When considering the introduction of artificial intelligence into your company, it’s important to understand the costs involved in implementing and maintaining your own LLM. Expenses go beyond just paying for model usage (e.g., token-based API fees) and include a range of factors — from infrastructure to security. Below, we discuss the types of costs associated with using dedicated LLMs and present example calculations for popular models (such as GPT-4, Claude, Mistral, LLaMA, etc.), including business use case scenarios.
More and more companies are considering the use of large language models (LLMs) in their own products and processes. These “dedicated” models can act as intelligent assistants—answering customer questions, analyzing documents, generating reports, and much more. You can read more about it here.
Types of Costs When Using LLMs
Before starting the implementation, it’s important to understand all the components that contribute to the total cost of using a dedicated model.
Infrastructure:
If you’re using models via a cloud API (OpenAI, Anthropic, Google), you only pay for the tokens used. The infrastructure cost is “hidden” on the provider’s side.
If you choose to self-host a model such as Mistral or LLaMA, you’ll need to maintain a GPU server—either locally or in the cloud. For example, renting an instance with an A100 GPU typically costs $1–2 per hour, which amounts to $750–1,500 per month if the server runs continuously. While such an investment can handle a high volume of queries, it may be underutilized at a smaller scale.
Licensing and Model Fees
Commercial models come with licensing or subscription fees. For example, when using the GPT-4 API from OpenAI or Claude from Anthropic, you pay per token used according to the provider’s pricing (we outline token costs in detail later on). On the other hand, open-source models like LLaMA or Mistral are available for free—there are no licensing or token fees. Meta, for instance, released LLaMA 2 under a license that allows businesses to use it freely. However, “free” doesn’t mean zero cost—you’ll still pay for the infrastructure and electricity needed to run the model (as mentioned earlier). It’s also important to check license restrictions: some open models may have specific usage conditions (e.g., restrictions on certain industries).
Model Adaptation and Customization
For an LLM to perform well in a specific company setting, it often requires customization—such as additional training (fine-tuning) on company-specific data or at least the preparation of tailored prompts (known as prompt engineering). This adaptation process can generate significant costs:
Model Fine-Tuning: Training a model on your own dataset requires computing power (typically GPUs running for many hours) and expert knowledge. For larger models, this can cost anywhere from several thousand to tens of thousands of dollars—factoring in both infrastructure expenses and specialist time. Even fine-tuning a smaller model (e.g., GPT-3.5) via OpenAI’s API can incur significant costs, as it involves processing hundreds of thousands or even millions of tokens during training—billed according to the provider’s token pricing.
Prompt Engineering: As an alternative or complement to training, you can craft tailored prompts and instructions for the model. While writing prompts itself doesn’t require paid resources, iteratively testing and refining multiple versions consumes tokens (which adds cost when using a cloud-based model) and takes up team time. This can be viewed as either an operational cost or a competence-related expense—specialist time is needed to optimize the model’s behavior for your specific use case.
Operational Costs
After deploying the model, ongoing operational costs come into play. These include monitoring the model’s performance, maintaining efficiency, logging results, applying updates, and fixing potential issues. If you’re using an API, the main operational cost will be the monthly bill for consumed tokens, along with any premium subscription fees (some providers offer subscription plans with usage limits or preferred pricing). If the model is hosted locally, operational costs typically include:
Electricity consumption – GPU-based models can consume significant amounts of power, leading to substantial monthly energy costs.
System administration – Time spent by administrators on server maintenance, backups, and updating software components (e.g., AI libraries).
Infrastructure scaling – As demand grows, additional machines or cloud instances may be needed, resulting in further expenses.
High availability – If the LLM assistant needs to operate 24/7 without downtime, you may need to invest in redundant resources (e.g., backup servers) or enter into an SLA agreement with your cloud provider.
Team Expertise
Implementing an LLM requires the right expertise within the IT/Data team. If your company lacks AI experience, it may be necessary to train existing employees or hire new specialists—such as an ML engineer or MLOps expert—which adds recruitment or training costs. Alternatively, some companies choose to work with external consultants or service providers to deploy the model. This also incurs costs, usually one-time project fees, which can be significant. It’s also important to account for the time your team spends integrating the model with existing systems (e.g., connecting it to a database or user-facing application). This is a labor cost that’s often overlooked in smaller projects but can have a major impact in practice.
The categories above show that the total cost of owning a dedicated LLM-based solution goes far beyond just the fee for accessing the model. It’s important to consider all these factors before making a decision. In the next section, we’ll look at specific numbers: how much a single prompt costs for various popular models, and what it would take to maintain a simple LLM assistant in two example business scenarios.
