11 Proven Strategies to Reduce Large Language Model (LLM) Costs
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You reduce LLM costs in two ways: send fewer tokens per request, and use the smallest model that does the job well. This guide covers 11 strategies that do one or both, from prompt optimization and caching to quantization, distillation, RAG and conversation summaries. If you only implement one of them, make it caching. It saves the most money and is quite easy to set up.
LLMs such as GPT and Claude have become everyday tools for many businesses and individuals, and their costs add up quickly. The strategies below help you save money without giving up performance or efficiency.
Understanding LLM costs
Before getting to the strategies, it helps to understand what drives the cost of running or using Large Language Models. LLMs need a lot of computing power to train and operate. The main cost factors are the size of the model, the number of requests, and the compute each request needs.
Most LLM providers charge by the number of tokens processed. A token can be a word, part of a word or even a single character. The more tokens your requests contain, the more you pay. Some providers also offer volume tiers, with lower per-token rates at higher usage. It's a similar story for self-hosted open-source models: the bigger the model and the more tokens you request, the more expensive it gets.
The choice of model has a big impact on cost. For example, Qwen3, one of the best-known open model families, comes in sizes from a compact "0.6B" model to a large "235B" model. Larger models tend to give more accurate and nuanced responses, but each request costs more.
These factors (model size, number of requests and compute per request) are what the following strategies target:
- Optimize your LLM prompt
- Use task-specific, smaller models
- Cache responses
- Batch requests
- Use prompt compression
- Quantize your model
- Fine-tune your model
- Implement early stopping
- Use model distillation
- Use RAG instead of sending everything to the LLM
- Summarize your LLM conversation
1. Optimize your LLM prompt
Optimizing your prompts is one of the easiest ways to save on LLM costs. Every request is billed by the number of tokens processed, and tokens are the building blocks of the text: words, punctuation and even spaces. The more tokens your prompt contains, the more you pay.
To optimize a prompt, be concise and specific. Instead of throwing a wall of text at the LLM and hoping for the best, write a clear, focused prompt. Cut unnecessary words and phrases and get straight to the point.
Say you want the LLM to write a product description for a new smartphone. Instead of 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 send:
"Generate a compelling product description for a smartphone with a 6.5-inch display, 48MP camera, 5000mAh battery, and 128GB storage."
The optimized prompt is much shorter and still contains all the information the LLM needs for a relevant product description.
Concise doesn't mean unclear, though. Your prompts still need to be easy to understand and give the LLM enough context. Vague or ambiguous prompts lead to irrelevant or low-quality outputs, which defeats the purpose.
Also avoid overly complex or technical language unless your use case needs it. LLMs are trained on a wide range of text, so they handle everyday language and common terminology best.
Prompt optimization comes down to balancing brevity and clarity. Concise, specific prompts reduce the tokens processed per request, which lowers costs over time. Take some time to review and refine your prompts. Your wallet will thank you!
Refining prompts by hand gets slow once you have more than a few of them. DSPy optimizes prompts automatically against a metric and a set of examples, which also makes it easier to move a pipeline to a smaller, cheaper model later.
2. Use task-specific, smaller models
It's easy to get caught up in the hype around the biggest and most powerful LLMs. But those large, general-purpose models come with a hefty price tag. If you want to save serious money, use task-specific, smaller models instead.
The largest GPT or Claude models can handle a wide range of tasks, but do you really need all that power for your use case? Probably not. A smaller, task-specific model can often do the job just as well for a fraction of the cost.
Consider which tasks your LLM actually performs. Sentiment analysis? Named entity recognition? Text summarization? Chances are there's a smaller model fine-tuned for exactly that task. Within their specialty, these models often deliver better results than their larger, general counterparts.
Smaller models are also faster and need fewer computational resources. Before you go all-in on the biggest LLM on the block, check whether a task-specific, smaller model is the smarter choice. If different requests need different model sizes, LLM routing sends each request to the cheapest model that can handle it.
3. Cache responses
Caching stores frequently requested data so it can be returned quickly the next time someone asks for it. In online services, this usually means keeping popular content ready, which cuts retrieval and response times and takes load off the backend servers. For an LLM application, a cache hit means you don't pay for the LLM call at all.
