How to Build an AI Wrapper as a Solo Developer

What You're Building

An AI wrapper is an application that takes an existing AI API (OpenAI, Anthropic, Fal.ai, Replicate) and wraps it with a custom user interface, specific prompts, and business logic tailored to a particular use case. Instead of users going to ChatGPT and figuring out the right prompt, your app does that for them.

This is honestly one of the best solo developer projects right now. The AI does the heavy lifting. You provide the UX, the domain expertise, and the specific workflow. I've built AI-powered tools that took less than two weeks from idea to paying customers.

Difficulty & Timeline

Recommended Tech Stack

Next.js for the frontend and API layer, Vercel AI SDK for streaming responses, and Stripe for payments. For the AI backend, pick your provider based on use case. OpenAI for text generation, Anthropic for long-form analysis, Replicate or Fal.ai for image/video generation. Supabase handles auth and usage tracking.

The entire stack can run on Vercel's free tier while you validate the idea. Your only real cost is AI API usage, which you pass through to users via credits.

Step-by-Step Plan

Phase 1: Foundation (Week 1)

Pick a specific use case. "AI writing tool" is too broad. "AI tool that generates product descriptions for Etsy sellers" is specific enough to build and market. The more specific your niche, the better your prompts will be, and the more willing people are to pay.

Build the core interaction loop. User provides input, your app constructs the optimal prompt (this is your secret sauce), sends it to the AI API, and displays the result in a polished UI. Use the Vercel AI SDK for streaming responses. Users seeing tokens appear in real-time feels magical compared to waiting for a full response.

Set up Supabase for user accounts and a simple credits table. Each AI call costs you money, so you need to track usage from day one.

Phase 2: Core Features (Week 2)

Refine your prompts. This is where most of your time should go. The difference between a mediocre AI wrapper and a great one is the prompt engineering. Test dozens of prompt variations, save the best ones, and iterate based on user feedback.

Add history and saved results. Users want to reference previous generations. Store their outputs in Supabase and build a simple dashboard where they can browse, favorite, and re-use past results.

Build usage controls. Set daily or monthly limits based on plan tier. Show users how many credits they've used and how many remain. This creates natural upgrade pressure without being pushy.

Phase 3: Polish & Launch (Week 3)

Add Stripe for payments. Offer a free tier with limited generations (maybe 10/day) and a paid plan at $19-29/month with higher limits. Make the upgrade flow seamless, a user hits their limit, sees an upgrade prompt, and can be paying within 60 seconds.

Create a landing page with examples of what your tool produces. Real output examples are the best marketing for an AI wrapper. Show before/after, show the quality, show how easy it is.

Launch on Product Hunt, relevant subreddits, and niche communities where your target users hang out.

Monetization Strategy

Credit-based pricing is the most natural fit for AI wrappers because your costs are per-API-call. Charge users more per credit than it costs you, and your margins improve as you optimize prompts (fewer tokens = lower cost per generation).

A typical pricing structure. Free tier with 20 generations/month. Pro at $19/month for 500 generations. Business at $49/month for 2,000 generations. The key is pricing your credits at 3-5x your actual API cost. If an OpenAI call costs you $0.02, charge the equivalent of $0.06-0.10.

Some AI wrappers do extremely well with one-time purchases too. "Generate 50 product descriptions for $29" works when users have a specific, finite need.

Common Mistakes to Avoid

Just wrapping ChatGPT with no added value. If your app is just a prettier ChatGPT interface, users will just use ChatGPT. Your wrapper needs to provide domain-specific prompts, workflow automation, or a UX that's significantly better for the specific use case.

Ignoring prompt costs. AI API calls add up fast. If you're sending 4,000-token prompts to GPT-4 for every request, your margins will be razor thin. Optimize prompts for cost. Use cheaper models (GPT-4o-mini, Claude Haiku) for simpler tasks. Cache common results.

Not handling API failures. AI APIs go down. They rate-limit you. They return unexpected responses. Build retry logic, graceful error handling, and maybe even a fallback to a different provider. Your users shouldn't see raw API errors.

Building features instead of improving prompts. The #1 thing that makes an AI wrapper better is better prompts. Before you add a new feature, spend that time testing and refining your prompt templates. The output quality is what users pay for.

Is This Worth Building?

Yes, with a caveat. The "just an AI wrapper" dismissal you see on Twitter is overblown. The tools making real money are the ones solving specific problems for specific audiences. An AI wrapper that writes cold emails for SaaS salespeople is worth $29/month because it saves them hours of work. The value is in the specificity.

The risk is that AI providers improve their own products to cover your use case. OpenAI launched custom GPTs, which compete with simple wrappers. But purpose-built tools with great UX still win because they're faster, more focused, and integrated into specific workflows.

Build it fast, validate with real users, and keep improving the output quality. AI wrappers are one of the fastest paths from idea to revenue for solo developers right now.