When considering whether to integrate generative AI, specifically large language models (LLMs), into your business or product, understanding the costs involved is crucial. Organizations have two main options: using LLMs via an API on a pay-per-use model or self-hosting the models on their own infrastructure. This post will guide you through the factors to consider for both approaches and how to estimate your LLM costs effectively.
Why Incorporate Generative AI?
Generative AI can transform the way businesses interact with data and customers. By leveraging LLMs, you can feed your data into the model to generate targeted recommendations, insights, and predictions. This can significantly enhance the user experience by personalizing interactions and providing relevant, contextual information.
Incorporating generative AI can lead to:
Enhanced Product Features: Adding generative AI capabilities can justify higher product pricing or create premium features. For example, an AI-powered recommendation engine or intelligent customer support system.
Increased Customer Retention: Customizing the user experience makes your product more engaging and "sticky," improving customer loyalty and reducing churn.
Operational Efficiency: Internally, generative AI can automate repetitive tasks, such as drafting emails, summarizing documents, or extracting information from large text datasets. I've seen implementations that cost mere dollars per month while automating hours of manual work. This can lead to significant cost savings and efficiency gains.
Pay-Per-Use Model: Using Large Language Models via API
For most organizations, using LLMs through an API, like the Chat GPT API, is the most practical option. This pay-per-use model allows companies to avoid the upfront costs and complexities of setting up their own infrastructure. With APIs, you only pay for what you use, making it a flexible and scalable choice.
How It Works:
Token-Based Pricing: API usage is typically priced based on the number of tokens submitted (input) and returned (output) by the model. A token can be as short as one character or as long as one word, depending on the language. For example, “AI” and “GPT” each count as one token.
Input and Output Tokens:
Input Tokens: These include both the user input and the system prompt, which guides the entire conversation. The system prompt is typically a predefined set of instructions and is counted as input tokens for every interaction. For example, a system prompt of 50 tokens combined with a user input of 20 tokens results in 70 input tokens.
Output Tokens: These are the tokens generated by the model in response to the input. Output tokens are typically more expensive than input tokens because they require more computational resources. For instance, if the model generates a response of 100 tokens, you are billed separately for these tokens.
Calculating Costs:
Calculate the Number of Input Tokens:
Combine the token count for the system prompt and user input. For example, a system prompt of 50 tokens and a user input of 20 tokens result in 70 input tokens.
Multiply the number of input tokens by the input token price set by the API provider. For instance, if the input token price is $0.0004, the cost would be 70 × $0.0004 = $0.028 per interaction for the input tokens.
Calculate the Number of Output Tokens:
Estimate the typical length of the model’s response. For example, if the expected output is 100 tokens, you use this number for your cost calculation.
Multiply the number of output tokens by the output token price. If the output token price is $0.001, the cost would be 100 × $0.001 = $0.10 per interaction for the output tokens.
Total Cost per Interaction:
Add the input token cost and the output token cost. Using the previous examples, the total cost per interaction would be $0.028 (input) + $0.10 (output) = $0.128 per interaction.
Estimate Monthly Usage:
Multiply the cost per interaction by the expected number of interactions per month. If you expect 1,000 monthly interactions, your monthly cost would be 1,000 × $0.128 = $128.
Finally, a better llm cost calculator!
Estimating the number of tokens accurately can be challenging, especially if you’re not familiar with tokenization. RTB-AI’s cost calculator simplifies this process by automatically calculating the number of tokens based on the examples you provide. Unlike other calculators, which require you to run your text through a tokenizer manually, our tool uses the OpenAI tokenizer called TikToken to determine the number of input and output tokens for you. This allows you to quickly estimate costs without needing to manually count tokens or guess the token lengths. You can also play around with our pre-populated examples just to get a sense of how much these models cost and the difference between the models' costs.
Quantifying the Value of LLM Usage
Understanding the cost is one side of the equation; quantifying the value it brings to your business is the other. LLMs can enable new features, improve customer experiences, and streamline processes, all of which can contribute to revenue growth and customer retention.
Revenue Opportunities: If you’re using LLMs to power features that you can charge for, like personalized recommendations or advanced customer support, the cost can be offset by the additional revenue these features generate. Check out my colleague Palle’s post on how to price AI features for more insights.
Operational Efficiency: LLMs can automate tasks that would otherwise take hours of manual work, such as summarizing documents or extracting information. This can lead to significant time and cost savings.
Customer Retention: Enhancing user experience with smarter and faster interactions can lead to better customer satisfaction and loyalty, which may justify the expense.
Bottom Line: Forecast Costs and Find the Right Fit
By carefully estimating the length of your inputs and outputs, the frequency of model usage, and the type of model you need, you can forecast your LLM costs with reasonable accuracy. For most businesses, using an API-based LLM is the best option due to its flexibility and lower upfront costs. However, if data security is paramount, self-hosting may be worth considering despite the higher initial investment.
Whichever path you choose, understanding the costs and planning accordingly will help you leverage LLMs effectively without breaking your budget.
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