Building AI Startups

Build vs Buy Framework

from dataclasses import dataclass
from typing import List, Dict
from enum import Enum

class Decision(Enum):
    BUILD = "build"
    BUY = "buy"
    PARTNER = "partner"

@dataclass
class BuildBuyAnalysis:
    component: str
    is_core_differentiator: bool
    has_team_expertise: bool
    time_constraint: str
    cost_factor: float
    recommendation: Decision

class BuildBuyFramework:
    def __init__(self):
        self.components = []
    
    def analyze_component(
        self,
        component: str,
        is_core: bool,
        has_expertise: bool,
        time_months: int,
        build_cost: float,
        buy_cost: float
    ) -> BuildBuyAnalysis:
        score = 0
        
        if is_core:
            score += 3
        if has_expertise:
            score += 1
        
        if time_months > 6:
            score -= 2
        
        if build_cost > buy_cost * 3:
            score -= 2
        
        if score >= 2:
            recommendation = Decision.BUILD
        elif score <= -1:
            recommendation = Decision.BUY
        else:
            recommendation = Decision.PARTNER
        
        return BuildBuyAnalysis(
            component=component,
            is_core_differentiator=is_core,
            has_team_expertise=has_expertise,
            time_constraint=f"{time_months} months",
            cost_factor=build_cost / buy_cost,
            recommendation=recommendation
        )
    
    def generate_report(self) -> str:
        report = "# Build vs Buy Analysis\n\n"
        for analysis in self.components:
            report += f"## {analysis.component}\n"
            report += f"- **Recommendation:** {analysis.recommendation.value}\n"
            report += f"- **Core Differentiator:** {analysis.is_core_differentiator}\n"
            report += f"- **Cost Factor:** {analysis.cost_factor:.1f}x\n\n"
        return report

framework = BuildBuyFramework()
analysis = framework.analyze_component(
    component="LLM Inference",
    is_core=False,
    has_expertise=False,
    time_months=2,
    build_cost=50000,
    buy_cost=5000
)
framework.components.append(analysis)

MVP Tech Stack

class AIStartupStack:
    def __init__(self):
        self.stack = {
            "foundation_model": {
                "option": "OpenAI API / Claude API",
                "cost": "Pay-per-use",
                "pros": ["Fast to market", "High quality"],
                "cons": ["Vendor lock-in", "Cost at scale"]
            },
            "fine_tuning": {
                "option": "OpenAI Fine-tuning / LoRA",
                "cost": "Training + Inference",
                "pros": ["Customization", "Better performance"],
                "cons": ["Data requirements", "Maintenance"]
            },
            "vector_db": {
                "option": "Pinecone / Weaviate",
                "cost": "Managed service",
                "pros": ["Easy setup", "Scalable"],
                "cons": ["Vendor dependency"]
            },
            "orchestration": {
                "option": "LangChain / LlamaIndex",
                "cost": "Open source",
                "pros": ["Rapid prototyping", "Community"],
                "cons": ["Abstraction overhead"]
            },
            "deployment": {
                "option": "AWS Lambda / Vercel",
                "cost": "Pay-per-use",
                "pros": ["Auto-scaling", "Low ops"],
                "cons": ["Cold starts", "Limitations"]
            }
        }
    
    def estimate_costs(self, monthly_requests: int) -> Dict:
        costs = {
            "api_costs": monthly_requests * 0.01,
            "infrastructure": 100,
            "vector_db": 70,
            "total": 0
        }
        costs["total"] = sum(costs.values())
        return costs

stack = AIStartupStack()
costs = stack.estimate_costs(monthly_requests=100000)

Scaling Strategies

class ScalingStrategy:
    def __init__(self):
        self.stages = {
            "pre_seed": {"users": 100, "focus": "product_market_fit"},
            "seed": {"users": 1000, "focus": "distribution"},
            "series_a": {"users": 10000, "focus": "monetization"},
            "series_b": {"users": 100000, "focus": "optimization"}
        }
    
    def get_recommendations(self, stage: str) -> Dict:
        recommendations = {
            "pre_seed": {
                "infrastructure": "Managed services only",
                "team": "2-3 engineers",
                "model_strategy": "API-based, iterate fast"
            },
            "seed": {
                "infrastructure": "Start self-hosting for cost",
                "team": "5-8 engineers",
                "model_strategy": "Fine-tune top performers"
            },
            "series_a": {
                "infrastructure": "Hybrid cloud",
                "team": "10-15 engineers",
                "model_strategy": "Custom models for differentiation"
            },
            "series_b": {
                "infrastructure": "Multi-region, optimize costs",
                "team": "20+ engineers",
                "model_strategy": "Own models + efficient inference"
            }
        }
        return recommendations.get(stage, {})

strategy = ScalingStrategy()
recommendations = strategy.get_recommendations("seed")

Key Metrics

Best Practices

• Validate problem-solution fit before building
• Use APIs for MVP, build for differentiation
• Focus on data moats and proprietary datasets
• Measure unit economics early
• Build for 10x scale from day one architecturally
• Prioritize distribution over technology