Short answerA focused AI tool or assistant typically costs ₹2 lakh to ₹4.5 lakh to build in India, and a full mobile app with AI features usually lands between ₹5 lakh and ₹15 lakh. The final number depends less on "AI" and more on how many other features sit around it: logins, payments, chat, dashboards and integrations.
"How much will my AI app cost?" is the first question almost every founder asks us, and the honest answer is "it depends". That is not very useful, so this guide breaks the number down the way we actually estimate projects at APN Code: a base cost for the type of product, plus a cost for each feature module, adjusted for the stage you are building for.
The three things that decide the price
- The platform. A web app is cheaper than a mobile app, and building for web, iOS and Android together costs more again.
- The feature modules. Every extra capability, such as payments, real-time chat or live maps, adds a predictable block of work.
- The stage. A first MVP for early users is built very differently from a platform that must survive a flash sale with 50,000 people online.
Typical price ranges by product type
These are typical Indian market ranges for a professionally built first version, including design, development, testing and launch:
| Product | Typical market range | Typical timeline |
|---|---|---|
| AI tool or assistant (web) | ₹2 L – ₹4.5 L | 4–6 weeks |
| Website or web app | ₹1.5 L – ₹3.5 L | 3–5 weeks |
| Mobile app (iOS + Android) | ₹3 L – ₹6.5 L | 6–9 weeks |
| Web + mobile apps together | ₹4.5 L – ₹9 L | 8–12 weeks |
That is the base. Most real products add two to five modules on top.
What each AI feature adds
"AI" covers very different kinds of work, and they are not priced the same:
- AI assistant or chatbot (LLM): ₹1.2 L – ₹3 L. A chat interface on top of a model such as GPT or Claude, with your own instructions, guardrails and conversation history. Add more if it needs to answer from your documents (see our guide to RAG).
- Prediction or recommendation (ML): ₹1.5 L – ₹4 L. Forecasting demand, scoring leads, recommending products or detecting fraud. Needs good historical data.
- Camera and computer vision: ₹1.5 L – ₹3.8 L. Reading documents (OCR), recognising products or checking photos.
- Voice: usually priced with the assistant, plus speech-to-text and text-to-speech services.
The non-AI features that quietly double budgets
In our experience, the AI part is rarely the most expensive line. These are the modules that move the total most:
- Marketplace or booking engine: ₹1.5 L – ₹3.5 L
- Real-time chat and notifications: ₹80k – ₹1.8 L
- Maps, location and live tracking: ₹70k – ₹1.6 L
- Payments and subscriptions (Razorpay, Stripe, UPI): ₹60k – ₹1.4 L
- Admin panel and analytics: ₹60k – ₹1.5 L
- Sign-up, login and profiles: ₹30k – ₹60k
A useful rule: if a feature has to work in real time, handle money or coordinate two sides of a market, budget for it carefully.
A worked example
Say you want an AI health coach mobile app: users sign in, connect a wearable, chat with an AI coach and get daily nudges.
- Mobile app base: ₹3 L – ₹6.5 L
- AI coach (LLM): ₹1.2 L – ₹3 L
- Login and profiles: ₹30k – ₹60k
- Notifications: included in a lighter chat and notifications module
- Wearable data integration: ₹50k – ₹1.5 L
That puts a solid MVP at roughly ₹5 L – ₹11 L on the open market. If you are building for a growing business with analytics and polish, multiply by about 1.35. For enterprise scale, with security audits and heavy traffic, multiply by about 1.8.
You can get a breakdown like this for your own idea in about a minute with our free Idea Checker.
Running costs people forget
The build is a one-time cost. These are monthly:
- AI model usage. You pay the model provider per message. For most early-stage apps this is a few thousand rupees a month, but it grows with users, so design prompts and caching carefully from day one.
- Cloud hosting. Usually ₹3,000 – ₹25,000 a month for an early product on AWS, GCP or Azure.
- Third-party services. SMS and OTP, maps, email, payment gateway fees.
- Maintenance. Plan for 15–20% of the build cost a year for updates, fixes and app store changes.
How to spend less without cutting quality
- Start with one core flow. Launch the single journey that proves people want the product. Everything else is phase two.
- Use a proven model, don't train your own. For almost every first version, an existing model with good prompts and your data beats a custom-trained one, at a fraction of the cost.
- Reuse proven modules. Login, payments and admin panels shouldn't be rebuilt from scratch for every project. Teams with a library of tested modules deliver faster and cheaper.
- Prototype before you build. A clickable prototype in the first week catches expensive misunderstandings before a single feature is coded.
