11 Proven Strategies to Reduce Large Language Model (LLM) Costs

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LLMs have become everyday tools for many businesses and individuals. However, the costs of running these models can quickly add up. Businesses and developers are seeking ways to minimize the costs associated with running these powerful tools. LLMs, such as GPT-4, offer immense potential for natural language processing tasks but can be expensive to operate. In this blog post, we'll explore 11 proven strategies to help you save money on LLM costs without compromising performance or efficiency.

Understanding LLM costs

Before we jump into the cost-saving strategies, it's crucial to understand what factors influence the costs of running or using Large Language Models. LLMs are incredibly complex and require massive amounts of computational power to train and operate. The main factors that affect LLM costs are the size of the model, the number of requests made, and the amount of computational resources required for each request.

When it comes to pricing models, most LLM providers charge based on the number of tokens processed. A token can be a word, a part of a word, or even a single character. The more tokens your requests contain, the higher the cost. Some providers also offer tiered pricing plans based on volume, with lower per-token rates for higher usage tiers. It's a similar story for self-hosted Open Source models: Generally speaking: the bigger the model and the more tokens you request, the more expensive it gets.

It's important to note that not all LLMs are created equal. Some models are more resource-intensive than others, and the choice of model can significantly impact your costs. For example, Llama2, one of the most well-known LLMs, comes in different sizes ranging from a compact "7B" model to the colossal "72B" model. The larger the model, the more accurate and nuanced its responses tend to be, but this also means higher costs per request.

Knowing these factors - the size of the model, the number of requests, and the computational resources required - helps us to formulate cost saving strategies.

You can expect the following optimization strategies from reading this article:

  1. Optimize your LLM prompt
  2. Use task-specific, smaller models
  3. Cache responses
  4. Batch requests
  5. Use prompt compression
  6. Use model quantization
  7. Fine-tune your model
  8. Implement early stopping
  9. Use model distillation
  10. Use RAG instead of sending everything to the LLM
  11. Summarize your LLM conversation

1. Optimize your LLM prompt

One of the easiest and most effective ways to save money on LLM costs is to optimize your prompts. You see, every time you send a request to an LLM, you're charged based on the number of tokens processed. Tokens are essentially the building blocks of the text, including words, punctuation, and even spaces. The more tokens your prompt contains, the more you'll end up paying.

So, how do you optimize your prompts? It's all about being concise and specific. Instead of throwing a wall of text at the LLM and hoping for the best, take some time to craft a clear and focused prompt. Cut out any unnecessary words or phrases and get straight to the point.

For example, let's say you want the LLM to generate a product description for a new smartphone. Instead of sending a prompt like:

"Please write a product description for our latest smartphone model. It should mention the key features and specifications, such as the screen size, camera resolution, battery life, and storage capacity. Try to make it engaging and persuasive."

You could optimize it to something like:

"Generate a compelling product description for a smartphone with a 6.5-inch display, 48MP camera, 5000mAh battery, and 128GB storage."

See the difference? The optimized prompt is much shorter but still conveys all the essential information needed for the LLM to generate a relevant product description.

But being concise doesn't mean sacrificing clarity. Make sure your prompts are still easily understandable and provide enough context for the LLM to work with. If you're too vague or ambiguous, you might end up with irrelevant or low-quality outputs, which defeats the purpose of using an LLM in the first place.

Another tip is to avoid using overly complex or technical language in your prompts, unless it's absolutely necessary for your specific use case. Remember, LLMs are trained on a wide range of text data, so they're better at handling everyday language and common terminology.

In summary, optimizing your LLM prompts is all about finding the right balance between brevity and clarity. By crafting concise and specific prompts, you can significantly reduce the number of tokens processed per request, which translates to lower costs in the long run. So, take some time to review and refine your prompts – your wallet will thank you!

