Building a standard CRUD app is easy. Building an AI Wrapper is easy.
But building a scalable AI SaaS, one that handles thousands of concurrent users, manages long-running LLM tasks, and doesn’t bankrupt you on GPU costs, is an engineering challenge.
Over the last year, I’ve refined a "Blueprint" that I use for almost every heavy-duty AI project. It separates the "fast" parts of the app from the "slow" AI inference layers.
If you are building the next big AI tool, here is the stack you should be looking at.
1. The Frontend: Speed is Perception
Stack: Next.js (App Router) + Tailwind CSS + Shadcn UI.
When a user clicks "Generate," they expect instant feedback. But LLMs are slow. They take 5, 10, sometimes 30 seconds to reply.
The Trick: Optimistic UI and S…
Building a standard CRUD app is easy. Building an AI Wrapper is easy.
But building a scalable AI SaaS, one that handles thousands of concurrent users, manages long-running LLM tasks, and doesn’t bankrupt you on GPU costs, is an engineering challenge.
Over the last year, I’ve refined a "Blueprint" that I use for almost every heavy-duty AI project. It separates the "fast" parts of the app from the "slow" AI inference layers.
If you are building the next big AI tool, here is the stack you should be looking at.
1. The Frontend: Speed is Perception
Stack: Next.js (App Router) + Tailwind CSS + Shadcn UI.
When a user clicks "Generate," they expect instant feedback. But LLMs are slow. They take 5, 10, sometimes 30 seconds to reply.
The Trick: Optimistic UI and Streaming.
I never make the user wait for a loading spinner. I stream the response token-by-token using Vercel AI SDK or similar libraries. It makes a 5-second wait feel like 500ms.
2. The API Layer: The Traffic Cop
Stack: TypeScript (Node.js or Bun) or Go.
I don’t let my Python AI services touch the user directly.
Why? Because Python is great for AI, but Node/Go are better at handling thousands of open connections (WebSockets/HTTP).
This layer handles auth (Supabase/Clerk), rate limiting (essential for AI APIs!), and validation. It’s the bouncer at the club.
3. The "Async" Worker: The Secret Sauce
Stack: Redis + BullMQ (or Celery if you stay in Python).
This is the most important part.
If 100 users click "Generate" at the same time, you cannot spawn 100 LLM calls instantly. You will hit rate limits or crash your server.
Instead, I push every AI request into a Redis Queue.
A separate "Worker Service" picks up these jobs one by one (or in batches) and processes them. This ensures the app stays responsive even during traffic spikes.
4. The Intelligence Layer
Stack: Python (FastAPI) + LangChain/LlamaIndex.
This is where the actual AI lives. Because it sits behind the Queue, it is isolated. If the AI service crashes or hangs, the main website stays up.
I usually containerize this with Docker so I can scale it independently. If the queue gets full, I just spin up 5 more Python containers.
5. The Memory
Stack: PostgreSQL (Main Data) + pgvector (Vector Data).
Stop using a separate Vector Database if you don’t have to.
In 2026, PostgreSQL with the pgvector extension is powerful enough for 95% of use cases. It keeps your architecture simple. You can join your "User" table with your "Embeddings" table in a single query. It is a developer experience dream.
Final Thoughts
The mistake I see most founders make is building a "Monolith AI App" where the frontend waits directly for the backend to finish thinking.
Decouple everything.
Let the frontend float. Let the backend queue. Let the AI think in the background.
That is how you build for scale.
Hi, I’m Frank Oge. I build high-performance software and write about the tech that powers it. If you enjoyed this, check out more of my work at frankoge.com