7 Simple and Proven ways to Reduce your LLM Costs

Large language models (LLMs) have transformed the field of natural language processing, enabling a vast array of applications from chatbots and writing assistants to code generation and data analysis. However, the immense computational power and knowledge required to train and deploy these models come at a significant cost. As businesses and organisations increasingly adopt LLMs, managing the associated expenses becomes a concern. Fortunately, there are several strategies and techniques available to optimise LLM usage and dramatically reduce costs.

This article outlines 7 methods to help you cut your expenditures while still harnessing the power of these advanced language models.

1. Choosing the Right LLM Model for the Use Case

2. Using Multi-modal Routing to a Cheaper Model

3. Prompt Compression

4. Memory Management

5. Semantic Caching

6. Rate Limiting Techniques

7. LLM Cost Analysis Tools

1. The Right LLM Model for the Use Case

Selecting an appropriate LLM based on your specific use case can significantly reduce costs. For instance, switching from GPT-4 to GPT-3.5 Turbo can be up to 20x cheaper. This is especially effective when the high capabilities of GPT-4 are not necessary for the task at hand.

Examples

Simple Customer Service: If you are building a chatbot for basic customer service inquiries, GPT-3.5 Turbo is sufficient instead of GPT-4.

Text Summarisation: Use GPT-3 or GPT-2 instead of GPT-4 for summarising documents/articles, as their capabilities may suffice at a lower cost.

Sentiment Analysis: Choose smaller, specialised models like BERT or RoBERTa, which are efficient for this task and cheaper than GPT-4.

Language Translation: For specific language pairs/domains, consider models like MarianMT or Opus-MT, which are tailored for translation and more cost-effective than GPT-4.

2. Multi-modal Routing

This involves using a less expensive model for simple queries and a more advanced model for complex ones. It requires a system that routes queries to the appropriate model based on complexity.

Example: A customer service system uses GPT-3.5 Turbo for straightforward questions like "What are your business hours?" and switches to GPT-4 for more complex issues like "Can you help me troubleshoot this technical problem?"

3. Prompt Compression

Reducing the length of prompts while retaining essential information can decrease the number of tokens used, thus lowering API costs. This method involves rewriting prompts concisely without losing the context.

Examples:

Instead of sending a lengthy query, "Can you please provide a detailed explanation on how to integrate a payment gateway with my website using PHP?", compress it to "How to integrate payment gateway with PHP website?"

Writing Assistance: When using an AI writing assistant for proofreading or editing, reduce costs by providing shorter prompts or instructions. Instead of pasting an entire document and asking the AI to proofread it, break it down into smaller sections or paragraphs and provide it to the AI one at a time. You'll use fewer tokens and pay less for the API call. The key is to give the AI enough context to understand the task without including unnecessary extra text.

4. Memory Management

Efficient memory management involves retaining only the essential details of a conversation to avoid unnecessary token usage. This is particularly useful in long customer service interactions.

Example: A customer service bot summarises previous interactions into key points and uses this summary for context in ongoing conversations, reducing the total number of tokens required.

5. Semantic Caching

Storing responses to frequent queries in a cache allows for immediate retrieval without re-querying the LLM, thus saving costs on repeated queries.

Example: For common questions like "How do I reset my password?", the system retrieves the answer from the cache instead of querying the LLM each time.

6. Rate Limiting

Implementing rate limits on the number of prompts per user per day/month helps control costs and prevent abuse. This can involve setting hard limits or reducing response times after a certain threshold.

Example: A service allowing users to make up to 100 queries per month, with any additional queries facing slower response times or being blocked, effectively manages costs.

7. Use an LLM Cost Analysis Tool

Employing tools to track and analyse the costs associated with LLM usage helps identify and address cost drivers effectively. This includes monitoring monthly usage and identifying areas where a significant portion of costs come from a specific feature.

Summary

By implementing these cost-reduction techniques, organisations can significantly lower their LLM operational expenses without compromising performance and efficiency. Adopting the right model for your use case, utilising multi-modal routing, compressing prompts, managing memory efficiently, leveraging semantic caching, applying rate limits, and using cost analysis tools are all practical strategies to optimise your AI usage. Ultimately, these measures don't just deliver cost-effective outcomes, they also ensure that your AI-driven solutions are more scalable and sustainable in the long term.

EPAM DIAL

At EPAM we are agnostic. We do not force-fit solutions into a static vendor-defined box, rather we build dynamically adjustable solutions precisely tailored to clients' requirements for growth, agility, and competitive advantage.

Our approach to Generative AI and LLMs is no different. We built DIAL to make GenAI solutions more accessible and efficient for businesses. It is model-agnostic and rich with features...

https://epam-rail.com/

DIAL - which stands for Deterministic Integrator of Applications and LLMs) - is an orchestration platform designed specifically to streamline and improve the way businesses use of Generative AI and Large Language Models (LLMs).

Here's a breakdown of what it does:

• Combines LLMs with code: DIAL integrates the power of LLMs with traditional, deterministic code. This allows for more secure, scalable, and customizable AI solutions.

• User-friendly platform: DIAL provides a unified user interface that businesses can use to leverage a variety of public and private LLMs, along with other tools and applications. This makes it easier for businesses to develop AI-powered solutions.

• Faster experimentation and innovation: DIAL helps businesses experiment with different LLMs and AI applications quickly, leading to faster innovation cycles.

• Open source and collaborative: Parts of DIAL are open-source, which means businesses can customize it to fit their specific needs and contribute to the overall development of the platform.