Ask five Indian companies for an AI quote and you will get numbers from ₹50,000 to ₹50 lakh — for what sounds like the same project. Nobody is necessarily lying. They are quoting five completely different things, and this post is about learning to tell them apart. With our actual prices in it.
We build AI for a living — our own patent-filed AI platform runs in production, and we ship AI development for clients across seven countries. So these are numbers from invoices, not from a pricing survey.
Why AI quotes vary so wildly
Three things explain almost the entire spread:
- What "AI" means in the quote. A chat widget wired to a model API is a weekend of work. An AI agent that reads your database, takes actions and passes app store review is an engineering project. Both get called "AI" in proposals.
- The integration surface. The number of your systems the AI must touch — database, payments, WhatsApp, CRM, admin panel — drives cost far more than the AI itself. Two integrations and ten integrations are different projects at the same "AI feature" label.
- Who is building it. A metro agency with sales teams and account managers prices in that overhead. A research-grade consultancy prices in PhDs you may not need. A small product team prices in neither.
The numbers we actually charge
In rupees, the pilot lands around ₹1.5 lakh, systems from roughly ₹3.8 lakh, and a dedicated developer about ₹84,000 a month — exchange rates move, the dollar prices are what we hold. Why can we sit at the low end of the market without cutting corners? We are in Pilani, not Bangalore. No metro office, no sales layer, and you talk to the founder directly. The full breakdown lives on our AI development services page.
The honest caveat: a fixed price only exists after scoping. Anyone who gives you a final number before understanding your integration surface is guessing — and you will pay for that guess later, in change requests.
The second bill nobody quotes: running costs
Build cost is half the story. Every AI feature has a monthly model bill that scales with usage, and it is the number most proposals quietly skip. Rough production ranges we see:
- A scoped support agent for a small business: $20–150 per month in API costs.
- AI voice adds per-minute synthesis charges — language and voice quality move this more than volume does.
- Document-heavy automation depends on page counts; caching repeated inputs routinely cuts the bill by half.
The prototype that calls the biggest model for every request feels fine at ten users and becomes your second-largest expense at ten thousand. This is a design problem, not a billing surprise — model selection per task, caching and hard caps have to be engineered in from day one.
How to keep the price honest
Not just the build price. If the vendor cannot estimate your monthly model bill at your expected volume, they have not scoped the project.
Guardrails, human approval on irreversible actions and audit logs are engineering line items. A quote without them is a demo, priced as a product.
Direction is cheap to change early and expensive at delivery. Any team confident in its estimate can show something real in a week.
IP assignment before work starts, code in your repository from the first commit. If the price looks low because the vendor keeps the system, it is not low.
When you should not spend this money
Sometimes the honest quote is zero. If your workflow is a fixed rule on structured data — send a reminder three days before the due date, flag orders over a threshold — you need a script, not a model. A cron job costs a fraction of any AI build and never hallucinates. We wrote a thirty-second test for exactly this decision, and four out of five of our own internal automations use no AI at all.
AI earns its cost when the input is messy, unstructured or multilingual, or when the path to the answer changes every time. If a vendor never tells you "you do not need AI for this", be careful — that sentence should appear in at least some scoping calls.