Traditional caches need an exact match between a new query and a cached one. That rarely works for LLMs, because users phrase their queries in many different ways and exact repeats are unlikely. This is where semantic caching comes in.
Semantic caching identifies and stores similar or related queries instead of relying on exact matches. That raises the chance of a cache hit even when queries aren't identical. Tools like GPTCache implement semantic caching for LLMs by converting queries into embeddings and using a vector store for similarity search (similar to how Retrieval Augmented Generation works).
When a new query comes in, GPTCache converts it into an embedding and searches the vector store for similar embeddings. If it finds a similar query in the cache, GPTCache returns the stored response without running the full LLM pipeline. This saves compute costs and gives the user a faster response.
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. GPTCache provides metrics like hit ratio, latency and recall so you can measure how well your cache performs and tune it.
Providers now also cache on their side. OpenAI applies prompt caching automatically, and Anthropic offers it as an opt-in feature. In both cases, a repeated prompt prefix (a long system prompt, for example) is billed at a reduced cached-input rate. Put the static parts of your prompt first to benefit from it.
If you implement only 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 LLM usage, especially for self-hosted models. Instead of sending each request to the LLM API individually, you group several requests and send them as a single batch. This has two benefits.
First, batching can speed up your application considerably. LLMs are generally faster when processing text in batches, because they can parallelize the computation and make better use of the hardware. You get results back sooner, and the user experience feels snappier.
Second, batching saves money, particularly if you host the model yourself. Each individual API call carries some overhead. Batching reduces this overhead and uses your computational resources more efficiently, which adds up to real savings over time.
If you use an LLM hosted by a provider like OpenAI or Anthropic, grouping requests yourself doesn't change the per-token price. Both providers do offer dedicated batch APIs, though: you submit a file of requests, get the results within 24 hours and pay 50% less (OpenAI Batch API, Anthropic Message Batches API). For anything that doesn't need an immediate answer, such as nightly classification or evaluation runs, that halves the bill.
Request batching is fairly easy to implement. Most LLM APIs accept multiple input texts in a single request, so you only need to collect a batch of texts before sending them. Experiment with batch sizes to find the right balance between throughput and latency for your use case.
For self-hosting, vLLM is an outstanding inference server that supports batching out of the box.
5. Use prompt compression
Prompt compression with LLMLingua, a tool from Microsoft Research, shortens natural language prompts without losing their meaning. LLMLingua uses a compact, well-trained language model, such as GPT2-small or LLaMA-7B, to identify and remove non-essential tokens from your prompts. Its successor, LLMLingua-2, replaces that small language model with a BERT-level encoder (for example XLM-RoBERTa-large) that was trained on compression examples generated with GPT-4. The large language model then runs on the trimmed prompt, which saves costs without hurting performance.
LLMLingua achieves up to 20x compression with minimal loss in the quality of the generated outputs. Your LLM still gets the essential information and context it needs for accurate, relevant responses.
Imagine a long prompt with background information, examples and specific instructions. These details provide context, but they also add tokens and cost. LLMLingua analyzes the prompt, removes the non-essential parts and keeps the core message and intent, giving you a compressed prompt that is cheaper to process.
Adding LLMLingua to your LLM workflow reduces the tokens processed per request, which means lower costs and faster inference. It's like having an assistant that optimizes your prompts behind the scenes.
6. Quantize your model
Quantization reduces the size of your LLM without sacrificing too much performance. It lowers the precision of the model's weights, so each weight is stored with fewer bits. That means less memory usage and faster inference, which translates to lower costs.
There are different quantization techniques. A common one is post-training quantization, where you take a trained model and quantize its weights. It is fairly simple and doesn't require retraining the model. The other option is quantization-aware training, which builds quantization into the training process itself. The model adapts to the reduced precision during training, which can lead to better performance. (For how far this can go, see our post on BitNet.)
When you quantize an LLM, balance model size against performance. Quantize too aggressively and accuracy can drop significantly. Experiment with different quantization levels and measure the impact on your use case. In many cases, you get substantial cost savings with minimal loss in quality.
