"How much does it cost to build an AI app?" is the first question almost every founder asks us. The honest answer is "it depends" — but that's useless on its own. So here is a real breakdown of the ranges, what moves the number, and how to spend less without cutting corners.
AI app cost by project type (2026 ranges)
| Project type | Typical cost | Timeline |
|---|---|---|
| LLM integration / simple chatbot | $2,000 – $5,000 | 1–3 weeks |
| RAG app (chat over your docs/data) | $8,000 – $30,000 | 3–8 weeks |
| AI agent / workflow automation | $20,000 – $60,000 | 6–12 weeks |
| Full AI SaaS platform | $40,000 – $120,000+ | 3–6 months |
These assume a senior team building production-quality software — not a throwaway prototype. A weekend demo costs almost nothing; a system real users and clients depend on is a different exercise.
What actually drives the cost
1. Your data (usually the biggest factor)
The model is the cheap part. Getting your data clean, chunked, embedded, and kept up to date is where the real work lives. A RAG system over tidy Markdown docs is fast; one over scanned PDFs, messy databases, and five external APIs is not.
2. Integrations
Every external system — your CRM, payment provider, email, internal database — adds engineering and testing time. Two integrations is a feature; ten is a project.
3. Reliability and scale
An internal tool used by your team can cut corners. A customer-facing product needs auth, rate limiting, monitoring, evals, guardrails, and graceful failure. That reliability layer is often 30–40% of the total build.
4. Ongoing LLM API + infrastructure costs
Beyond the build, budget for monthly running costs: LLM API usage (varies hugely by traffic and model), vector database, and hosting. For most early products this is tens to low hundreds of dollars per month — far less than the build itself.
How to spend less without cutting corners
- Start with RAG, not fine-tuning. It's cheaper, faster, and enough for most use cases. (More on this in RAG vs fine-tuning.)
- Scope a sharp MVP. One workflow done well beats ten half-built. Ship, learn, then expand.
- Use managed models and infra early — OpenAI/Claude/Gemini APIs and managed vector DBs are cheaper than running your own until you have real scale.
- Hire senior, not cheap. A senior engineer who scopes well saves more than a junior rate ever will. See how to hire an AI agency.
How we price at Kortex Labs
We scope every project to the simplest architecture that solves the problem, then quote a fixed range for the MVP plus an optional monthly engagement for iteration. You own 100% of the code and IP. We've built exactly these systems in production — large-scale search and data pipelines, real-time event platforms, and voice-first AI products — so the estimate reflects real delivery, not guesswork.
Want a real number for your idea? Tell us what you're building and we'll send a free, no-obligation quote within 24 hours.