Building Your Complete Infrastructure

Reference Architecture

Here's the complete stack, layer by layer. This is the architecture that Like One runs on — proven in production, affordable for indie developers, and scalable when growth demands it.

Frontend: Next.js on Vercel. Auto-deploys from GitHub. Edge middleware for auth checks and rate limiting. Streaming responses for AI-generated content so users see results immediately.

Backend / API: Supabase edge functions. Serverless, auto-scaling, close to the database. These handle AI orchestration — receiving requests, checking caches, calling providers, and returning results.

Database: PostgreSQL on Supabase with pgvector enabled. One database for application data, vector embeddings, operation logs, and cached responses. Row-level security for multi-tenant isolation.

AI Layer: Tiered provider setup. Free embeddings via HuggingFace. Mid-tier model for simple tasks. Flagship model for complex reasoning. Semantic cache in front of everything.

Monitoring: Structured logs in a dedicated Supabase table. Cost tracking per operation. Alerts via cron-triggered edge functions to Slack or email.

What to Build First

Don't try to build everything at once. This is the order that minimizes rework and gets you to production fastest.

Week 1: Foundation. Set up your Vercel project and Supabase database. Deploy a basic app that serves pages. Confirm your CI/CD pipeline works — push to main, see it live.

Week 2: AI Integration. Add your first AI API call through a Supabase edge function. Store the API key in environment variables. Add basic logging — every call writes to your operations log table.

Week 3: Vector Search. Enable pgvector. Create your embeddings table. Build a basic RAG pipeline: embed content, store vectors, query by similarity, inject context into your AI prompts.

Week 4: Hardening. Add rate limiting, input validation, and output checking. Implement response caching. Set up cost alerts. Write your first post-deploy smoke test.

After four weeks, you have a production-grade AI infrastructure. Everything after this is optimization and scaling — which you do when you need it, not before.

Mistakes Everyone Makes

Over-engineering on day one. You don't need Kubernetes, multi-region deployment, or a microservices architecture to serve your first 1,000 users. Start simple. Add complexity when simple breaks.

Ignoring costs until the bill arrives. Set up cost tracking and alerts before you launch, not after your first $500 surprise. The monitoring lesson exists for a reason — do it early.

No caching layer. Every production AI app needs caching. The cost difference between "cache everything possible" and "call the API every time" is the difference between a viable business and a money pit.

Skipping security. Prompt injection isn't theoretical. Data leakage isn't theoretical. API key exposure isn't theoretical. Build security in from the start — retrofitting it is always harder.

Single provider dependency. If your entire app breaks when one AI provider goes down, your architecture is fragile. Even a simple fallback to a cached response is better than showing users an error page.