Technical Architecture for AI Startups

Phase 1: Prototype (0-100 users)

Start simple and validate assumptions:

Use OpenAI/Anthropic APIs directly

Simple Python/Node.js backend

SQLite or basic Postgres

Vercel/Railway for hosting

Cost: ~$100-500/month

Phase 2: Early Product (100-10K users)

Frontend (Next.js/Vercel)
    ↓
API Layer (FastAPI/Node.js)
    ↓
LLM Router (model selection logic)
    ↓
┌─────────┬──────────┬──────────┐
OpenAI   Anthropic  Open-Source
                    (Ollama/vLLM)

Add:

Response caching (Redis)

Queue for async jobs (BullMQ/Celery)

Structured logging

Basic monitoring

Cost: ~$500-2000/month

Phase 3: Growth (10K-100K users)

Key additions:

Vector database (Pinecone/Qdrant)

ML feature store

A/B testing infrastructure

Model monitoring

python
Smart model routing
def route_llm_request(request: LLMRequest) -> str:
    if request.complexity_score < 0.3:
        return "gpt-3.5-turbo"  # Cheap, fast
    elif request.is_cached:
        return cached_response  # Free
    elif request.requires_latest:
        return "gpt-4o"         # Best
    else:
        return "claude-haiku"   # Good balance

Phase 4: Scale (100K+ users)

Consider:

Fine-tuned models for your use case

Self-hosted open-source models

Custom inference optimization

Global CDN for model caching

Cost: $10K+/month, potentially millions

Common Architectural Mistakes

Mistake 1: Over-engineering early

Don't build a custom ML platform when OpenAI API works fine.

Mistake 2: No caching strategy

Semantic caching can reduce API costs by 40-60%.

Mistake 3: Ignoring cold start

LLM API cold starts can add 2-5 seconds. Use connection pooling.

Mistake 4: Blocking on LLM calls

Always use async patterns for LLM calls in production.