Quantization mainly applies to self-hosted models. Closed-model providers like OpenAI and Anthropic don't offer quantized versions of their models. Hosts of open models are different: Together AI, for example, lists the quantization (such as FP8) of each model it serves, and OpenRouter lets you filter providers by quantization.
7. Fine-tune your model
Off-the-shelf LLMs handle a wide range of tasks, but they are not always the most efficient or cost-effective option for your use case. That's where fine-tuning comes in.
Fine-tuning adapts a pre-trained LLM to a specific domain or task by training it further on a smaller, focused dataset. The result is a customized model that fits your requirements better, performs better and costs less to run.
How does that save money? Imagine you run a customer support chatbot for a tech company. A general-purpose LLM might handle common queries but struggle with technical or product-specific questions. That leads to longer conversations, more back-and-forth, longer and more elaborate prompts, and ultimately higher costs.
If you fine-tune the model on past customer interactions and product documentation, the chatbot gets better at understanding and answering tech-related queries. That means faster resolutions, fewer tokens per conversation and lower overall costs.
Fine-tuning also lets you use smaller, specialized models instead of large, general-purpose LLMs. These smaller models are often faster and cheaper to run, while still delivering excellent results in their domain. Our guide on how to finetune LLMs walks through the process for an open model, and our post on fine-tuning GPT-4o mini does the same for one of OpenAI's hosted models.
8. Implement early stopping
Early stopping saves on LLM costs by stopping the model from generating unnecessary tokens.
With early stopping, the model halts generation as soon as it has produced an acceptable output, instead of generating tokens until it hits the maximum length. This can significantly reduce the tokens processed per request.
To implement early stopping, define criteria for when a response counts as satisfactory. The criteria depend on your use case and requirements. For example, you might set a minimum number of characters or sentences, or look for specific keywords or phrases that indicate a complete response.
Then change your LLM integration to check the generated text against these criteria after each token. Once the criteria are met, stop the generation and return the response to the user.
Early stopping takes some extra development effort and doesn't suit every application. For many use cases, though, it cuts costs without noticeably affecting the quality of the generated text.
Chatbots are a very common use case for early stopping. Give users a simple stop button so they can end the LLM output once they are satisfied. Why would they? Because no one wants to wait for a slow LLM to write more text than necessary. Users get their answers faster, and you save on LLM costs.
9. Use model distillation
Model distillation transfers knowledge from a large, expensive model to a smaller, more efficient one. Think of an experienced teacher (the large model) passing their knowledge to a student (the smaller model). The goal is a compact model that still performs well on your tasks, without the hefty price tag.
You start by training a large, powerful LLM on a broad range of data, or you use an existing general-purpose model like a large GPT or Claude model. This model is your "teacher." Then you create a smaller "student" model with a lighter architecture. The student learns by mimicking the teacher's behavior and trying to match its predictions. That way, the student absorbs the teacher's knowledge without going through the same extensive training.
You can tailor the student model to your needs and focus it on the tasks and domains that matter most to your application. You get the benefit of the large teacher model without paying for its ongoing usage.
Model distillation does take some technical know-how. You need to design the student model architecture, choose the right distillation techniques and tune the process. With some experimentation and iteration, you end up with a lean, cost-effective LLM that punches above its weight.
An early example of model distillation is Microsoft's Orca model. More recent examples are the distilled DeepSeek R1 models. The 32B version runs on two 24GB GPUs, as we show in our DeepSeek R1 setup with SGLang.
10. Use RAG instead of sending everything to the LLM
Retrieval-Augmented Generation (RAG) saves costs because you no longer send every piece of context to the language model with each query. RAG combines information retrieval with language generation, so you can use knowledge stored in external databases or documents without relying only on the LLM.
When a user sends a query, the RAG system first searches a pre-indexed database for the most relevant passages. These passages are then fed into the LLM along with the original query, and the LLM uses this context to generate a better-informed, more accurate response. Say you want to ask questions about a book. Instead of sending the entire book to the LLM, RAG retrieves the parts relevant to your current question and sends only those.
