Building an AI Side Project That Actually Ships — Lessons from Shipping 3 MVPs

I’ve lost count of how many side projects I’ve started and never finished. You know the pattern: a spark of inspiration, a weekend of frantic coding, then slowly the repo goes cold. The README stays a TODO. The domain expires.

But something clicked when I started building AI-powered side projects. In the last two months, I shipped three MVPs that actually saw real users. Not thousands, but real people using real features. Here’s how I broke the cycle of unfinished projects, and the one infrastructure decision that made it possible.

The curse of over-engineering

My first AI side project was supposed to be a “smart” note-taking app. I spent two weeks designing a custom RAG pipeline, fine-tuning embeddings, and setting up a vector database. By the time I had a working prototype, I was bored. The code worked, but the product didn’t. I never launched it.

The problem was I treated the side project like a production system. I needed to unlearn that. An MVP isn’t a product — it’s a hypothesis. The fastest way to test it is with the simplest possible stack.

Lesson 1: One API call is enough

For my first shipped MVP — a content summarizer — I wrote exactly 47 lines of Python. No custom models, no vector DB, no caching layer. Just a Flask app that took a URL, scraped the text, and hit the OpenAI API.

from flask import Flask, request, jsonify
import requests
from bs4 import BeautifulSoup

app = Flask(__name__)

def fetch_text(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    return ' '.join(p.get_text() for p in soup.find_all('p'))

@app.route('/summarize', methods=['POST'])
def summarize():
    data = request.json
    url = data.get('url')
    text = fetch_text(url)[:2000]  # cheap truncation
    prompt = f"Summarize this in 3 bullet points:\n{text}"
    api_key = "sk-..."  # yes, hardcoded for MVP
    response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        headers={"Authorization": f"Bearer {api_key}"},
        json={
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": prompt}]
        }
    )
    return jsonify(response.json()['choices'][0]['message']['content'])

That’s it. No async workers, no queue, no database. I deployed it on a free Vercel instance and shared the link on a Slack community. Within a day, 12 people had used it. One person emailed me saying it saved them 30 minutes during research. That was enough validation to keep going.

The code is ugly. It breaks on long pages. But it shipped. And that’s the point.

Lesson 2: Don't host your own models (seriously)

Every tutorial tells you to run Llama locally or spin up a GPU instance. For a side project with zero users, that’s a trap. Here’s the math:

• A small GPU instance on AWS: ~$0.50/hour. If you experiment for 40 hours, that’s $20 — and you haven’t deployed anything yet.
• The same $20 using an API like OpenAI or Claude gives you thousands of requests. I spent about $30 total across my three MVPs over two months. That includes development and live usage.

I’m not saying you should never self-host. If your project gets traction and you need lower latency or privacy, sure. But for an MVP, paying per request is cheaper and faster. You skip the ops burden and focus on the product.

Lesson 3: Pick one feature and kill the rest

Each of my three MVPs had exactly one core feature:

• Summarizer: paste a link, get three bullet points.
• Chat with docs: upload a PDF, ask questions (using a simple text splitter and embeddings from an API — still no local models).
• Code explainer: paste a snippet, get a plain‑English explanation.

No auth, no history, no settings page. I used a single HTML file with a form and a result div. The backend was a single route. That’s it.

When I was tempted to add “save to cloud” or “export as PDF”, I wrote it on a sticky note and taped it to my monitor. Those features stayed there until the MVP had users asking for them. None ever did.

Lesson 4: Launch on day one, iterate after

The summarizer MVP went from idea to first user in under six hours. I wrote the code, deployed to Vercel, and posted the link on a few forums. That’s the launch.

You don’t need a landing page, a domain, or even a proper name. I called it “summy” and used a free .vercel.app subdomain. The feedback I got in the first week shaped the next version: people wanted a word count limit, better error handling, and a dark mode toggle.

If I had spent a month building the “perfect” version, I would have built the wrong thing. Shipping fast means you learn what matters.

The infrastructure decision that saved me

After the first MVP, I realized I was going to build more. Each one needed an API key, a way to handle costs, and flexibility to switch models without rewriting everything. I didn’t want to manage multiple provider accounts or worry about rate limits on a free tier.

That’s when I started using a pay‑as‑you‑go API gateway. It aggregates multiple providers (OpenAI, Claude, Gemini) behind a single endpoint. I can switch models by changing one string in my code. Billing is usage‑based — no monthly minimums, no surprise charges.

For my side projects, I use tai.shadie-oneapi.com. It’s not sponsored or anything — it just works. One API key, one dashboard, and I pay for exactly what I use. For a solo developer shipping MVPs, that removes the overhead of infrastructure decisions. I don’t think about models anymore; I think about the product.

What I’d do differently next time

Looking back, I would have started even simpler. The “Chat with docs” MVP still had a vector store and a few hundred lines of preprocessing. I could have just sliced the PDF into chunks and let the LLM handle retrieval via prompt engineering. It would have shipped in half the time.

I also would have charged money earlier. For the code explainer, I added a $5/month plan after a week. Two people subscribed. That covered my API costs and gave me a reason to keep improving it. Even a small amount of revenue changes your motivation from “toy” to “something people value.”

The real takeaway

Side projects don’t die because the idea is bad. They die because we try to build the cathedral before we lay the first brick. An AI MVP doesn’t need a custom model, a fancy frontend, or a scalable architecture. It needs one working feature and a URL someone can visit.

I shipped three MVPs in two months by keeping each one embarrassingly small. The code is messy, the UIs are ugly, and the error handling is laughable. But they work, and people use them. That’s more than I can say for the six side projects I started and abandoned last year.

If you’re thinking about building an AI side project, start today. Not next weekend. Today. Write the simplest possible code, get it online, and share it. The infrastructure will sort itself out. And if you want to skip the headache of managing multiple API providers, a pay‑as‑you‑go gateway like the one I mentioned can get you from zero to shipped in minutes.

Your first user is waiting. Don’t let perfect be the enemy of shipped.