2. Use task-specific, smaller models

When working with Large Language Models, it's easy to get caught up in the hype surrounding the biggest and most powerful models out there. But here's the thing: those massive, general-purpose LLMs come with a hefty price tag. If you want to save some serious cash, it's time to start thinking about using task-specific, smaller models instead.

Sure, models like GPT-4 are incredibly versatile and can handle a wide range of tasks, but do you really need all that power for your specific use case? Probably not. By opting for a smaller, task-specific model, you can get the job done just as effectively without breaking the bank.

Take a moment to consider the specific tasks you need your LLM to perform. Is it sentiment analysis? Named entity recognition? Text summarization? Chances are, there's a smaller model out there that's been fine-tuned specifically for that task. And guess what? These specialized models often deliver better results than their larger, more general counterparts when it comes to their specific area of expertise.

Not only will you save money by using a smaller model, but you'll also benefit from faster processing times and reduced computational resources. It's a win-win situation! So, before you go all-in on the biggest, baddest LLM on the block, take a step back and consider whether a task-specific, smaller model might be the smarter choice for your needs. Your wallet (and your stakeholders) will thank you for it.

3. Cache responses

Picture this: you've just implemented a state-of-the-art language model for your application, and users are loving it. The only problem? The costs are starting to add up, and you're wondering how to keep your budget in check without sacrificing performance. Enter caching, a tried-and-true technique that can help you save on LLM costs while keeping your users happy.

At its core, caching is all about storing frequently accessed data so that it can be quickly retrieved when needed. In the context of online services, this often means storing popular or trending content that users are likely to request again in the near future. By keeping this data readily available, caching systems can reduce retrieval time, improve response times, and take some of the load off of backend servers.

Traditional caching systems rely on an exact match between a new query and a cached query to determine whether the requested content is available in the cache. However, this approach isn't always effective when it comes to LLMs, which often involve complex and variable queries that are unlikely to match exactly. This is where semantic caching comes in.

Semantic caching is a technique that identifies and stores similar or related queries, rather than relying on exact matches. This approach increases the likelihood of a cache hit, even when queries aren't identical, and can significantly enhance caching efficiency. Tools like GPTCache make it easy to implement semantic caching for LLMs by using embedding algorithms to convert queries into embeddings and a vector store for similarity search. (Similar to how Retrieval Augmented Generation works ).

Here's how it works: when a new query comes in, GPTCache converts it into an embedding and searches the vector store for similar embeddings. If a similar query is found in the cache, GPTCache can retrieve the associated response and serve it to the user, without having to run the full LLM pipeline. This not only saves on computational costs but also improves response times for the user.

Of course, no caching system is perfect, and semantic caching is no exception. False positives (cache hits that shouldn't have been hits) and false negatives (cache misses that should have been hits) can occur, but GPTCache provides metrics like hit ratio, latency, and recall to help developers gauge the performance of their caching system and make optimizations as needed.

If you implement any one of these tips - caching is the one to go for. It will save you the most money and is quite easy to implement.

4. Batch requests

Batching requests is a smart way to optimize your LLM usage and save on costs, especially if you're running a self-hosted model. Instead of sending individual requests to the LLM API every time you need to process some text, you can group multiple requests together and send them as a single batch. This approach has two main benefits.

First, batching requests can significantly speed up your application. LLMs are generally faster when processing text in batches, as they can parallelize the computation and make better use of their hardware resources. This means you can get your results back more quickly, leading to a snappier user experience.

Second, batching can help you save money on LLM costs, particularly if you're hosting the model yourself. When you send requests individually, there's a certain amount of overhead involved in each API call. By batching your requests, you can reduce this overhead and make more efficient use of your computational resources. Over time, this can add up to significant cost savings.

It's worth noting that if you're using an LLM hosted by a provider like OpenAI or Anthropic, batching may not have as much of an impact on your costs. These providers typically charge based on the number of tokens processed, regardless of whether the requests are sent individually or in batches. However, you'll still benefit from the performance improvements that come with batch processing.

