What an AI-powered feature actually costs to run monthly
API and token costs, usage scaling, and how a managed integration avoids surprise bills.
The short answer
Building an AI feature is a one-time cost. Running it is ongoing — usually ₹5,000–₹80,000+/month for SMB workloads, depending on message volume, document processing, and which models handle each task.
Costs scale with usage (tokens/API calls), not seat count. A viral WhatsApp line or heavy PDF automation can spike bills if uncapped.
What you pay for every month
Beyond the initial build, budget for these line items:
- LLM API usage — Input + output tokens per request. A short FAQ reply might cost paise; a 10-page document summary costs rupees.
- Embeddings & search — Indexing your knowledge base for RAG (retrieval). Re-index when content changes.
- Speech / vision — Voice notes on WhatsApp, image recognition — separate API meters.
- Hosting — Agent runtime, queues, logs, vector database. Often ₹3,000–₹25,000/mo depending on scale.
- WhatsApp / messaging fees — Meta conversation charges on top of AI costs for Business API.
- Support & tuning — Prompt updates, new intents, model regression checks — retainer or hourly.
Realistic monthly ranges (India SMB)
Illustrative bands — your mileage depends on traffic and workflow depth:
- Light FAQ bot — ~500 conversations/mo, short answers — ₹3,000–₹12,000 API + hosting.
- WhatsApp lead qualification — ~2,000 chats/mo, multi-turn — ₹15,000–₹45,000 all-in.
- Document processing — ~200 invoices/mo with extraction — ₹8,000–₹30,000 depending on page length.
- Internal copilot — 20 staff, moderate daily use — ₹10,000–₹35,000.
- Heavy agent stack — Multiple workflows, high volume, large context — ₹50,000–₹1,50,000+. Needs caps and routing.
What causes surprise bills
These patterns show up after launch:
- Unbounded conversation length — Users chat endlessly; each turn re-sends full history.
- Wrong model for task — Using flagship model for “hello” classification.
- No caching — Same FAQ answered from scratch every time.
- Batch jobs without approval — Nightly “summarise everything” on huge datasets.
- Missing rate limits — Bot exposed publicly without abuse protection.
- Model price changes — Providers update pricing; uncapped contracts hurt.
How managed integration keeps costs predictable
This is a core reason businesses use a managed layer instead of raw API keys in a spreadsheet:
- Monthly usage band — Included tokens/messages; overage quoted upfront.
- Model routing — Cheap model first; expensive model only when needed.
- Alerts at 80% — Time to throttle or upgrade before invoice shock.
- Hard caps — Feature degrades gracefully (shorter answers, human handoff) instead of unlimited spend.
- Centralised billing — One invoice, not five vendor dashboards.
How to budget before you build
Ask your vendor or internal team for:
- Expected conversations or documents per month (use last quarter’s manual volume).
- Average turns per conversation and average message length.
- Which actions use AI vs fixed rules (rules are nearly free).
- 12-month projection if volume doubles — AI costs usually scale linearly or better with routing.
- Written cap policy — What happens at 100% of budget.
Next steps
Estimate volume from existing WhatsApp, email, or form traffic — not wishful growth.
Pair this with our model choice and privacy guides before signing a build contract.
Need help with this?
We build what this guide describes.
Tell us about your business and timeline — honest scope and quote, usually within one business day.
Contact Zulo Labs