GPT-4, Claude, or custom ML models integrated into a production SaaS — shipped in 2–4 weeks.
Timeline
2–4 weeks
Starting from
$3,460
Full platform
up to $30,000
Next.js + Server Actions
Full-stack with streaming SSE
OpenAI / Anthropic SDK
LLM API integration
Supabase + pgvector
Database and vector embeddings
Stripe Billing
Usage-based or subscription billing
Generic AI chat apps have no moat. The successful AI apps wrap AI around a specific workflow with proprietary data, unique UX, or deep integrations. 'AI for legal contracts' beats 'AI assistant.'
GPT-4 costs ~$30/1M output tokens. A user who generates 10,000 tokens/day costs $0.30/day = $9/month in API costs alone. Price subscriptions above this floor and cap usage for lower tiers.
LLM responses are slow (3–10 seconds for full response). Streaming (token-by-token display) is non-negotiable UX. Next.js Server-Sent Events handle this cleanly.
Rough estimates per feature. Our fixed-price MVP bundles all of these — see the full AI Tool cost guide.
| Feature | Typical range |
|---|---|
| LLM API integration + streaming | $600 – $3,000 |
| RAG / vector search | $800 – $4,000 |
| Usage tracking + limits | $400 – $2,000 |
| Stripe billing (usage or sub) | $500 – $2,500 |
| Conversation history | $300 – $1,500 |
| Prompt management | $200 – $1,000 |
| Document upload + parsing | $400 – $2,000 |
| Total estimate | $3,200 – $16,000 |
We ship production-ready ai tool MVPs in 2–4 weeks at a fixed price. No hourly billing, no scope creep surprises.
A simple AI SaaS (specific use case, OpenAI API, subscription billing) costs $8,000–$20,000. Apps with custom RAG pipelines, fine-tuned models, and complex document parsing cost $25,000–$60,000.
GPT-4o is the default for most apps — best balance of speed, quality, and cost. Claude 3 is better for long documents and complex reasoning. Gemini 1.5 Pro has the largest context window (2M tokens). We pick the right model per use case.
RAG (Retrieval Augmented Generation) lets the AI answer questions about your own documents or data. If your app needs to reference specific content, knowledge bases, or customer data, you need RAG. We implement it with pgvector on Supabase.