// stack/ai_product.md

# AI Product MVP Tech Stack

// stack_breakdown

FrontendNext.js (App Router) + Tailwind CSS

BackendNestJS or FastAPI (Python for ML-heavy tasks)

DatabasePostgreSQL + Pinecone (vector store)

AuthSupabase Auth

HostingVercel (frontend) + Railway or Fly.io (backend)

// cost_estimate

USD range $6,000–$18,000

INR range ₹5.6L–₹16.8L

Timeline 10–16 weeks

// why_this_stack[]

## Why This Stack

01.FastAPI is the fastest path to a Python-based ML pipeline with async support and auto-generated OpenAPI docs.

02.Pinecone or pgvector handles semantic similarity search without managing embedding infrastructure yourself.

03.Streaming responses via Server-Sent Events give users instant feedback — critical for LLM UX.

// mvp_features[]

—Prompt-based interface with streaming output

—Document ingestion and RAG (Retrieval-Augmented Generation)

—Usage tracking and per-user token quotas

—Conversation history with semantic search

—Feedback loop (thumbs up/down) to improve prompts

// risk_flags[]

!LLM API costs compound fast — implement token budgets and caching before launch, not after.

!Prompt injection is a real attack surface — sanitise all user input that reaches your system prompt.

Get a scope, timeline, and cost estimate tailored to your specific AI Product project — free, no sign-up.

// related_resources