AI feature development cost in India starts around ₹1.5–2.5 lakh for a focused chatbot on your own documents and reaches ₹15 lakh or more for a multi-language, multi-source build. Unlike an ordinary feature, it also adds a bill that never stops: the model’s usage charges, which you pay on every request.
This guide splits the cost into three lines, build, run and keep-good, so you can compare quotes properly and budget the part most proposals leave out.
- *Key takeaways**
- A support chatbot built on a hosted model API is quoted at roughly ₹3–15 lakh on top of the app, plus a few hundred dollars a month to run (Ecorpit).
- RAG chatbots on your own data are quoted at ₹1.5–2.5 lakh (focused) up to ₹8–15 lakh (multi-language, multi-source) (Navtechy, Decipher).
- One guide puts monthly running cost of a standard RAG bot at ₹25,000–₹80,000 for model usage, vector database and monitoring (RaftLabs).
- Output tokens typically cost 4–5 times as much as input tokens (Machine Learning Plus).
How much does it cost to add an AI feature in India?
Expect ₹1.5 lakh to ₹15 lakh for most business AI features, and more only for custom models. A focused single-knowledge-base chatbot starts around ₹1.5–2.5 lakh, a standard build is ₹3–8 lakh, and multi-language or multi-source builds run ₹8–15 lakh (Decipher).
Whole AI apps are quoted wider: ₹6–60 lakh, and past ₹1 crore with computer vision or custom models (Ecorpit). Fine-tuning a model on your own data is quoted at ₹20–60 lakh and up (Innovative AIs).
These are vendor-published ranges, not quotes, and we haven’t verified them. Use them to sanity-check proposals.
What does a model in the loop actually add to the bill?
It adds three lines that a normal feature doesn’t have: usage charges, retrieval infrastructure and ongoing quality work.
| Line | What it covers | Pattern |
|—|—|—|
| Build | Integration, prompts, retrieval, guardrails, screens | One-off, quoted in lakh |
| Run | Model usage, vector database, hosting, monitoring | Monthly, grows with use |
| Keep-good | Testing answers, updating documents, handling model changes | Recurring effort |
The build line looks like any software project. India’s hourly rates are published at about $25–$80 against $100–$250 in North America (AVIXA). The other two lines are why an AI feature is not a one-time purchase.
How much does the model itself cost to run?
Hosted model APIs charge per million tokens, and the spread between models is huge: from about $0.05 per million input tokens for the cheapest to $30 input and $180 output for top-tier ones (BenchLM). Mid-tier models are listed at roughly $2.50–$3 input and $15 output.
Here is a worked example using those mid-tier prices. It is our arithmetic, not a quote.
1. A feature handles 20,000 requests a month.
2. Each request sends 2,000 input tokens (the question plus retrieved text) and returns 500 output tokens.
3. Input: 40 million tokens at $3 per million is $120.
4. Output: 10 million tokens at $15 per million is $150.
5. Total: about $270 a month, before the vector database, hosting and monitoring.
Notice that a quarter of the tokens produce more than half the cost. Short answers are cheaper than long ones. Published per-token maths can also miss real bills by a wide margin once traffic patterns, retries and long documents are included (Fungies), so run a two-week pilot before you fix a number.
Do you need fine-tuning, or is retrieval enough?
Start with retrieval. RAG, which means fetching your own documents and handing them to the model with each question, is the cheaper path and covers most business cases: help desks, policy lookups, document search.
Fine-tuning changes the model’s behaviour using your data and is quoted at ₹20–60 lakh and above. It makes sense when you need a consistent format or tone at high volume. It does not make a model learn facts reliably, and your facts change. Documents in a retrieval index can be updated by Tuesday; a fine-tuned model has to be retrained.
What pushes the number up?
Five things move an AI feature’s price most.
1. Integrations. Each connection to your ERP, CRM or WhatsApp is roughly 3–8 days of work, depending on the API (RaftLabs).
2. Languages. Budget 20–40% extra for Hindi and regional languages (Navtechy).
3. Data quality. Scanned PDFs, duplicate files and outdated policies cost cleaning time before any model sees them.
4. Accuracy bar. An internal search tool can be wrong sometimes. A tool that quotes prices to customers cannot, and needs checks and human review built in.
5. Volume. More users means a larger monthly bill, not only a larger build.
What should a good AI quote include?
A good quote is itemised under the three lines above, so you can compare totals fairly. Look for these entries before you compare prices.
1. Scope in plain words. Which questions or tasks the feature must handle, and which it will refuse.
2. Integrations listed one by one. Each one is days of work, so a quote that says “integrations” without a list is hiding the biggest variable.
3. Model and hosting named. Which model, who holds the API account, and whether the bill is in your name. You should own the account.
4. A monthly running estimate. With the assumptions written down: requests a month, tokens per request, document count.
5. A cap or alert on usage. A runaway loop or a popular launch can multiply a bill. Ask how spend is monitored.
6. Test set and handover. The questions used to check accuracy, delivered to you with the code.
If two quotes differ by 5x, the gap is almost always in lines 2, 4 and 6.
The angle quotes miss: who checks the answers?
Most proposals price the build and the model bill, and say nothing about testing. An AI feature can give a confident wrong answer, so someone has to own a set of test questions with known good answers, re-run them whenever the documents or the model change, and read a sample of real conversations.
Ask each vendor three questions. Who writes and maintains the test questions? What happens when the model provider retires or changes a model? Who reviews wrong answers after launch? If the answer is “you,” price that time. It is a real cost, just not on the invoice.
What we’ve seen at DECK IT
DECK IT has built software from Ahmedabad since 2017, and we now add AI features to the systems we build, alongside our own AI products such as MedSlay, which writes clinical documentation for Indian clinics. The pattern we see repeatedly: the first demo works in a week and the estimate gets set from that demo. The cost sits in everything after it, the messy documents, the integrations and the test set.
Our advice is to buy a small, fixed-scope pilot first, on one real workflow with real documents, and decide on the full build from what it shows. If you are weighing a bespoke build against an off-the-shelf tool, our custom vs off-the-shelf guide covers the trade-off, and the custom development cost guide covers the non-AI side of the budget. We build these features through our custom software and web development work.
Frequently asked questions
How much does it cost to add AI to an existing app in India?
How much does a RAG chatbot cost in India?
What does an LLM API cost per month?
Do I need to fine-tune a model for my business?
What are the ongoing costs after launch?
How long does an AI feature take to build?
Conclusion
Budget an AI feature as build, run and keep-good, and ask every vendor to price all three. Fix a small pilot first, measure real usage, and only then commit to the full build.
If you want an itemised estimate for a specific feature, send us the workflow and a sample of the documents.
Talk to DECK IT about your project →
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