The Practical Guide to LLM Cost Optimization

Where do LLM Costs Arise?

Given the massive scale and complexity of modern LLMs, it’s no surprise that integrating them into real-world workflows comes with a hefty price tag. In this section, we’ll break down exactly where the money goes when businesses start using LLM systems.

Input/Output Tokens

LLMs are fundamentally built on stacks of Transformer decoders. Therefore, the input prompt from users is first converted into tokens. A single token can represent a word, a sub-word, or even a single character. The model’s output is also generated token-by-token, which then forms a coherent text.

When we use flagship proprietary models from providers like OpenAI (GPT), Anthropic (Claude), Google (Gemini), etc., we are charged based on the total number of tokens processed. The general rule: the longer the prompt and the longer the generated response, the higher the token count would be and thus, the higher the cost.

Another important point is that output tokens are almost always much more expensive than input tokens. For example, GPT-5.1 currently costs around $1.25 per million input tokens but $10 per million output tokens. This pricing, of course, varies depending on the model that we use.

Model Selections

There are a lot of LLMs to choose from in the market right now, from “small” models with a few billion parameters to frontier models with hundreds of billions or even trillions of parameters. Smaller models are significantly cheaper to run but naturally less capable on complex tasks.

Smaller models also have a big hidden advantage: much faster inference time and if needed, faster fine-tuning as well. As a result, the total cost of pre-training or fine-tuning a smaller model is often orders of magnitude lower than the ones with tens or even hundreds of billions of parameters.

Another key consideration is proprietary vs. open-source models:

• Proprietary models (OpenAI, Anthropic, Google, Cohere, etc.): extremely easy to adopt, we don’t need to manage infrastructure ourselves. The costs are almost entirely tied to token usage.
• Open-source models (Llama 3, Mistral, Gemma, etc., often hosted on Hugging Face): the costs are tied to the infrastructure we use, and we need to manage it ourselves. However, over time, this can be dramatically cheaper than relying on proprietary models.

Deployment Modes

There are two main ways companies serve LLMs in production: cloud-based deployment or on-premises (on-prem). Each approach comes with its own advantages and trade-offs.

Cloud providers are by far the most popular choice because they offer maximum flexibility and almost zero upfront infrastructure complexity. For example, on AWS we can:

• Use proprietary models through Amazon Bedrock (pay-per-token pricing), or
• Host open-source models using Amazon SageMaker, EC2, or ECS/EKS (pay for the underlying instances and GPUs).

In the cloud, the costs ultimately depend on whether we choose a managed proprietary model (billed mainly by tokens) or self-host an open-source model (billed by instance type, hours running, and data transfer).

On-premises deployment, on the other hand, shifts the cost structure entirely to hardware. Companies have to invest in their own GPU clusters, and the total expense is driven primarily by how much GPU memory (VRAM) and compute power they can afford, along with electricity, cooling, and maintenance.

Inference Modes

There are two common ways to run an LLM system: on-demand (real-time) and batch mode.

On-demand mode processes each request the moment it arrives. This mode is ideal for use cases like chatbots, customer support agents, or any interactive application where users expect an immediate response. Because the system must process requests as soon as possible, it’s significantly more expensive.

Batch mode, on the other hand, collects requests into larger groups (e.g., 100 or 1,000 requests at a time) and processes them all together in one go. We sacrifice latency (responses can take minutes or even hours), but the cost per request drops dramatically. Batch processing is ideal for non-urgent tasks like content generation, data enrichment, report creation, etc.

Additional LLM Tools

The rise of AI agents means companies are rarely using an LLM in isolation. To give them true “agentic” capabilities, such as reasoning, memory, tool use, and retrieval, companies also need to set up supporting infrastructure like vector databases (for RAG), external API endpoints, caching layers, and function-calling tools.

As request volume and data size grow, these additional components can quietly become a major part of the cost. As an example, the more searches we run against a vector DB or the more external API calls the AI agents make, the more expensive the cost will be.

Metrics for LLM Cost Optimizations

As we saw in the previous sections, LLM costs can balloon from multiple sources. That’s why continuously observing the system is important. In a nutshell, there are five different metrics that should be continuously observed: token usage, model quantitative performance, resource utilization, request frequency, and cost per business KPI.

Token Usage

The cost of LLM integration, particularly when using proprietary models, is heavily dependent on token usage, i.e., the count of input and output tokens.

To track the costs associated with the token usages, we can look at several things. As an example, we can monitor total input + output tokens per day/week/month, breakdown of token counts by model, feature, customer, or user, average input length vs. average output length, and input-to-output token ratio (lower is usually better for cost).

Continuously monitoring token usage can help teams quickly identify misuse, or unintended behavior of our LLM system that may drive costs up unexpectedly.

Model Quantitative Performance

Observing quantitative performance of the model is particularly important since model choice has a huge impact on the overall cost of LLM integration. Observing quantitative performance of a model can be done by preparing a dataset with ground-truth labels for a specific use case. Then, we compare the LLM’s generated output against this ground truth using whatever metrics make sense for the task, such as accuracy, Levenshtein distance, embedding similarity, ROUGE, BLEU scores, etc.

These metrics would provide a solid performance benchmark to fairly compare different models against each other.

Resource Utilization

Observing metrics related to resource utilization becomes critical, especially when opting for self-hosting open-source models, since companies directly pay for the underlying infrastructure.

As we all know, running LLMs at reasonable speed usually demands one or more high-end GPUs. While generating outputs, two key metrics should always be observed: GPU utilization (how busy the GPU actually is) and memory utilization (VRAM usage).

Monitoring these tells us exactly where there’s waste. For example, if GPU utilization is consistently low (say, below 50 %), it’s a clear signal that the hardware is being underutilized. Therefore, there is a perfect opportunity for optimization to maximize throughput, such as switching to batch or increasing concurrency.

