AI Wrapper Stack Guide for Solo Developers

The Stack

"AI wrapper" gets used as an insult, but some of the fastest-growing SaaS products right now are wrappers around OpenAI or Anthropic APIs with a focused UX for a specific use case. The key is that the AI is the engine, but the product is the interface, the workflow, and the domain-specific context you add on top.

When to Use This Stack

Perfect for: AI-powered writing tools, code assistants, data analysis tools, customer support bots, content generation platforms, any product where AI is the core value proposition.

Not ideal for: Products that need AI as a minor feature (just call the API directly, you don't need a whole stack for it), or applications where latency is critical and you can't tolerate API round-trips.

If your product's main interaction is "user gives input, AI generates output," this is your stack.

Why Solo Developers Love It

Vercel AI SDK handles the hard parts. Streaming AI responses to the browser in real-time is tricky to build from scratch. The Vercel AI SDK gives you useChat and useCompletion hooks that handle streaming, loading states, error handling, and message history out of the box. What would take days to build correctly takes minutes with the SDK.

Model switching is a lifesaver. The AI SDK abstracts provider differences. Switch from OpenAI to Anthropic to an open-source model by changing one line. This matters because AI providers change pricing, models, and capabilities frequently. Being locked to one provider is risky.

The margin structure works for solo developers. You pay per API call (a few cents per request typically) and charge users monthly ($10-49/month) or per credit. The margins are high once you have enough volume because AI API costs are relatively low compared to what users will pay for a well-designed product.

Speed to market is unbeatable. You can build a functional AI product in a weekend. The AI does the hard work. You're building the interface, the workflow, and the billing layer. Strip away the AI provider and what remains is a fairly standard web application.

The Parts Nobody Warns You About

API costs can spiral. OpenAI's GPT-4 costs significantly more than GPT-3.5-turbo, and users will consume more than you expect. I've seen AI wrapper developers shocked by their first real API bill. Monitor usage carefully, set hard limits per user, and design your pricing to cover worst-case API costs with comfortable margin.

Build a cost tracking system from day one. Log every API call with its token count and cost. Know exactly what each user costs you. Some users will send massive prompts that cost 10x what a normal request costs. Your pricing needs to account for these outliers.

"Just a wrapper" is a real competitive risk. If your product is a text box that sends input to OpenAI and displays the response, you have zero defensibility. OpenAI can replicate your product tomorrow. The moat comes from domain-specific context, curated prompts, integrations with other tools, and workflows that go beyond a single AI call.

Streaming UX requires thought. Streaming AI responses character by character looks cool but creates UX challenges. How do you handle errors mid-stream? What if the user navigates away? What about mobile where long streams push content off screen? The Vercel AI SDK handles the technical streaming, but the UX design around streaming is on you.

Prompt engineering is ongoing maintenance. Your prompts are your product's brain. They need regular tuning as models change, user feedback comes in, and edge cases emerge. Budget ongoing time for prompt refinement. Treat prompts as code. Version them, test them, and review changes carefully.

Rate limits and timeouts. AI API calls take 2-30 seconds depending on the model and response length. Your app needs to handle loading states, timeouts, and rate limit errors gracefully. Users are surprisingly impatient, even when they know AI generation takes time.

Setting Up the Stack

JavaScript path (fastest to ship). Start with Next.js. Install the Vercel AI SDK (npm install ai). Set up your OpenAI API key. The useChat hook gives you a complete chat interface in about 20 lines of code. Add Supabase for auth and database, Stripe for billing, deploy to Vercel.

Python path (more AI ecosystem access). Start with FastAPI or Django. Use LiteLLM for unified AI API access across providers. Build your own streaming endpoint (FastAPI's StreamingResponse works well). Add a React or Next.js frontend that consumes your API. Deploy the backend to Railway and the frontend to Vercel.

For both paths, implement usage tracking immediately. Every API call should record the user, model used, tokens consumed, and cost. This data drives your pricing decisions and helps you identify users who are costing more than they're paying.

Architecture

Frontend (Next.js)
  ├── Chat / Input interface
  ├── Streaming response display
  ├── History / saved outputs
  └── Usage dashboard

Backend (Next.js API routes or FastAPI)
  ├── AI provider calls (OpenAI, Anthropic)
  ├── Prompt management
  ├── Usage tracking & rate limiting
  ├── User authentication
  └── Billing (Stripe)

Data Layer
  ├── PostgreSQL (users, conversations, usage logs)
  ├── Redis (rate limiting, caching)
  └── Vector DB (RAG documents, if needed)

AI Provider
  ├── OpenAI (GPT-4o, GPT-4-turbo)
  ├── Anthropic (Claude)
  └── Open source (Llama, Mistral via Groq/Together)

Cost Breakdown

The wild card is AI API costs. For a product with 100 active users making 10 requests per day using GPT-4o-mini, expect around $15-30/month in API costs. With GPT-4o, that jumps to $100-300/month. Price your product accordingly.

Pricing Models That Work

Credit-based. Users buy credits, each AI generation costs credits. This directly ties your revenue to your costs. Easiest to manage profitably.

Subscription + limits. Monthly plan with a set number of generations per month. $9/month for 100 generations, $29/month for 500, $79/month for unlimited (with fair use limits).

Freemium. 10-20 free generations to hook users, then require a subscription. The free tier is your marketing funnel.

I'd recommend credits or subscription with limits for v1. "Unlimited" plans are dangerous with AI APIs because heavy users can cost you more than they pay.

Alternatives to Consider

If you want to avoid API costs: Run open-source models (Llama, Mistral) on your own GPU via RunPod or Modal. Higher upfront cost but predictable pricing and no dependency on OpenAI.

If you want simpler: Skip the framework entirely. Build a Chrome extension or Raycast plugin that calls the AI API directly. Smaller scope, faster to build, different distribution model.

If you're building with Python: Django + Celery for async AI processing + HTMX for the frontend. All Python, no JavaScript framework needed.

My Take

AI wrappers get dismissed as low-effort products, and honestly, many of them are. A text box connected to OpenAI's API is not a product. But a focused tool that solves a specific problem using AI as the engine? That's a real business.

The winning AI wrapper products have three things in common. First, they solve a narrow, specific problem (not "AI for everything" but "AI that writes real estate listings" or "AI that summarizes legal documents"). Second, they add context and domain knowledge through carefully crafted prompts, RAG, or curated workflows. Third, they make the output usable, not just generated text, but formatted, actionable results that integrate into the user's existing workflow.

If you can build that, the wrapper label doesn't matter. The value is in the product, not the underlying technology.