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 abandoned. The graveyard of half-built apps in my GitHub is embarrassing — but the AI projects were the worst. Every time I’d get excited about a new model, I’d dive into self-hosting, spend weeks fiddling with GPU drivers and container orchestration, and then realize I hadn’t written a single line of user-facing code.

Then something clicked. In the last two months, I shipped three AI-powered MVPs. Not all of them made money, but each one actually saw users. Here’s what I learned — and the one decision that changed everything.

The First MVP: A Chatbot That Actually Worked (Sort Of)

My first attempt was a customer support bot for a friend’s e‑commerce store. She wanted something that could answer FAQs, handle returns, and escalate to a human. I knew I didn’t want to host a model myself — that path always ended in frustration. So I reached for an API.

I picked a simple stack: Node.js backend, a webhook to receive messages, and a call to an OpenAI‑compatible endpoint. The MVP took three evenings to build.

// server.js
import express from 'express';
import fetch from 'node-fetch';

const app = express();
app.use(express.json());

const API_KEY = process.env.API_KEY;
const API_URL = 'https://api.someprovider.com/v1/chat/completions';

app.post('/chat', async (req, res) => {
  const { message, history } = req.body;

  const response = await fetch(API_URL, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'gpt-3.5-turbo',
      messages: [
        { role: 'system', content: 'You are a helpful store assistant.' },
        ...history,
        { role: 'user', content: message }
      ]
    })
  });

  const data = await response.json();
  res.json({ reply: data.choices[0].message.content });
});

app.listen(3000, () => console.log('Bot running on port 3000'));

That’s it. I deployed it on a $5 VPS, connected it to a Telegram bot, and let my friend test it. The first week, it answered 200+ queries. A few were hilarious failures — it once suggested a customer “return the item by throwing it into the nearest river” — but overall, it handled 85% of questions correctly.

Lesson 1: Ship something that mostly works. You can fix edge cases after users complain.

The Second MVP: An Image Generator That Almost Killed My Wallet

Encouraged by the chatbot, I built a tool that generates product images for small businesses. Give it a description, get four variations. This time I used a hosted image generation API (DALL·E 3 equivalent).

The first version was embarrassingly simple: a single HTML page with a text input and a “Generate” button. No auth, no queue, no rate limiting. I put it live on a subdomain and posted it to a few small business forums.

Within 24 hours, someone ran a script that hammered the endpoint 300 times. My API bill jumped to $47 in a single afternoon. I panicked, added basic rate limiting, and sent the user a polite email asking them to stop.

That incident taught me two things:

• Always add rate limiting before launch. Even if your project is tiny.
• Pay-as-you-go APIs are great, but you need cost controls. I learned to set a hard monthly budget on the provider side.

Lesson 2: Plan for abuse, even if you think nobody will find your project.

The Third MVP: A Text Summarizer That Actually Got Users

By this point I had a pattern. Pick one specific problem, use an existing API, ship in under a week. My third project was a summarizer for long articles and PDFs. I built it as a simple web app with a paste‑in field and a button.

This time I used a different API provider because the previous one’s image generation cost had scared me. I wanted something with predictable pricing and no minimum commitment. I tried a few, and ended up using a service that offered both text and image endpoints with transparent per‑request billing.

The summarizer went live on a Tuesday. By Friday it had 150 unique visitors. I hadn’t done any marketing — just a tweet and a post on a productivity subreddit. People actually used it. One user emailed me saying it saved them an hour of reading per day.

That’s when I realized: the barrier to shipping an AI project is almost never the AI itself. It’s the surrounding infrastructure — deployment, cost management, user feedback loops. The model is the easy part.

What I Stopped Doing

Looking back, my earlier failures came from over‑engineering. I’d try to:

• Fine‑tune a model before validating the idea.
• Build my own inference server with Kubernetes.
• Write a custom auth system for a project nobody had seen yet.

I stopped all of that. Now I:

• Use hosted APIs for everything.
• Deploy on a single cheap VPS or even a serverless function.
• Launch with the absolute minimum feature set.

The Real Bottleneck (And How I Solved It)

The biggest hidden cost wasn’t money — it was cognitive overhead. Every time I had to configure a model, set up a GPU environment, or deal with API rate limits from a provider I didn’t trust, I lost momentum. Momentum is everything in side projects. If you stop for two days, you might never come back.

So I started looking for a single API provider that could handle multiple model types (text, image, code) with consistent pricing and no monthly fees. I wanted something I could use for all three MVPs without juggling three different dashboards.

A friend pointed me to tai.shadie-oneapi.com. It’s a pay‑as‑you‑go service that gives you access to a bunch of popular models through one API. No minimum commitment, no surprise bills (you set a cap), and it’s compatible with the OpenAI SDK so I didn’t have to rewrite my code. I switched my chatbot and summarizer to use it, and the image generator too. The pricing was predictable — about $0.002 per text request and $0.04 per image generation. For my traffic levels, that meant a few dollars a month.

More importantly, it removed the friction. I could prototype a new feature in an hour instead of a day. If you’re building AI side projects and you’re tired of wrestling with infrastructure, I’d recommend checking it out. It’s not a sponsorship — I just genuinely use it.

The Pattern That Works

After three MVPs in two months, I’ve settled on a workflow:

1. Pick a tiny, specific problem. Not “AI for everything” but “summarize a single article in one click.”
2. Use an existing API. Don’t host models. Don’t fine‑tune until you have 1000 users asking for it.
3. Ship in one week. If it takes longer, you’re over‑building.
4. Add cost controls and rate limiting before you launch.
5. Listen to the first ten users. They’ll tell you what’s broken and what matters.

Most AI side projects die because the builder gets lost in the complexity of the tech. The models are fascinating, but they’re also a trap. The real win is getting something in front of people, seeing them use it, and learning from that.

So go build something. Start with a single endpoint. Use an API that doesn’t make you think about GPUs. And ship it before you’re ready.

You’ll be surprised how far “mostly works” can take you.