Membangun Produk AI SaaS

Memadukan semua konsep menjadi produk yang bisa dijual. Architecture, pricing, scaling, dan bisnis model AI SaaS.

Architecture

Frontend (React/Next.js)
  ↕ REST/WebSocket
Backend (Node.js/Laravel)
  ├── Auth & billing (Stripe)
  ├── AI orchestration layer
  │   ├── OpenAI / Anthropic API
  │   ├── Vector DB (Pinecone/pgvector)
  │   └── Cache (Redis)
  ├── Job queue (background processing)
  └── Database (PostgreSQL)

Pricing Model

• Credit-based — User beli credits, setiap AI action habiskan N credits. Paling fair, predictable revenue.
• Tier-based — Free (50 queries/bulan), Pro (1000), Enterprise (unlimited). Simple tapi hard to balance.
• Usage-based — Bayar per token. Transparent tapi unpredictable bills.

Key Engineering Decisions

• Streaming — Always stream responses. Non-negotiable for UX.
• Queue heavy tasks — Document processing, batch embeddings → background job
• Cache aggressively — Semantic cache untuk frequent queries
• Model fallback — Primary model down? Fallback ke alternatif
• Token tracking — Track usage per user untuk billing dan limits

Common Pitfalls

• Cost surprises — Satu user spam AI → bill $500. Selalu set limits.
• Latency — AI call 2-10 detik. Precompute apa yang bisa, stream sisanya.
• Hallucination — User trust AI output = risiko. Selalu sertakan disclaimer.
• Vendor dependency — OpenAI down = app down. Abstract provider layer.