Ai Marketing2 min read
You know your AI tools cost money, but do you know the four hidden ways they are quietly eating up your budget behind the scenes?
According to a recent breakdown by the Your Everyday AI podcast, token usage has exploded by up to 100x over the last few months as AI models get smarter and more autonomous. To manage your budget effectively, you need to understand what an AI token actually is, and the specific ways your agents are spending them.
What is an AI Token?
In simple terms, a token is the currency of artificial intelligence. It’s a segment of text—roughly three-quarters of an English word. Everything your AI reads, thinks, or writes is converted into tokens, and that is ultimately the metric your business pays for.
While the cost per token has dropped, the sheer volume of tokens being used has skyrocketed. Here are the four ways AI consumes these tokens.
1. Input Tokens
This is the most obvious cost. Every word you type into a prompt, and every PDF, spreadsheet, or document you upload, costs input tokens. The larger the file and the longer the prompt, the higher the cost.
2. Output Tokens
This is the deliverable. Whether it is text, code, or structured data, the response the model gives back to you is charged as output tokens.
3. Reasoning Tokens (The Hidden Cost)
This is where it gets expensive. Modern AI models now “think” by default before they answer. They reason through complex problems internally, mapping out their logic before generating a response. These unseen steps dramatically increase your token count, often without you realizing it.
4. Tool Use Tokens
When your AI agent steps outside of its core model—like searching the web, reading a live file, or running a piece of code—each of those tool invocations eats up more tokens. In automated or repeating workflows, tool use can compound costs rapidly.
The Real-World Impact
Imagine you have an AI agent automatically researching sales prospects. It isn’t just a simple input/output exchange. It’s reading websites (input), thinking about how the data aligns with your ideal customer profile (reasoning), using a search plugin to find recent news (tool use), and finally writing a summary for your CRM (output).
If you don’t understand how these four token types work together, your automated workflows could be burning through your budget without generating proportional value.
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