The constantly changing pricing of AI and poor AI usage practices have surprised companies and, in some cases, led to enormous bills. AI adoption has entered a new phase. In addition to maximizing benefits, organizations must now actively manage costs. Fortunately, there are effective ways to do both.
“AI is an amazing tool, but it can become expensive when used incorrectly. If you use the most advanced and costly AI model for every task, the expenses can quickly pile up,” says Chief Technology Officer Juhana Juppo.
Examples of this have been reported in the United States. Many companies had become accustomed to fixed monthly fees and even encouraged employees to maximize AI usage. When some AI providers moved to consumption-based pricing, one large enterprise reportedly saw its monthly bill grow to USD 500 million.
Juppo compares AI to a chainsaw. It can significantly improve productivity, but in inexperienced hands it can also cause damage. As consumption-based pricing becomes more common and AI costs continue to change, organizations must pay closer attention to how much AI costs and how those costs can be managed, even in real time. Most importantly, however, companies should focus on the bigger picture and business value.
Developing and operating the most advanced AI models can cost AI providers tens of billions of euros. It is therefore understandable that the use of these models often comes with a significant price tag.
According to Juppo, lower-cost and even free AI models have emerged alongside premium models, and their performance has improved considerably. In many tasks, an affordable model can already deliver around 90 percent of the performance of the most advanced alternatives.
“If a task requires extensive reasoning capabilities or highly complex software development, using a leading model may be justified. At the same time, research shows that most users can achieve excellent results with a more affordable model in many situations. The key is to choose the right AI model for each task.”
Costs are also affected by the volume and scope of queries. For example, if an AI agent executes dozens or hundreds of queries at once, using the most expensive model is rarely the best option. Costs can also be controlled by keeping prompts concise and limiting the amount of source material supplied to the AI.
Now that AI costs have become a critical factor, Juhana Juppo highlights two key considerations. First, organizations need a robust AI architecture that guides how AI is used.
“We are no longer competing over who has the best AI model. What matters most is who has the best AI architecture. A strong architecture enables cost control while ensuring the secure management of data and context.”
A well-designed architecture makes it possible to select the most appropriate AI model, or even other technologies, for a specific task. AI initiatives in organizations typically also involve data platforms, integrations, security, and monitoring capabilities. Digia delivers these kinds of solutions to its customers, and according to Juppo, new tools for managing the AI ecosystem continue to emerge.
The second, and most important, consideration is the overall business perspective. What is the organization trying to achieve, and what combination of AI and human expertise best supports that goal? How are costs distributed? How do quality, security, and responsibility influence the outcome?
Juppo reminds organizations that productivity without quality has little value. It is not enough for customer service to become more efficient or for software code to be generated quickly. The end result must meet quality requirements and ideally be better than before. This must also be considered when selecting AI technologies.
“AI is not inherently expensive or inexpensive. Everything depends on how it is used. If AI creates significant value, it may be worth paying more for it. That is why organizations should not focus only on the cost of an AI model. They should focus on the overall business benefits.”
1. Choose the right AI model for each use case
The most advanced and expensive AI models are best suited for tasks that require the highest levels of quality and reasoning capability. For most tasks, however, more affordable models are more than sufficient, especially as AI capabilities continue to improve.
2. Pay attention to query volumes
AI agents in particular can generate a large number of queries, making model costs a much more important consideration. Organizations should also evaluate open-source models that can run in their own environments.
3. Control prompt length
The more data an AI model processes, the higher the cost. Avoid overly long prompts and do not provide unnecessary background material.
4. Use caching
If employees frequently ask similar questions, a previously generated answer can be retrieved from a cache instead of running a new query through an AI service.
5. Focus on the bigger picture
Important outcomes and significant savings may justify higher costs. Do not focus solely on the price of an AI model. Consider what you are trying to achieve and how AI can help you reach that goal.
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