What this AI product budget includes
- OpenAI / Anthropic API integration
- Prompt engineering layer
- Semantic search (vector DB)
- Conversation memory
- Admin panel for AI configuration
- Usage monitoring & cost controls
- API rate limiting
- RAG (Retrieval-Augmented Generation)
What drives the cost of an AI product?
The main cost drivers in AI products are the complexity of the prompt engineering layer, the choice of retrieval architecture (RAG vs fine-tuning), and latency requirements. A well-designed RAG pipeline can deliver 80% of the value of fine-tuning at a fraction of the cost.
Note: this budget covers development cost only. Ongoing API usage fees from OpenAI or Anthropic vary with your user volume and are billed separately by the API provider.
Frequently asked questions
- How much does an AI product cost?
- A mid-scope AI product for LATAM costs around $53,950 USD and takes 6 months with a blended team at ~$45/hour. This includes API integration, prompt engineering, semantic search, and monitoring.
- What AI APIs does it integrate?
- OpenAI (GPT-4o, Embeddings) and/or Anthropic (Claude). The architecture includes an abstraction layer that allows switching models without rewriting application logic. Vector databases (Pinecone, pgvector) are included for semantic search.
- Does it include training data?
- No. This covers API-based integration using pre-trained models with RAG on your own data. Custom model training is a separate engagement. Most products don't need custom training — RAG with good prompt engineering delivers excellent results.
- Can I start with a smaller MVP?
- Yes. A focused AI MVP with a single use case can start from $30,000 USD / 3 months. See the MVP budget.