What AI Really Costs: Understanding Token-Based Pricing

The AI flat rate has an expiry date. Recent developments show a clear trend: leading providers, including Microsoft for GitHub Copilot, are switching to token-based billing. In the future, customers will pay based on actual computing power consumed instead of a fixed monthly price. Reports show the significant impact: monthly costs per developer can increase from around **29 Euros to up to 750 Euros**. Anyone who wants to know what AI really costs must understand this shift.

This example doesn't just concern one tool. It's a signal for the entire market. Computationally intensive agents drive inference costs, and providers pass these on to customers.

Why Prices Are Changing

The reason is sober: The costs for AI computing power have risen sharply, primarily due to agents that perform many steps autonomously. A flat rate only works as long as average consumption remains calculable. With agentic AI, consumption fluctuates too much.

Specifically with models like Copilot: During transition phases, business customers often receive a capped token credit. Once this is exhausted, the provider bills based on consumption. Reports describe that regular basic tariffs, with intensive use by complex AI agents, can consume a large part of the monthly budget within just a few hours.

What Token-Based Pricing Means for Businesses

Three points are changing:

• Predictability decreases. Instead of a fixed license sum, variable consumption arises, depending on usage and task.
• Controlling becomes more important. AI expenditures belong in ongoing monitoring, not just in annual license planning.
• Architecture influences costs. The number of model calls a workflow generates directly determines the bill.

Four Levers to Manage AI Costs

1. Measure consumption. Maintain a simple overview of token or cost consumption per team and use case.
2. Choose models appropriately. Not every task requires the most expensive model. Smaller models often handle routine tasks more cost-effectively.
3. Set budgets and alerts. Define thresholds at which a team is informed or a cheaper model takes over.
4. Keep workflows lean. Reduce unnecessary model calls, for example, through clear prompts and intermediate steps without AI.

This approach is essentially FinOps for AI: making consumption visible, managing it, and regularly reviewing it.

What Consulting You Really Need

Pure license and contract questions are often clarified directly by the provider or an IT service provider. When it comes to strategically introducing AI and considering costs, benefits, and governance together, strategic AI consulting is worthwhile. 6Rocks categorizes use cases by impact and cost before any budget is spent.

What You Should Do Specifically

1. This week: Inventory which AI tools you use and how they are billed.
2. Check consumption: Identify applications with the highest anticipated token consumption.
3. Define budget: Set an upper limit and an alert value per team.
4. Assign models: Allocate routine tasks to more cost-effective models.
5. Adjust quarterly: Review consumption versus benefit and adjust accordingly.

AI costs are becoming an ongoing management metric. Those who make them visible maintain control instead of being surprised by the next bill.