Implementing request batching in your application is relatively straightforward. Most LLM APIs support sending multiple input texts in a single request, so you'll just need to modify your code to collect a batch of texts before sending them to the API. You can experiment with different batch sizes to find the optimal balance between performance and latency for your specific use case.

For your self-hosting needs, vLLM is an outstanding inference server which supports batching out of the box.

5. Use prompt compression

One powerful technique to consider is prompt compression using LLMLingua - a tool created by Microsoft Research. LLMLingua is a language model that can compress natural language prompts into shorter, more efficient representations without losing the original meaning. It leverages a compact, well-trained language model, such as GPT2-small or LLaMA-7B, to intelligently identify and remove non-essential tokens from your prompts. By trimming down the prompt to its core components, LLMLingua enables efficient inference with large language models, resulting in significant cost savings without compromising performance.

The magic of LLMLingua lies in its ability to achieve up to 20x compression while minimizing any loss in the quality of the generated outputs. This means you can feed your LLMs with streamlined prompts that maintain the essential information and context, ensuring accurate and relevant responses.

Imagine you have a lengthy prompt that includes background information, examples, and specific instructions for the LLM. While these details are important for providing context, they can also add unnecessary tokens that contribute to higher costs. LLMLingua steps in and carefully analyzes the prompt, identifying and removing the non-essential elements. It preserves the core message and intent, creating a compressed version of the prompt that is more cost-effective to process.

By integrating LLMLingua into your LLM workflow, you can significantly reduce the number of tokens processed per request, leading to lower costs and faster inference times. It's like having a smart assistant that optimizes your prompts behind the scenes, ensuring you get the most value out of your LLM usage.

6. Quantize your model

Quantization is a technique that can help you reduce the size of your LLM without sacrificing too much performance. The idea behind quantization is to reduce the precision of the model's weights, effectively representing them with fewer bits. This means less memory usage and faster inference times, which translates to lower costs.

There are different types of quantization techniques you can apply to your LLM. One common approach is post-training quantization, where you take a trained model and quantize its weights. This method is relatively straightforward and doesn't require retraining the model. Another option is quantization-aware training, where you incorporate quantization into the training process itself. This allows the model to adapt to the reduced precision during training, potentially leading to better performance.

When applying quantization to your LLM, it's important to strike a balance between model size and performance. You don't want to quantize too aggressively, as this may lead to a significant drop in accuracy. Experiment with different quantization levels and evaluate the impact on your specific use case. In many cases, you can achieve substantial cost savings with minimal performance degradation.

It's worth noting, that this technique mainly only applies for your self-hosted models, as most LLM providers don't allow you to use quantized versions of their models.

7. Fine-tune your model

When it comes to using Large Language Models, one size doesn't always fit all. While off-the-shelf LLMs can handle a wide range of tasks, they may not be the most efficient or cost-effective solution for your specific use case. That's where fine-tuning comes in.

Fine-tuning is the process of adapting a pre-trained LLM to a specific domain or task by further training it on a smaller, more focused dataset. By doing so, you can create a customized model that is better suited to your unique requirements, resulting in improved performance and reduced costs.

So, how does fine-tuning help you save money on LLM usage? Well, imagine you're running a customer support chatbot for a tech company. A general-purpose LLM might be able to handle common queries, but it may struggle with more technical or product-specific questions. This could lead to longer conversations, more back-and-forth, longer, more elaborate prompts and ultimately, higher costs.

By fine-tuning the model on a dataset of past customer interactions and product documentation, you can create a chatbot that is more adept at understanding and responding to tech-related queries. This means faster resolutions, fewer tokens consumed per conversation, and lower overall costs.

Moreover, fine-tuning allows you to use smaller, more specialized models instead of relying on massive, general-purpose LLMs. These smaller models are often faster and cheaper to run, while still delivering excellent results within their domain of expertise.

In general, fine-tuning is a powerful technique for adapting LLMs to your specific needs, often resulting in better performance and lower costs. By creating custom models that are tailored to your domain or task, you can reduce token consumption, speed up processing times, and ultimately, save money on your LLM usage.

