Estimating AI Inference Costs

1. The Core Formula for a Single AI Request

Let's start with the cost of a single interaction with an AI model. As we discussed, the total cost is the sum of what you pay to send information to the model (input) and what you pay to get a response from the model (output).

Most AI providers, like OpenAI and Anthropic, price their models per 1 million tokens. Therefore, the formula for a single request looks like this:

• Input Cost = (Number of Input Tokens / 1,000,000) × Price per 1M Input Tokens
• Output Cost = (Number of Output Tokens / 1,000,000) × Price per 1M Output Tokens
• Total Cost per Request = Input Cost + Output Cost

This article provides a very clear template for this calculation.

To see this formula in action, let's watch a short video segment that walks through a live calculation for a single API call.

2. From a Single Request to Monthly Operational Cost

Calculating the cost for one request is useful, but the real power comes from scaling this up to predict the monthly operational cost of a feature across your entire user base. This is how you forecast budgets and determine if a feature is sustainable.

To do this, we need three additional pieces of information:

1. Number of Users: How many people will use this feature?
2. Usage Frequency: How often will each person use it?
3. Time Period: We'll be calculating for a month (typically assumed to be 30 days).

The scaled-up formula is:

Total Monthly Cost = Cost per Request × Number of Users × Avg. Requests per User per Month

Let's look at a comprehensive example that puts all these pieces together.

As the article demonstrates, the process is straightforward:

1. Calculate the cost for a single, representative query.
2. Determine the total number of queries per day (Users × Queries per User per Day).
3. Multiply the daily query count by 30 to get the monthly query count.
4. Multiply the total monthly queries by the cost per query to find the total monthly cost.

The table in the article also effectively highlights the dramatic cost difference between a high-end model (GPT-4o) and a more economical one (GPT-4o-mini), a crucial architectural trade-off we'll explore more in the next lesson.

3. Guided Practice: Modeling a Fintech AI Assistant

Now, let's apply this yourself. Imagine you are the architect for a new feature in a fintech mobile app: an "AI Financial Assistant" that users can chat with to ask questions about their spending habits.

Let's define the parameters for our cost model.

Feature Scenario: AI Financial Assistant

• User Base: The app has 10,000 active users who will have access to this feature.
• Usage Pattern: On average, an active user makes 8 requests (asks 8 questions) to the assistant per month.
• Token Count per Request:
  • Input: 1,500 tokens. This includes the system prompt ("You are a helpful financial assistant..."), a summary of the last few messages for conversation history, and the user's current question.
  • Output: 250 tokens. This is the AI's generated answer.

Model Pricing (Illustrative):

• Model A (Premium): GPT-4o
  • Input Price: $5.00 per 1M tokens
  • Output Price: $15.00 per 1M tokens
• Model B (Economy): GPT-4o-mini
  • Input Price: $0.15 per 1M tokens
  • Output Price: $0.60 per 1M tokens

Let's calculate the monthly cost step-by-step.

Step 1: Calculate the Cost per Request for Each Model

Model A (Premium):

• Input Cost: (1,500 / 1,000,000) * $5.00 = $0.0075
• Output Cost: (250 / 1,000,000) * $15.00 = $0.00375
• Total Cost per Request (Model A): $0.0075 + $0.00375 = $0.01125

Model B (Economy):

• Input Cost: (1,500 / 1,000,000) * $0.15 = $0.000225
• Output Cost: (250 / 1,000,000) * $0.60 = $0.00015
• Total Cost per Request (Model B): $0.000225 + $0.00015 = $0.000375

Step 2: Calculate the Total Monthly Requests

• Total Monthly Requests = Users × Requests per User per Month
• Total Monthly Requests = 10,000 × 8 = 80,000 requests

Step 3: Calculate the Total Monthly Cost for Each Model

Model A (Premium):

• Total Monthly Cost = Cost per Request × Total Monthly Requests
• Total Monthly Cost = $0.01125 × 80,000 = $900 per month

Model B (Economy):

• Total Monthly Cost = $0.000375 × 80,000 = $30 per month

This simple model gives you, the architect, immediate and powerful insight. The choice of model results in a 30x difference in monthly operational cost ($900 vs. $30). This allows you to have an informed discussion with your team: "Is the performance of the premium model worth an extra $870 every month for this feature?"

Conclusion

You can now translate a feature concept into a concrete financial forecast. This is a crucial bridge between design, engineering, and business strategy.

Key Takeaways:

• The Cost Formula is Your Foundation: The total cost of an AI request is always the sum of its input and output token costs.
• Scale with User Data: To find the monthly cost, you scale the single-request cost by your total number of users and their average usage frequency.
• Modeling Informs Decisions: Creating a simple cost model, like the one we just did, is an essential architectural step. It makes the economic trade-offs of your design choices explicit and quantifiable.
• Model Choice is a Key Variable: The cost difference between different tiers of AI models is not incremental; it can be orders of magnitude. Your calculations will directly inform which model is appropriate for a given task.

Next Steps

Now that you can calculate costs, the next logical question is how to manage them strategically. In the next lesson, we will delve into model tiering, a powerful strategy for balancing cost and performance by intelligently routing different types of user requests to different AI models.