Short answer: The cost of an AI project is made up of four items: one-time development (analysis, integration, user interface), model usage fees (based on the amount of text processed), infrastructure (servers, database, storage) and maintenance (monitoring, improvement, content updates). Whether the investment makes sense is calculated by comparing it with the hourly cost of the work being automated.
What items make up the cost?
| Item | What does it include? | What drives it? |
|---|---|---|
| Development | Requirements analysis, flow design, integrations, dashboard | Scope and number of integrations |
| Model usage | Language model API fees | Amount of text processed (per token) |
| Infrastructure | Servers, database, search index, backups | Number of users and data volume |
| Maintenance and improvement | Monitoring, bug fixes, knowledge base updates | Fixed monthly fee or hourly |
| Data preparation | Cleaning and organizing documents | Initial data quality |
The most common mistake is to look only at the development fee. Model usage and maintenance are monthly expenses and should be included in the budget from the start.
How do you estimate model usage fees?
Language models are generally priced by the amount of text processed (tokens). Three questions are enough for an estimate:
- How many transactions will there be per month? (number of conversations, documents, emails)
- How much text does an average transaction contain? (question + context + answer)
- Which model will be used? (more powerful models cost more per unit)
In practice, many tasks do not need the most powerful model. Simple classification and summarization jobs are handled by faster, cheaper models; only the difficult tasks are routed to a large model. This routing significantly reduces the total usage fee.
How do you calculate return on investment?
A simple and honest method:
- Current cost: Monthly hours spent on the work to be automated × hourly cost. For example, the time of an employee who spends 3 hours a day answering messages.
- Share the AI will take on: Measured during the pilot; use a conservative rate for your first estimate.
- Monthly savings: (1) × (2) − monthly model and maintenance costs.
- Payback period: One-time development fee ÷ monthly savings.
Savings are not limited to hours. Revenue effects such as requests that are no longer missed outside business hours, faster quote turnaround and fewer errors should be noted separately, but factored into the decision conservatively.
How can you keep costs low?
- Start small: Run a pilot with a single process, measure it, then expand.
- Use ready-made components: Instead of writing everything from scratch, choose proven components and integration with your existing systems.
- Choose the model based on the task: Do not send every job to the most expensive model.
- Caching and rules: Use fixed answers for frequently asked questions and caching for repetitive operations.
- Set up measurement from the start: You cannot manage costs without seeing how much usage each operation generates.
What should you look for when getting a quote?
Ask for development, the estimated monthly model usage, infrastructure and maintenance to be listed separately in the quote. Also have the contract state who owns the code and the data, and the criteria by which the project will be considered "successful" at the end of the pilot. To clarify which task you should start with, see our article AI for Business: Where to Start?
Want to calculate what AI could gain for your business? As part of our AI solutions, we review your processes and produce an itemized cost and return-on-investment table.
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Frequently Asked Questions
It depends on the scope. Pilot projects that target a single process and are built with ready-made components start with a relatively low budget. The main investment comes at the rollout stage, after the pilot has succeeded.
If usage limits, per-transaction caps and monthly budget alerts are set up, increases can be kept under control. These settings should be configured at the start of the project.
It can be advantageous at high volumes and where data privacy is critical. At low and medium volumes, however, using an API is usually more economical because of hardware and maintenance costs.