8. Implement early stopping

Early stopping is a simple yet effective technique that can help you save on LLM costs by preventing the model from generating unnecessary tokens.

By implementing early stopping, you allow the model to halt the generation process as soon as it produces an acceptable output, rather than continuing to generate tokens until it reaches the specified maximum. This approach can significantly reduce the number of tokens processed per request, leading to lower costs.

To implement early stopping, you'll need to define a set of criteria that determine when a response is considered satisfactory. These criteria can vary depending on your specific use case and requirements. For example, you might set a threshold for the minimum number of characters or sentences in the generated text, or you could look for specific keywords or phrases that indicate a complete response.

Once you've established your early stopping criteria, you can modify your LLM integration to check the generated text against these criteria after each token is produced. If the criteria are met, the generation process can be terminated, and the response can be returned to the user.

Implementing early stopping does require some additional development effort and may not be suitable for all applications. However, for many use cases, it can provide a valuable cost-saving measure without significantly impacting the quality of the generated text. By reducing the number of unnecessary tokens processed, you can make your LLM usage more efficient and cost-effective.

One very prominent use case for early stopping are chatbots. By providing a simple stop-button to the user, they can stop the LLM output when they are satisfied. Why should they stop the LLM when they are satisfied? Well, because no one has time to wait for a slow LLM to write more text then necessary. Win-win situation - user get's answers faster and you save on LLM costs.

9. Use model distillation

Model distillation is a technique that can help you save on LLM costs by transferring knowledge from a large, expensive model to a smaller, more efficient one. It's like having a wise, experienced teacher (the large model) pass on their knowledge to a bright, eager student (the smaller model). The goal is to create a compact model that can still perform well on your specific tasks without the hefty price tag.

Here's how it works: you start by training a large, powerful LLM on a broad range of data. Or - alternatively - you use an already pretrained, general-purpose model like GPT-4 or Anthropic Claude 3. This model becomes your "teacher." Then, you create a smaller "student" model with a more lightweight architecture. The student model learns from the teacher by mimicking its behavior and trying to match its predictions. By doing this, the student model can absorb the knowledge of the teacher without having to go through the same extensive training process.

The beauty of model distillation is that you can tailor the student model to your specific needs. You can focus on the tasks and domains that matter most to your application, making the student model more specialized and efficient. This way, you get to leverage the insights of the large teacher model without having to pay for its ongoing usage.

Implementing model distillation does require some technical know-how, but the benefits can be significant. You'll need to carefully design your student model architecture, choose the right distillation techniques, and fine-tune the process to get the best results. But with some experimentation and iteration, you can create a lean, mean, cost-effective LLM that punches above its weight.

An early example of model distillation is Microsofts Orca-2 model.

10. Use RAG instead of sending everything to the LLM

One clever approach for cost saving is to use Retrieval-Augmented Generation (RAG) instead of sending every single query and piece of context to the language model. RAG is a technique that combines information retrieval with language generation, allowing you to tap into the knowledge stored in external databases or documents without relying solely on the LLM.

Here's how it works: when a user sends a query, the RAG system first searches through a pre-indexed database of relevant information to find the most appropriate passages or snippets. These retrieved pieces of text are then fed into the LLM along with the original query. The LLM uses this additional context to generate a more informed and accurate response. Imagine you want to ask some questions about a book. Instead of sending the entire book to the LLM, you can use RAG to retrieve relevant information - relevant for your current query - from the book and send only the most important parts to the LLM.

By using RAG, you can reduce the number of tokens sent to the LLM, as the retrieved information helps to provide context and answer parts of the query. This means you'll be using fewer API calls and processing fewer tokens overall, resulting in lower costs.

Moreover, RAG can help improve the quality of your LLM's responses by providing it with relevant, up-to-date information that might not have been included in its original training data. This is particularly useful for domains that require specialized knowledge or deal with rapidly changing information.

