Debunked by AI: The future of misinformation on social

[mouakter9005]

I first tested AI tools in 2019 to automate repetitive parts of my workflow. It seemed like the perfect solution: Let the code execute the manual stuff I was too bored to do.

However, I quickly discovered hidden costs to everything I did. My automation script needed a server to live on. The server needed maintenance and monitoring. And if the backend APIs changed, my script would no longer work.

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AI today has similar overhead costs, but at a far greater scale. For instance, Sam Altman recently tweeted that saying “yes” and “please” to ChatGPT cost tens of millions of dollars in computing country wise email marketing listresources.

This got me thinking: How much does AI cost businesses, and when does it make sense to integrate AI?

In this article, I’ll walk you through different kinds of AI models and how much AI solutions cost based on the model type. Whether you’re a startup founder, an SMB, or an enterprise business, you’ll learn how to budget for AI in your company.

Table of Contents

How much does AI cost?
How is pricing determined for AI?
How much should you spend on AI in your business?
How much does AI cost?
AI costs vary widely depending on the type of solution, business model, data quality, model variant, usage patterns, and more.

Let’s break down the cost of AI based on four commonly used AI model types.


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The Cost of Large Language Models (LLMs)
LLMs are trained on massive amounts of data (think billions of tokens) to understand and generate human-like language. I use LLMs extensively in my workflow — whether it is to prompt ChatGPT to draft an email, analyze photos through Gemini, or get content ideas from Claude.

Businesses can use LLMs across multiple departments to do things like:

Answer user complaints through chatbots.
Screen resumes, create SOPs, or help decide business pricing models.
AI-assisted code development or debugging.