This reduces the number of tokens sent to the LLM, because the retrieved passages provide the context and answer parts of the query. You process fewer tokens overall, which lowers costs.
RAG can also improve the quality of the LLM's responses, because it adds relevant, up-to-date information that might not be in the model's training data. This is particularly useful in domains that need specialized knowledge or where information changes quickly.
RAG does require extra setup and maintenance. For many businesses and developers, the cost savings and better responses make it worth the effort.
11. Summarize your LLM conversation
Sending entire chat conversations to an LLM gets expensive, especially for long conversations or when you process many of them. Tools like LangChain help here with a Conversation Memory interface that summarizes the conversation, so you send only the summary to the LLM instead of the full history.
A summary has far fewer tokens than the full conversation, which lowers costs. It keeps the essential points and context, so the LLM still has enough information to respond meaningfully.
Here's how to use LangChain's Conversation Memory interface to summarize
conversations and save on LLM costs. (Current LangChain versions do this with
the SummarizationMiddleware, described in the short-term memory
docs.)
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Integrate LangChain into the application or system where the conversations take place.
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As the conversation progresses, use LangChain to process and store the conversation history.
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When you need to send the conversation to an LLM for further processing or response generation, use LangChain's Conversation Memory interface to create a summary of the conversation.
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Send the summary to the LLM instead of the entire conversation history. The LLM then generates a response based on the essential points and context in the summary.
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Continue the conversation, repeating steps 2-4 as needed.
This approach significantly reduces the tokens sent to the LLM, without hurting the quality of the conversation or the relevance of the LLM's responses.
Is self-hosting your own LLM cheaper?
Why isn't self-hosting a pretrained open-source LLM on our list of cost-saving strategies? Running your own model removes API calls and usage fees, after all. It's a tempting idea, but in reality self-hosting an LLM is rarely cost-effective.
First, consider the computational resources. Large language models often have billions of parameters. Running them efficiently takes powerful hardware, including GPUs or TPUs, which are expensive to buy and maintain. If you MUST self-host, consider using quantization to reduce the hardware requirements, as described in the sections above. Our guide to running GLM-4.7-Flash locally shows how far that goes: with Q4 quantization, a 30B coding model fits on a single 16GB GPU.
Second, self-hosting an LLM requires significant technical expertise. You need experienced engineers to set up, configure and optimize the model for your use case. This is time-consuming and costly, especially if you don't have the expertise in-house. Deciding between an API and a self-hosted model for a specific project is one of the questions we work through with clients in our AI consulting.
Finally, there's ongoing maintenance. New model versions and improvements come out regularly. Keeping a self-hosted model up to date and performant requires a dedicated team, which adds to the overall costs.
In most cases, a commercial LLM provider gives you the scalability, reliability and support you need at a predictable cost. Self-hosting might look good on paper, but it's rarely the most economical choice in practice.
Conclusion
LLM costs come down to tokens and model size, so every strategy in this list either sends fewer tokens or uses a cheaper model.
Prompt optimization, caching and batching get the most out of every token. Quantization, fine-tuning and distillation give you smaller custom models that are cheaper to run. RAG, LangChain's conversation summaries and LLMLingua cut the number of tokens you send in the first place.
Some of these strategies take technical skill and real work to implement. A practical order is to start with caching and shorter prompts, which are quick wins, then look at smaller or fine-tuned models for your highest-volume tasks. Every euro you save on LLM costs can go back into your business or your next AI project.
Further reading
- How to integrate knowledge with LLMs?
- How to test your RAG pipeline?
- How to host your own LLM - including HTTPS?
Interested in building high-quality AI agent systems?
We prepared a comprehensive guide based on cutting-edge research for how to build robust, reliable AI agent systems that actually work in production. This guide covers:
- Understanding the 14 systematic failure modes in multi-agent systems
- Evidence-based best practices for agent design
- Structured communication protocols and verification mechanisms
About the author
Andreas Nigg
Co-Founder, Pondhouse Data
Andreas has spent more than eight years building data applications. At Pondhouse Data he designs and ships AI systems and integrates them into complex, real-world environments.
