Ship your AI SaaS in days.
FastAPI + Next.js with token billing built in.
Stop rewriting Python models into JavaScript wrappers. Keep your LangChain, DSPy, and PyTorch pipelines in native Python, paired with a Linear-style Next.js 15 frontend, Supabase pgvector RAG, and Stripe metered token billing.
The Production Dashboard
Inspect the real UI components, vector explorer, and telemetry included in the template. No toy demos.
Based on semantic vector matching against your indexed documentation, Q3 customer churn decreased by 14.2% among enterprise tiers, primarily driven by the revised SLA policy introduced in August.
Retrieved contract clause §4.2 affirms that accounts exceeding 50,000 monthly active users maintain automated failover without additional compute surcharges.
Repository File Tree Preview
Clean, modular, and strictly typed code. See how data flows from Next.js 15, is authorized in FastAPI, and streamed back with Stripe metering.
from fastapi import Depends, HTTPException, status
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from jose import jwt, JWTError
from api.core.config import settings
security = HTTPBearer()
async def get_current_user(
credentials: HTTPAuthorizationCredentials = Depends(security)
) -> dict:
"""
Zero-Trust JWT verification dependency.
Validates Supabase JWT signature against SUPABASE_JWT_SECRET.
Never trusts client parameters for user identity.
"""
token = credentials.credentials
try:
payload = jwt.decode(
token,
settings.SUPABASE_JWT_SECRET,
algorithms=["HS256"],
audience="authenticated"
)
user_id: str = payload.get("sub")
if not user_id:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid token payload: missing sub claim"
)
return {
"id": user_id,
"email": payload.get("email"),
"role": payload.get("role", "authenticated"),
"stripe_customer_id": payload.get("user_metadata", {}).get("stripe_customer_id")
}
except JWTError as e:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail=f"Cryptographic JWT verification failed: {str(e)}"
)See the Dual-Stack Engine in Action
Watch how a Next.js 15 client securely triggers a Python FastAPI SSE stream, queries pgvector with RLS, and records metered Stripe token usage in real time.
Why Other Approaches Fall Short for ML Engineers
Node.js boilerplates force you into weak JavaScript AI libraries. Streamlit apps don't look like real B2B software. AgentLaunch gives you the best of both worlds.
| Architecture Feature | Node.js Boilerplates | Streamlit / Reflex | AgentLaunchRecommended |
|---|---|---|---|
| AI / ML Library Ecosystem | Restricted to JS wrappers (LangChain.js lags behind) | Full Python, but trapped in clunky UI | Full native Python 3.12 (LangChain, DSPy, PyTorch, Hugging Face) |
| Frontend UI & User Experience | Modern React/Next.js | Streamlit / Gradio (Looks like internal data tools) | Linear-style Next.js 15 App Router + Tailwind & Framer Motion |
| Billing Model for AI Tokens | Flat monthly fee (High risk of token cost deficit) | No built-in billing | Automated Stripe Metered / Usage-Based Billing per token |
| Streaming Protocol | Custom WebSockets or polling | Session-state polling / rerenders | Sub-50ms Server-Sent Events (SSE) with token live-counter |
| Multi-Tenant Vector Isolation | App-level filtering (vulnerable to prompt leaks) | In-memory FAISS / Chroma (Single tenant) | Postgres pgvector with native Row-Level Security (RLS) |
| Zero-Trust Auth Bridge | NextAuth / Node sessions only | Basic HTTP basic auth or cookies | Supabase JWT validated cryptographically in FastAPI Depends |
Engineered for Separation of Concerns
No messy monolithic compromises. Keep your frontend fast and SEO-ready on Vercel while running your heavy AI workloads on high-throughput Python backends.
Next.js 15 Frontend
App Router & React Server Components. Manages sessions, handles SSR, renders the UI, and opens an SSE stream listener.
SSE & JWT Protocol
Zero-Trust authorization. Supabase JWT passed via Bearer header. Server-Sent Events stream tokens smoothly with sub-50ms TTFT.
FastAPI Python Runtime
Native Python 3.12 ecosystem. Executes LangChain, LlamaIndex, DSPy, or custom PyTorch models with Pydantic v2 validation.
Supabase + pgvector
PostgreSQL 16 with HNSW cosine distance indexing for ultra-fast vector similarity search, enforced by native Postgres RLS policies.
Everything You Need to Monetize AI Models
Engineered specifically for Machine Learning Engineers who want to launch enterprise-grade AI products without reinventing the full-stack wheel.
FastAPI 3.12 & Pydantic V2
Functional, strictly-typed Python backend. Enforces RORO pattern with full input/output validation, zero untyped dicts, and async I/O.
Sub-50ms SSE Streaming
Yields text/event-stream chunks directly to custom Next.js stream consumers. Smooth typing effect without UI blocking or web sockets overhead.
Zero-Trust Supabase JWT
FastAPI verifies Supabase Auth tokens cryptographically via FastAPI Depends injection. No unauthorized access to heavy AI GPU/API workloads.
PostgreSQL pgvector RAG
Store and query document embeddings with HNSW cosine distance indexing. Strictly protected by PostgreSQL native Row-Level Security (RLS).
Stripe Metered Billing
Calculates exact input and output tokens consumed during LLM requests and records metered usage in Stripe before stream closure.
Production Docker & Multi-Cloud
Optimized multi-stage Dockerfiles. Ready for 1-click deployment on Vercel (frontend) and Railway, Fly.io, Modal, or AWS ECS (backend).
Pay Once, Build Forever.
$129 one-time vs. 80+ hours of tedious plumbing ($8,000+ value at $100/hr). Spend your weekend launching your AI model instead of wrestling with JWT headers and SSE buffers.
Full production boilerplate for solo developers and ML engineers shipping their first commercial AI SaaS.
For AI agencies, development shops, and teams building multiple commercial client platforms.
Everything You Need to Know
Honest answers about architecture, licensing, updates, and requirements.