Ask ten agencies what an AI product costs and you'll get ten versions of "it depends." That's technically true and completely useless. We build AI products for a living, so here are the actual numbers — what the market charges, what moves the price, and where budgets quietly die.
The market ranges in 2026
These are market ranges we see across the industry — not our quote (yours takes a 20-minute call, and it's free):
- Validation prototype / proof of concept: $10,000–$50,000. A working system that proves the idea on your real data. Weeks, not months.
- Production MVP: $50,000–$150,000. Real users, real guardrails, real monitoring — the version you can charge money for.
- Scaled product: $150,000–$500,000+. Multi-feature, compliance-grade, integrated with your stack, tuned for cost and latency at volume.
If a vendor quotes far below these ranges, you're usually buying a demo wearing a product's clothes. Far above, you're often paying for an agency's org chart.
The five things that actually move the number
1. Scope clarity. "An AI assistant for our team" costs more than "an assistant that answers policy questions from these 400 documents" — vagueness is billed by the hour. Scoping is a skill; it's why we treat it as its own discipline.
2. Data readiness. If your knowledge lives in clean docs and databases, you're cheap to build for. If it lives in seventeen formats and one retiring employee's head, the first invoices buy data plumbing, not AI.
3. Integrations. Every system the AI must read from or write to — CRM, ERP, telephony, calendars — adds surface area. Integrations are rarely hard; they're numerous.
4. Compliance and privacy. Healthcare and fintech builds carry audit trails, data boundaries and access controls. Worth every cent, but it's real engineering time.
5. Latency and volume. An internal tool ten people use is one problem. A voice agent that must respond in under 300 milliseconds to thousands of callers is a different one. Speed at scale is where senior engineering earns its rate.
The running costs nobody quotes
An AI product keeps costing money after launch: model API calls, hosting, monitoring. Unmanaged, token bills can quietly rival payroll. Engineered properly — caching repeated queries, routing simple requests to small models, tracking cost per feature like you track latency — running costs become boringly predictable. Ask any vendor how they'll engineer your unit economics. Silence is an answer.
Where budgets die
- The rebuild. A cheap prototype that can't survive contact with real users, built twice. The most expensive way to save money.
- The bottomless retainer. Monthly invoices, no shipping dates, no acceptance criteria.
- The junior bait-and-switch. Seniors sell the project, juniors build it. You pay senior rates either way.
How we structure it instead
Three models, chosen by where you are: a fixed-price AI Sprint (2–4 weeks to a working proof of concept), a Fixed Build with a committed price and timeline once scope is clear, or an embedded senior AI Team by the month. Payments are milestone-based on demoed software, and you own all code and IP at the end — no lock-in, no licensing games.
Want the real number for your idea? Bring it to a free 20-minute call with a senior engineer — or just ask the AI on our homepage; it's one of ours, and it books calls.