Fixed price vs time-and-materials vs dedicated team
The same app can be priced in three different ways, and the model you choose changes both the number on the quote and who carries the risk.
| Model | How you pay | Works best when | Watch out for |
|---|---|---|---|
| Fixed price | One agreed amount for an agreed scope | The first version is clearly defined | Every change becomes a change request |
| Time and materials | Hours or days actually worked | Scope is still moving or you are exploring | Costs can drift without weekly tracking |
| Dedicated team | A monthly fee for named people | You have a long roadmap after launch | You need someone to set priorities each week |
Fixed price suits most first versions. You know what you will spend, and the studio carries the risk if the work takes longer than planned. The trade-off is that the scope has to be written down properly before work starts. If you change your mind halfway through, expect a change request with its own price.
Time and materials is better when you genuinely don't know the shape of the product yet, for example when you are testing whether an AI feature is accurate enough to be useful at all. You pay for what is done, so ask for a weekly summary of hours against a working budget, and agree a cap you can stop at.
A dedicated team makes sense once the product is live and growing. You get the same developers every month, they learn your business, and you decide what they work on. It is usually the most expensive option per month and the least efficient one for a short, well-defined build.
A common and sensible pattern is a fixed-price MVP followed by a smaller time-and-materials or dedicated arrangement once real users start asking for changes.
How to read (and compare) a development quote
Two quotes for "the same app" can differ by a factor of three, and often they are not quoting for the same thing. Before you compare totals, check these points:
- Is design included? Some quotes cover development only and assume you will supply finished screens. Design for a first version is a real piece of work, not a small extra.
- Which platforms? "Mobile app" can mean Android only, or iOS and Android from one codebase, or two separate native apps. Each is priced very differently.
- Is the admin panel in scope? Someone has to manage users, content and refunds. If the admin side is missing from the quote, it will come back later as an extra.
- What testing is included? Look for testing on real devices and a stated bug-fixing period after launch, not just "QA" as a single line.
- Who owns the code and accounts? You should own the source code, the cloud account, the app store listings and the AI provider account from day one.
- What happens after launch? Check how long the warranty period is, what counts as a bug versus a new feature, and what support costs after that.
- How is AI usage handled? A good quote states whether model usage during development and after launch is included or billed separately, and roughly what it will cost per month at your expected volume.
When a quote is much lower than the others, it is usually missing one of these items rather than being more efficient. When it is much higher, ask which parts are driving it; sometimes it includes enterprise work, such as security audits or heavy load testing, that a first version doesn't need yet.
The most useful thing you can do is send every studio the same written brief, with the same list of features and the same stage (MVP, growing business or enterprise). Then the quotes become comparable.
What drives cost up after launch
The build is only the first bill. Once real people use the product, a few things tend to push spending up:
- Feature requests from real users. This is a good problem, but it is the biggest source of extra spend. Keep a ranked list and release in small batches rather than reacting to every message.
- AI usage growing with users. Model costs scale with the number of conversations and how long they are. Long prompts, long chat histories and answering from large documents all raise the cost per message. Caching common answers and trimming context keeps this under control.
- App store and platform changes. Apple and Google change their rules and operating systems every year. Some updates are mandatory, which is why the 15–20% yearly maintenance budget above matters.
- New integrations. Connecting to a CRM, an ERP, WhatsApp or a new payment method each needs its own block of work and testing.
- Scaling the infrastructure. An architecture that is perfect for 500 users may need changes for 50,000. Plan for this when growth is real, not before.
- Compliance and security. Handling health, financial or children's data, or selling to large companies, brings audits, data protection work and stricter logging.
A practical approach is to keep a yearly budget of roughly 20–30% of the original build cost for maintenance plus small improvements, and to price larger new features separately as their own mini-projects.
Why AI-accelerated teams cost less
The biggest change since 2024 isn't AI inside apps, it's AI in how apps are built. Engineering teams that use AI coding tools with senior review and a reusable module library ship the same production-grade work in far less time. At APN Code that is how we typically come in around 40% below standard agency rates and get a first working prototype live in about seven days.
FAQ
Can I build an AI app for under ₹2 lakh?
A narrow internal tool, yes. A product for paying customers usually needs more.
Is a web app or a mobile app cheaper?
A web app, by a clear margin. Many founders launch on web first.
Do I need to train my own AI model?
Almost never for a first version. An existing model with your data works well.
How long does a typical build take?
Usually 4–12 weeks, depending on platform and modules, as in the table above.
Who pays for AI usage after launch?
You do, directly to the provider. Ask for a monthly estimate upfront.
If you have an idea and want a real number, run it through the Idea Checker or send us a brief. A founder reviews every one personally.