Keep in mind that while RAG can significantly reduce your LLM costs, it does require some additional setup and maintenance. However, the long-term benefits of using RAG—both in terms of cost savings and improved response quality—make it a worthwhile investment for many businesses and developers.

11. Summarize your LLM conversation

Sending entire chat conversations to an LLM for processing can be costly, especially if the conversation is lengthy or if you need to process multiple conversations. To reduce costs, you can leverage tools like LangChain, which provides a Conversation Memory interface. This interface allows you to summarize the conversation and send only the summary to the LLM, rather than the full conversation history.

By summarizing the conversation, you reduce the number of tokens that need to be processed by the LLM, leading to lower costs. The summary captures the essential points and context of the conversation, ensuring that the LLM still has enough information to generate meaningful responses.

Here's how you can use LangChain's Conversation Memory interface to summarize conversations and save on LLM costs:

  1. Integrate LangChain into your application or system where the conversations take place.

  2. As the conversation progresses, use LangChain to process and store the conversation history.

  3. When you need to send the conversation to an LLM for further processing or response generation, utilize LangChain's Conversation Memory interface to create a summary of the conversation.

  4. Send the summarized conversation to the LLM instead of the entire conversation history. The LLM can then process the summary and generate a response based on the essential points and context provided.

  5. Continue the conversation, repeating steps 2-4 as needed, while leveraging the summarization capabilities of LangChain's Conversation Memory interface.

By adopting this approach, you can significantly reduce the number of tokens sent to the LLM, resulting in lower costs without sacrificing the quality of the conversation or the LLM's ability to generate relevant responses.

What's with self-hosting your own model?

You might be wondering why self-hosting a pretrained open-source LLM isn't specifically on our list of cost-saving strategies. After all, wouldn't running your own model eliminate the need for costly API calls and usage fees? While it's a tempting idea, the reality is that self-hosting an LLM is rarely a cost-effective solution.

First, consider the computational resources required to run a large language model. These models are massive, often containing billions of parameters. To run them efficiently, you'll need powerful hardware, including GPUs or TPUs, which can be expensive to purchase and maintain. If you MUST use self-hosting - consider using quantization to reduce hardware requirements - as outlined in the chapters above.

Moreover, self-hosting an LLM requires significant technical expertise. You'll need a team of experienced engineers to set up, configure, and optimize the model for your specific use case. This process can be time-consuming and costly, especially if you don't have the necessary expertise in-house.

Finally, there's the issue of ongoing maintenance and updates. Language models are constantly evolving, with new versions and improvements released regularly. Keeping your self-hosted model up-to-date and performant requires a dedicated team of experts, which can add to the overall costs.

In most cases, it's more cost-effective to use a commercial LLM provider that can offer you the scalability, reliability, and support you need at a predictable cost. While self-hosting might seem like a good idea on paper, it's rarely the most economical choice in practice.

Conclusion

At the end of the day, running large language models doesn't have to be a budget-buster. With a little bit of know-how and the right strategies up your sleeve, you can unlock the full potential of these AI powerhouses without watching your costs spiral out of control.

It's all about being smart and strategic. Optimizing your prompts, caching responses, and batching requests can help you get the most bang for your buck on every single token. And by leveraging techniques like model quantization, fine-tuning, and distillation, you can create custom-tailored models that are leaner, meaner, and more cost-effective than ever before.

But hey, why stop there? Tools like RAG, LangChain, and LLMLingua are like your trusty sidekicks on this cost-saving adventure. They'll help you slash token consumption, tap into the wealth of external knowledge, and compress those prompts down to size – all without breaking a sweat.

Now, I won't sugarcoat it – some of these strategies might take a bit of technical savvy and good old-fashioned hard work to implement. But trust me, it's worth rolling up your sleeves and diving in. Every penny saved on LLM costs is a penny earned, ready to be reinvested in growing your business or exploring even more mind-blowing AI innovations.

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