Request Frequency

This metric tracks how many requests the users (or internal systems) actually send to the LLM service. By looking at request frequency, we can spot peak hours, daily/weekly spikes, and quiet periods. This opens the possibility for optimizations, such as scheduling batch jobs during off-peak times, scaling instances up/down automatically, or even turning the service off completely when no one’s using it.

It also gives a clear picture of system reliability. For example, if 95 out of 100 requests succeed, the attention can be focused on 5 cases that are failing, and we can dig into logs, timeouts, or token-limit errors to fix edge cases and make the whole system more robust.

Cost per Business KPI

This metric closely ties LLM spending directly to the outcomes that actually matter to the business (and these will vary depending on the specific use case). For example, we can check LLM cost per resolved customer-support ticket, per processed document or contract, per sale closed with AI-assisted customer service, per generated article, report, or piece of marketing copy, etc.

Tracking cost-per-KPI gives a clear view of how efficient the LLM system really is in production. More importantly, it provides hard evidence of whether the investment is paying off, or whether further optimization is needed.

Strategies for LLM Cost Optimization

In this section we’re going to give you several practical tips to optimize LLM cost, starting from model selection until the utilization of knowledge base.

Model Selection

As mentioned in the previous section, bigger models cost more (especially with proprietary models). Therefore, the best strategy is to gradually test different models on your use case, starting from the cheapest model until the more expensive one. Here are the common steps:

• Start with the cheapest capable model you can find (e.g., Llama 3.1 8B, Gemma 2 9B, Mistral Small, GPT-4o-mini, Claude Haiku, Gemini Flash, etc).
• Use the ground-truth dataset and quantitative metrics we talked about in the previous section to measure real performance of each model.
• Gradually test stronger/more expensive ones only if the cheap one fails your benchmarks.
• Calculate the performance-to-cost ratio for each model and pick the clear winner.

In practice, we’ll often discover that a cheaper model can deliver most of the quality we need on our specific use case.

Model Routing

When building an LLM-powered workflow, most businesses default to using a single model for everything. While it’s totally fine, using one model for all cases usually leaves a lot of room for cost optimization. By observing the metrics we’ve already discussed (request frequency and quantitative performance), the LLM workflow can be optimized in many ways, such as model routing.

As an example, let’s say that we notice that 80–90 % of user prompts are actually very simple and your current model achieves 99 % accuracy on them. In this case, model routing would actually be beneficial to reduce the cost. The procedure is simple:

• Route simple queries to a small, cheap model (e.g., Llama 3.1 8B, Phi-3, GPT-4o-mini, Claude Haiku).
• Only send complex or high-stakes queries to the big, expensive model.

To decide what queries can be considered as “complex,” we can train a lightweight classifier (e.g a fine-tuned BERT) that predicts whether the query needs the heavy model. However, we need to collect prompt examples and label each of them to train the classifier.

Prompt Optimization

Another powerful way to cut LLM costs is through prompt optimization. This technique is especially effective because the input prompt directly determines input and output token usages: the more concise the prompt, the lower the cost. Therefore, we need to craft prompts such that they are clear, concise, and unambiguous. Also, we need to remove redundant words, filler text, or unnecessary context that inflates token count.

Optimizing the prompt can also have a direct impact on output tokens, which further save the cost. The output tokens can be controlled with explicit instructions. For example, we can add phrases like “respond in no more than two sentences.”, “give a maximum of three recommendations.”, “provide a brief, concise summary only.”, “answer in bullet points, under 100 words.”, etc.

The goal of this method is simple: reduce both input and output tokens as much as possible without sacrificing generation quality.

Consider Batch Request Processing

Most LLM providers (including OpenAI and Anthropic) offer a batch processing option that is significantly cheaper than on-demand (real-time) mode. As an example, GPT 5.1 in batch mode costs $0.625 per million input tokens and $5 per million output tokens, whilst the on-demand mode costs twice the batch mode. However, there is a trade-off: user requests aren’t processed the moment they arrive and the results typically come back within minutes or hours.

Therefore, opting to use batch processing is a perfect solution for any workload that doesn’t need instant responses. Common use cases for batch processing are extracting information from large batches of PDFs or documents, generating or enriching datasets, and report or email generation. We can then notify the users via email, webhook, etc. once the results are ready.

Utilizing Knowledge Base

One of the biggest and most common causes of cost inflation in LLM systems is stuffing huge chunks of context into every prompt. This is usually to prevent hallucinations when the LLM response must be based on internal company knowledge.

A far better solution in this case is using the Retrieval-Augmented Generation (RAG) method. Instead of putting the entire knowledge base or thousands of lines of text in the prompt with every request, RAG retrieves and injects only the few most relevant passages that can help LLM to answer user queries. This keeps input prompts short, drastically cuts token usage, and still gives the model accurate, up-to-date information.

As mentioned previously, implementing an efficient RAG pipeline requires some upfront work, such as setting up a vector database (Pinecone, Weaviate, Qdrant, etc.), choosing or training an embedding model, and building the retrieval logic. However, the token savings usually pay for that effort in the long run, especially when request frequency is high.

Checklist

Now you know everything about LLM cost optimization: where the money actually goes, the key metrics to observe, and proven, practical strategies we can start applying today to cut LLM costs in production. 

The table below summarizes each strategy, its main benefits, and concrete ways to implement it.

Conclusion

Optimizing LLM costs becomes a strategic necessity as LLM becomes more deeply embedded in business workflows. By understanding exactly where expenses come from and which metrics matter, companies can make smarter decisions about model choice, infrastructure, and overall system design. Every part of the pipeline, from prompt length to GPU utilization, offers opportunities to tighten spending without sacrificing performance.