The short answer
Implementing AI has two cost parts: an upfront investment for design, integration with your systems and testing, and a recurring cost for AI model usage, infrastructure and support. The total depends mostly on scope: how many processes the AI handles, how many systems it connects to and how many conversations or tasks it runs each month.
That's why two projects with the same name, say "a WhatsApp chatbot", can cost very different amounts: one only answers FAQs, while the other checks inventory, books appointments and logs every customer in your CRM.
The 5 cost components
- Implementation. Discovery, solution design, development, integrations and testing. It's a one-time payment and usually the largest part upfront.
- AI model usage. Providers like Anthropic (Claude), OpenAI (GPT) and Google (Gemini) charge by the amount of text processed, measured in tokens. It's a variable cost that grows with usage.
- Infrastructure. Servers, a database, automation tools like n8n and, for voice agents, telephony.
- Integrations. Connecting AI to your CRM, ERP, calendar or WhatsApp may require licenses or plans on those platforms.
- Maintenance and improvement. Reviewing conversations, tuning answers, updating information and fixing issues. AI gets better with use, but someone has to do that work.
How to estimate model usage cost
A token is roughly a piece of a word. Every message the model reads or writes uses tokens, and providers publish a price per million tokens. For a quick estimate:
- Work out how many conversations or tasks the AI will handle per month.
- Estimate how many tokens each one uses, including instructions and the information the model looks up.
- Multiply the two and apply the per-million-token rate of the model you choose.
For example, if your agent handles 3,000 conversations a month and each uses about 4,000 tokens, that's 12 million tokens a month. With that figure and the provider's rate you get an approximate monthly cost. Picking the right model for each task, instead of always using the most powerful one, can cut this cost significantly.
What pushes the cost up or down
| Factor | Raises the cost | Lowers the cost |
|---|---|---|
| Scope | Many processes at once | One well-defined use case |
| Integrations | Several systems without APIs | Tools with APIs or ready-made connectors |
| Volume | Thousands of conversations a day | Moderate volume or business hours only |
| Information | Scattered, messy data | Organized documents and data |
| Channels | Voice, WhatsApp, web and email | A single channel to start |
What about an AI voice agent?
An AI voice agent adds per-minute costs: telephony, speech recognition, the language model and the synthetic voice. So it's budgeted by call volume and average call length. Even so, it's usually compared against the hours your team spends on the phone today and the calls that are missed after hours.
How to start without a big investment
- Pick a single process that repeats often and has clear rules, such as answering FAQs or booking appointments.
- Start with an MVP on one channel, measure results for a few weeks, then expand.
- Build on what you have: your CRM, WhatsApp and existing documents are the foundation.
- Decide upfront how you'll measure success: hours saved, customers served after hours or qualified leads.
How do you know if it's worth it?
Compare the total cost with what AI saves or brings in. A simple way: multiply the hours per month your team spends on the task by their hourly cost, then add the value of opportunities you lose today, like unanswered messages or missed calls. If that number is higher than the solution's monthly cost, the project makes sense.
At weqode we give you a clear estimate after a discovery call, with no commitment. To see what an agent could do for you, check out our page on AI agents for business.