LLM-Powered Applications with prompt caching

You’ve probably used large language models (LLMs) like ChatGPT, Groke, Gemini, LLaMA, or Anthropic, and maybe you're even thinking about integrating one into your own applications to add more power to what you offer. Understanding how these models work, especially how to estimate their usage and manage costs, is key to getting the most out of them without breaking the bank. Here’s a simple breakdown of the important terms and how you can calculate your usage and costs when using LLMs in your projects.

I have included the calculation of the usage with "prompt caching" as well.

Prompt caching is a game-changer for managing the costs of using large language models (LLMs). By storing and reusing responses to frequently asked questions, you can dramatically cut down (reducing costs by up to 90% and latency by up to 85% for long prompts) on token usage. Instead of processing the same query thousands of times, you only do it once and serve the cached response to subsequent users. This not only slashes your monthly billing but also improves response times and reduces the load on your LLM, making it a smart strategy for any high-traffic application!

Let’s start with some basic terms:

• Token: Think of this as a piece of text. It can be a word or even just a character. For example, "Hello, world!" counts as 4 tokens.
• Prompt Tokens: These are the tokens you send to the model when asking it something. For instance, the phrase "What’s the weather today?" consists of the prompt tokens.
• Completion Tokens: These are the tokens the model uses to give you an answer. If the model replies with "It’s sunny today," those are the completion tokens.
• Context: This is the amount of text the model can consider at one time, including both what you ask and how it responds.
• Context Window: The total number of tokens the model can handle at once. If you exceed this, the model might "forget" earlier parts of the conversation.
• Pricing per 1,000 Tokens: The cost to process 1,000 tokens, with different rates for prompts and completions.
• Interaction: A full exchange with the model, from your question to its answer.
• Model Version: Different versions of the model offer different capabilities, context sizes, and pricing.
• Usage Dashboard: A tool to track how many tokens you’ve used and what it’s costing you.
• API Rate Limits: The maximum number of requests you can make to the model in a given time period.

How to Estimate AI Model Costs:

1. Determine Token Usage: Estimate how many prompt and completion tokens your project will use. For example, if you’re building a chatbot, think about the average number of tokens per interaction and multiply that by the number of expected interactions.
2. Apply the Pricing: Use the pricing model (e.g., $0.03 per 1,000 prompt tokens) to calculate costs based on your token usage.
3. Monitor and Adjust: Keep an eye on your spending using the Usage Dashboard and make adjustments as needed to stay within your budget.

The Impact of Prompt Caching

Prompt caching is a real game-changer when it comes to managing the costs of using LLMs. Here’s how it works: instead of having the LLM process the same question over and over, you store (or “cache”) the response the first time it’s asked. Then, whenever another user asks the same question, the system serves up the cached response instead of processing a new one. This can reduce token usage by up to 90% and latency by up to 85% for long prompts.

For example, let’s say 10,000 users ask about their data usage. Without caching, you’d consume around 280,000 tokens. With caching, you drop that down to just 28 tokens for the initial query, plus a tiny cost for retrieving the cached response. This could turn an $84 cost into just a few cents, making it a no-brainer for high-traffic applications. Not only does caching slash your monthly billing, but it also speeds up response times and reduces the load on your LLM, making everything run more smoothly.

By understanding these concepts and leveraging strategies like prompt caching, you can optimize your LLM usage and keep your costs in check. That way, you can focus on delivering great experiences to your users without worrying about your budget.

The table below summarizes other use cases, where the context is bigger than the above example.