Everyone wants AI. But how much does it actually cost?

Last updated: 08.09.2026 08:00

Anyone who uses AI must also understand how it is billed – after all, even artificial intelligence doesn't work for free. This raises the question of whether using AI helps companies save money.

More is... more!

When using AI systems or language models – such as chatbots or automated text generators – costs are often calculated not on a flat-rate basis but based on the actual computing power used.

1. A common billing model is based on so-called tokens as the primary unit of measurement. Tokens are small units of text or words that the AI system processes. A token can be a single word, part of a word, a syllable, or even just a single character. When text is entered into an AI and when a response is generated, the text is broken down into tokens and counted. The cost depends on how many tokens the AI processes. A single prompt can very quickly generate five-digit numbers of tokens.

2. The more tokens that are processed, the more computing time and energy are required. A long input text or an extensive response therefore means not only more tokens, but also more computing power, more energy, and thus higher costs.

3. In general, AI models are becoming increasingly powerful and processing larger amounts of data – which automatically increases token consumption. Furthermore, the more complex a model becomes, the higher the computational effort per token. And as more and more companies make intensive use of AI and usage grows exponentially, the infrastructure is also being utilized more heavily. That, too, costs money.

4. With regard to innovative AI agents – which no longer merely provide support but independently take on and complete tasks – billing will sooner or later be calculated not only based on tokens or users, but also per service, per automation, or per hour of labor saved. AI is thus becoming infrastructure for which users must pay to access and utilize.

5. And finally, AI providers and operators are currently investing heavily in data centers, chips, and energy infrastructure. These investments must pay off

Transparency is strongly recommended!

The longer and more intensively AI is used, the higher the costs for every company. So, if you want to avoid unpleasant surprises, you should know which AI models are being used in your own company, by whom, and for what purpose – hence the need to be wary of “shadow AI” – as well as how many tokens are being used. Since costs are typically spread across different departments or divisions, there should be transparency regarding this. So-called AI gateways offer suitable dashboards that clearly display exactly this usage and distribution.

Does using AI in customer service save money?

Honest answer: In the long run, yes. That’s because inquiries – such as those regarding order status, rate inquiries, cancellation periods, etc. – can easily be automated using chatbots or voice assistants. This works around the clock without requiring an employee to be on standby. For more complex issues, AI-based assistance can support employees by making real-time suggestions, summarizing conversations, or providing information. This reduces AHT, allowing more inquiries to be processed in less time. AI-supported quality management through automated call analysis, etc., reduces errors; training bots minimize the training burden for new employees; and more accurate call volume forecasts reduce the need for excess capacity in workforce planning.

However, especially in the beginning, implementing AI costs money – for the technology itself, for training, and for adapting processes. Good consulting is essential here. Effective AI requires clean and well-structured data as well as technical integration, and, of course, employees and customers must embrace the AI solutions; otherwise, the benefits diminish. The automation and efficiency gains achieved typically materialize one to three years after the solution is launched.