How to build an AI app and deploy it for free: architecture, stack, hosting, costs and the checklist that survives real users

How do I build an AI app?

Build a normal web application and put the model call behind one function. The front end collects input and shows output, the API validates the input, calls the model with a prompt and any retrieved context, validates the output, stores what happened, and returns it. Add authentication, a database, logging and a deploy pipeline, and you have an AI app. The model is one dependency, and everything around it is what makes the product work when a stranger uses it.

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I shipped five production AI applications between February and July 2026, a RAG platform, a fine-tuning platform, a document extraction service, a browser face recognition system and an OCR and speech workspace, each with Docker, CI/CD, structured logging and metrics [12]. The stack below is the one they share, and the checklist at the end is what each of them taught me the hard way.

The model call is thirty lines. The other three thousand lines are why the app is still up next month. Pranjul Rathour

What is the architecture of an LLM app?

Five layers. A client: a web page, a mobile app, a WhatsApp bot. An API layer that authenticates, validates and rate-limits. A model layer: the prompt, the provider client, retries and fallbacks, and optionally retrieval, tools or a fine-tuned model. A data layer: a relational database for users and logs, a vector index for documents, object storage for files. And an operations layer: deploy pipeline, logs, metrics, alerts. Most AI apps are small enough that the API and model layers live in one service.

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The longer write-up of the FastAPI and Next.js version of this, with the folder layout, is on the portfolio [13].

Which tech stack should I use for an AI app?

Python for the back end, because every model library, SDK and example is in Python, with FastAPI for the API [1]. Postgres for data, with pgvector when you need embeddings [3]; Supabase gives you Postgres, auth and storage on a free tier [4]. Any front end you already know, plain HTML, React or Next.js. Docker to package it [2]. GitHub Actions to test and deploy [8]. One platform host. Add nothing else until a real need appears.

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On frameworks such as LangChain: excellent for a first prototype and for loaders; for production, I write the model wrapper myself, because a bug under three abstraction layers costs a day and a bug in my own fifty lines costs ten minutes.

How do I deploy an AI app for free?

Package the app in Docker and deploy to a platform with a free or trial tier: Railway, Render or Fly.io for a container, Hugging Face Spaces for a demo with a GPU option, Supabase for the database, and a static host such as GitHub Pages, Netlify or Cloudflare Pages for the front end. Free tiers sleep or cap hours, so design the app to start fast and hold no state in memory. The model API itself is not free beyond trial credits, so budget tokens from day one.

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Free tiers change; the platform documentation is the source of truth and this table is a map, not a guarantee [4][5][6][7]. RAG.NextUpgrad, with hybrid retrieval, reranking and streaming, runs in about 220 MB of memory on a free tier, which is the proof that a real AI app does not need a large server [12]. My deployment write-up walks through it step by step [14].

How much does it cost to run an AI app?

Hosting can be zero at first. The model API is the real bill: input tokens plus output tokens, times the price per million, times requests. Estimate it before launch: measure the tokens of a typical request, multiply by expected daily requests, multiply by the provider's rate. Then set a hard monthly cap at the provider, a per-user daily limit in your code, and an alert at half the cap. An app with no cap is one viral post away from a bill you did not plan.

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How do I make an AI app reliable?

Wrap every model call with a timeout, a bounded retry with backoff, and a fallback to a second provider or a smaller model with the same prompt. Validate outputs against a schema and fail closed when they do not parse. Stream responses so users see progress. Make the app stateless so it can restart. Test the prompt against a fixed set of inputs in CI. Log every request with its tokens, latency and outcome. My apps fall back across providers automatically, and that single feature has kept them up through several API outages.

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async def generate(prompt: str, schema: type[BaseModel]) -> BaseModel:
    for provider in PROVIDERS:                      # identical prompt, ordered by preference
        for attempt in range(2):
            try:
                raw = await asyncio.wait_for(provider.complete(prompt, max_tokens=800), timeout=30)
                return schema.model_validate_json(raw)   # fail closed if it does not parse
            except (TimeoutError, ProviderError, ValidationError) as e:
                log.warning("provider=%s attempt=%d err=%s", provider.name, attempt, e)
                await asyncio.sleep(0.5 * (attempt + 1))
    raise ServiceUnavailable("all providers failed")

How do I secure an AI app?

Treat every input, and every document or tool result the model reads, as untrusted. Keep instructions in the system prompt and data in delimited user content. Give tools the least permission they need and require a human confirmation for irreversible actions. Never put secrets in prompts. Rate-limit per user. Validate outputs before acting on them. Log prompts and outputs with user consent and a retention policy. OWASP's list of LLM application risks is the checklist to review before launch [9].

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The most common student mistake is the API key in the front-end code, where anyone can read it and run up your bill. The key lives on the server, always, and the server enforces the limits.

Should I build an AI agent or a simple app?

A simple app, until a decision appears that you cannot write as code. Most "AI agents" that succeed in production are workflows: code decides the steps, a model does the language work at each step. Reach for a real agent loop only when the task is open-ended and the model must choose actions at run time, and then add limits, permissions and confirmations before anything else [11].

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How do I get users for an AI app?

Build it for a group you belong to, your class, your club, a family business, and put it in front of them the day it works. Ask what they tried to do that failed, and fix that. Post a demo video where those people are. Ten real users teach more than a thousand sign-ups from a launch post, and they are how a student project becomes a line on a resume that a recruiter believes.

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What mistakes kill AI apps after demo day?

No cap on API spend. The key in the front end. No logs, so the first complaint is undebuggable. State held in memory on a host that sleeps. Prompts edited in production with no test set. One provider and no fallback. No per-user limits, so one script drains the budget. A model output acted on without validation. Each is on the checklist above; each I have either done or watched a mentee do.

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What are common AI app interview questions?

Design an LLM feature end to end and name where it can fail. Estimate its monthly cost. Explain how you would handle a provider outage. Explain where prompt injection can enter and what limits its damage. Describe how you would test a prompt change. Say what you would log. Each answer is a section above, and the best evidence is a live app you can open during the interview.

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Where should I start?

Take a problem someone near you has, build the smallest version with FastAPI, one model call with a timeout and a fallback, Postgres on Supabase, Docker, and deploy it on a free tier this weekend. Log everything. Show it to three people. Then read the checklist again. All five of my production apps are open source and follow this shape [12]. If your college wants a workshop that ends with every student having a deployed AI app, email pranjulrathour41@gmail.com or use pranjulrathour.scult.in/invite.

Pranjul Rathour

GenAI Engineer · Kanpur, Uttar Pradesh, India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.

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