: '3.8'
services:
  ollama:
    image: ollama/ollama
    ports:
      - "11434:11434"
    volumes:
      - ./models:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

  redis:
    image: redis:alpine
    ports:
      - "6379:6379"

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf