AI app development cost:
what you'll actually pay

Add AI to an existing app

₹5–20 lakh

An AI-first app on model APIs

₹12–60 lakh

Carries a running cost

Every request

The model is the cheapest part to start. The surrounding system is what you pay for.

Model APIs charge by the token — roughly, pieces of words — with separate prices for what you send (input) and what comes back (output)³⁴. Speech APIs charge by the minute or by characters². That makes an AI app's cost behave like a utility bill:

1. Estimate requests per active user per month for your feature (a light user might make 20; a power user 600).
2. Measure tokens per request: the user's message, your instructions, any retrieved documents and the conversation so far (input), plus the answer (output).
3. Multiply by the model's price per token for input and output.
4. Add speech, embeddings, storage and servers.
5. Divide by what the user pays. That ratio is your AI gross margin.

A worked example with illustrative prices (check the providers' pages for current ones³⁴): suppose a request sends 3,000 input tokens and returns 500 output tokens, on a model priced at $0.50 per million input tokens and $2 per million output tokens. One request costs about $0.0025. A user making 300 requests a month costs about $0.75 — roughly ₹65. Swap in a premium model priced ten times higher and the same user costs ₹650 a month. That gap is why model choice is a business decision, not just a technical one.

Three things change the maths for Indian teams:

• Indian-language models and speech. Providers such as Sarvam publish rupee pricing for speech-to-text, text-to-speech and Indian-language LLMs², which makes voice-first and vernacular apps far more practical than they were.
• Subsidised compute for eligible startups and researchers under the IndiaAI Mission¹.
• Data protection. The Digital Personal Data Protection Rules, notified in November 2025 with an 18-month phased timeline⁵, apply to the personal data users type into AI features. Decide what you store, for how long, and whether prompts go to providers outside India — and say so in your privacy notice.

An AI feature that is right 90% of the time can still lose users if the other 10% is handled badly. Budget design time for:

• Streaming answers so users see progress instead of a spinner
• Showing sources for answers drawn from documents
• Clear "I'm not sure" behaviour instead of confident guesses
• Easy ways to correct, regenerate or report an answer
• Limits and plan upgrades explained before users hit them
• Undo for any action the AI takes on the user's behalf

People trust a slow answer more when they can see the work being done — a pattern known as the labor illusion. Use it honestly: show real steps ("searching your documents…"), not fake delays.

1. Prove the value with a narrow feature inside an existing product or a no-frills prototype before building a full app.
2. Start with the strongest model to learn what good looks like, then route traffic to cheaper models where quality holds.
3. Write the evaluation set first — 50–200 real questions with good answers. It makes every later decision faster.
4. Use managed vector search and auth instead of building infrastructure.
5. Ship with usage limits on day one. It is easier to raise a limit than to claw one back.

These are Science of Apps editorial estimates, consistent with our app development cost guide, which puts an AI-powered app at ₹12–60 lakh. They cover design, development, retrieval, guardrails, evaluation, testing and launch at the hourly bands Indian teams publish, before 18% GST and before usage costs. Pricing structures come from the model and speech providers' own pages²³⁴; the compute subsidy from the IndiaAI Mission¹. Model prices change often, which is why the worked example uses illustrative numbers. We review these ranges every six months.

Token — a chunk of text (roughly three-quarters of an English word) that models read and write; APIs bill by tokens. Inference — running a model to get an answer. RAG (retrieval-augmented generation) — finding relevant passages from your own data and giving them to the model with the question. Fine-tuning — further training an existing model on your examples. Evaluation set — a fixed list of questions and good answers used to score quality after every change. Guardrails — checks that block unsafe inputs and outputs.