How to Estimate Large Language Models Costs ?

Estimating Costs for Large Language Models in Web Apps
Hey everyone, welcome back to the channel! Today, we're diving deep into the world of web applications and the nitty-gritty of costs associated with using large language models. If you've ever wondered about the financial side of deploying applications with models like GPT-4, this one's for you.
In this video, we're going to break down a super practical guide on estimating costs, using OpenAI's GPT-4 as an example. So, buckle up and let's get started!
First things first, we need to understand the pricing model. OpenAI charges based on the number of tokens processed by their models. For GPT-4, it's three cents for inputting a thousand tokens and six cents for the output of the same. Now, let's make some assumptions to get a ballpark figure for our application.
I'm assuming a typical input of 200 tokens and an output of 800 tokens. Why? Because in real-world scenarios, questions or prompts to these models are usually short, but the responses are huge! Now, imagine this: our application is in the hands of a hundred analysts, each throwing in around 30 prompts per day. That's 252 working days in a year.
Now, let's do some math. For GPT-4, the annual cost per analyst is around $407, totaling approximately $40,700 for a hundred analysts. If we go for the beefier 32,000 token model, the annual cost per analyst bumps up to about $816, totaling around $81,648 for a hundred analysts.
But how did we get to these numbers? Well, I'll show you the breakdown. We calculated costs based on 200 input tokens and 800 output tokens per analyst per day. It's all about breaking down the costs into input and output token components, giving you a clear picture of where your money is going.
This framework is super handy for developers and businesses planning to incorporate large language models into their applications. Remember, these are just assumptions, and you should tweak them based on your specific use case.
For a more detailed breakdown and additional resources, check out the blog post linked in the description. And if you find this video helpful, don't forget to hit that like button, share it with your fellow developers, and of course, subscribe for more deep dives into the exciting world of AI, machine learning, and web development.
Happy coding, everyone! 💻🚀 #AI #MachineLearning #WebDevelopment #OpenAI #GPT4 #CostEstimation #Programming #TechTutorial #DeveloperGuide
00:22 Estimating the Cost of OpenAI's Models
01:22 Assumptions for Cost Estimation for LLM apps
02:10 Calculating the Cost for Different Analyst Scenarios
02:44 Detailed Breakdown of Cost Calculation for LLM App
03:57 How to count LLM tokens in the prompt ?
05:15 Final Thoughts and Recommendations on LLM Cost Calculations
05:52 Conclusion
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