A flat illustration style image of a vending machine that outputs little robots in exchange for tokens. Style: use black and white and light gray as primary colors, with some gold or maroon as accents. context: a factory that builds robots

Is There a Cost to Using CreateAI For Students?

CreateAI projects use AI models to process requests and generate responses. This usage has a real cost to ASU, but students are not personally charged for standard CreateAI usage.

Student-owned projects receive a monthly token allocation. CreateAI shows the percentage of that allocation used so students can understand how much capacity remains.

Student workers using CreateAI as part of their ASU employment follow the faculty/staff usage limits and guidance for their work-related projects.

What Is a Monthly Token Allocation?

Tokens represent the information processed by an AI model when a project receives a request and generates a response.

Each student-owned project receives a monthly allocation. Your usage view shows the percentage used rather than a dollar amount.

How Is AI Usage Calculated?

AI usage is based on the number of tokens processed and the model used. Different models have different costs, so projects using more tokens or higher-cost models will use their monthly allocations faster.

Am I Charged for Using CreateAI?

No. Students are not personally billed for standard CreateAI usage.

If you own a project, you are responsible for monitoring and managing that project’s allocation. If you use a project owned by a faculty member, staff member, or another student, the usage is attributed to that project and its builder.

Users and app viewers are not charged for interacting with someone else’s project.

How Can I Review My Project Usage?

Open your profile menu in CreateAI Builder and select Usage.

The student usage view shows:

  • Your active projects.

  • The percentage of each project’s monthly allocation used.

  • A visual indicator of the remaining project capacity.

Dollar amounts are not displayed in the student view.

How Can I Make My Allocation Last Longer?

If you own the project and can select its model, choose a more cost-effective model that can still complete the task successfully.

You can also use your allocation thoughtfully by:

  • Keeping requests focused.

  • Avoiding unnecessary repeated prompts.

  • Testing project changes intentionally.

  • Using higher-cost models only when the project requires their capabilities.

If you are using someone else’s project as an app viewer, the builder selects the model and manages its usage.

What Happens When a Project Reaches 100%?

When a project uses its full monthly allocation, it may be temporarily paused until the allocation resets at the beginning of the next month.

Our Ask

Because AI usage has a real cost, even though ET currently covers standard usage, we encourage students to choose models intentionally and use resources responsibly. Thoughtful usage helps us continue providing broad access to the ASU community.

If you have any questions, please let us know. We’re here to support you!


Keep Reading

Agents in Generative AI

Zohair Zaidi

Agents in generative AI are semi-autonomous entities that collaborate and interact dynamically, allowing them to solve complex problems and combine specialized capabilities for greater efficiency and adaptability.

How to Use Knowledge Base (RAG)

Jinjing Zhao

Explore an overview of the Knowledge Base and Retrieval Augmented Generation (RAG) methods. Learn about the different types of Knowledge Base retrieval and understand the distinctions between the Knowledge Base and system prompts.

CreateAI Platform Available LLM Models

Faith Timoh Abang

We are proud to offer 70+ models including multi-modal (voice, image, text) for the ASU community to access securely on the CreateAI Platform. Users can find the following models available for experimentation and use in CreateAI Compare, CreateAI Chat, and CreateAI Builder (access request required). Originally posted: January 1, 2025. 

 

 

Breakdown of RAG Model Parameters, Settings and Their Impact

Kofi Wood

Retrieval-Augmented Generation (RAG) is an advanced approach in natural language processing that integrates information retrieval and generative language modeling. Unlike traditional language models that generate responses solely based on their pre-trained knowledge, RAG combines retrieval mechanisms with generative models to enhance the relevance and accuracy of its responses. This hybrid framework works by first retrieving relevant documents or information from a predefined knowledge base (e.g., databases, documents, or PDFs) and then using a generative model (such as a transformer-based model) to synthesize a response that incorporates the retrieved context.