Cost of a Single Prompt in Popular LLM Models
Language models are typically billed based on the number of tokens processed. A token is a small piece of text—it may represent a single word or part of a word (for example, 1,000 tokens roughly equals 750 words of continuous text). API providers list prices per 1,000 or 1 million tokens.
Below is a comparison of the approximate cost to process 1,000 tokens using selected popular LLM models:
| LLM Model | Access / License | Cost per 1000 tokens | Notes |
|---|---|---|---|
| GPT-3.5 Turbo (OpenAI) | Cloud API (chat model available, e.g., in ChatGPT) | $0.0015 (input) $0.0020 (output) |
|
| GPT-4 (8k) | Cloud API (OpenAI) | $0.08 (input) $0.16 (output) | High quality; high cost |
| GPT-4 Turbo (128k) | Cloud API (OpenAI) | $0.01 (input) $0.03 (output) |
|
| Claude Instant v1.2 | Cloud API (Anthropic) | $0.0008 (input) $0.0024 (output) |
|
| Claude 2 (100k) | Cloud API (Anthropic) | $0.008 (input) $0.024 (output) |
|
| Mistral 7B | Open source (free model) | Token cost: $0 |
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| LLaMA 2 13B | Open source (free model) | Token cost: $0 |
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| LLaMA 2 70B | Open source (free model) | Token cost: $0 |
|
Legend: How Token Costs Are Calculated
Input tokens – words contained in the user’s prompt.
Output tokens – words generated by the model in response (completion).
For most commercial providers, the cost is charged separately for input and output tokens. For example:
GPT-4 Turbo:
1,000 input tokens: $0.03
1,000 output tokens: $0.06
If a dialogue contains a total of 1,000 tokens (e.g., 500 input + 500 output), the cost is approximately $0.045.
For simplicity, you can assume that a full interaction of 1,000 tokens costs about $0.09.
By comparison:
GPT-3.5 Turbo – a similar 1,000-token dialogue costs only about $0.0035 (i.e., 0.35 cents).
Open-source models (e.g., Mistral, LLaMA) – token costs are $0, since the models run locally. You only pay for infrastructure-related costs (power consumption, server uptime, etc.).
Open-source models (such as Mistral, LLaMA, etc.) are attractive because they come with no fees for the model itself—you can generate any number of tokens without paying the model provider a cent. However, to run these models, you need to maintain your own infrastructure. At a small scale, the cost of renting a machine for a single query may actually exceed the cost of an individual API call to a model like GPT. On the other hand, at a large scale—with many queries per day—open-source solutions can become significantly more cost-effective. In summary, cost-effectiveness depends on the use case, which we’ll explore in the next section.
Example Costs of Implementing an LLM Assistant (100 Queries per Day)
Let’s now consider a practical scenario: your company wants to implement a simple LLM-based virtual assistant that performs one of the following tasks:
Document analysis – e.g., the assistant reads offers or contracts and extracts key information such as clauses, deadlines, and amounts.
Customer inquiry handling – e.g., the assistant replies to customer emails with questions about pricing, product availability, technical support, etc.
Let’s assume that:
The assistant will handle approximately 100 interactions per day.
Each interaction consists of a prompt and a response, totaling around 2,000 tokens (e.g., 1,000 tokens in the prompt—roughly 750 words or several paragraphs—and 1,000 tokens in the response, or about 750 generated words). This token size covers fairly complex queries and detailed replies.
On a monthly basis, the assistant will process around 6 million tokens (3,000 interactions × 2,000 tokens = 6,000,000 tokens).
We want to compare the monthly operating costs of such an assistant depending on the choice of model and deployment approach. We’ll present two variants:
API Variant (Closed Model): We use a commercial model via an API (e.g., OpenAI GPT or Anthropic Claude). We don’t maintain our own servers—costs are limited to token usage, billed according to the provider’s pricing.
Self-Hosted Variant (Open-Source Model): We use an open-source model (e.g., Mistral or LLaMA) deployed on our own servers. Costs include infrastructure needed to support approximately 100 queries per day—such as cloud GPU instance rental or hardware amortization, plus electricity.
Below is a table comparing estimated monthly costs for several example models under both deployment variants, assuming 6 million tokens per month:
| Model (variant) | Estimated Monthly Cost | Comment |
|---|---|---|
| GPT-3.5 Turbo (API) | approx. $18 (USD) |
|
| GPT-4 (8k) (API) | approx. $270 |
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| GPT-4 Turbo (128k) (API) | approx. $18 |
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| Claude Instant (API) | approx. $20–25 |
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| Claude 2 (API) | approx. $150–200 |
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| Mistral 7B (open source, self-hosted, 1x GPU) | approx. $300 |
|
| LLaMA 2 70B (open source, self-hosted, multi-GPU) | approx. $1,000+ |
|
| Local model (e.g., LLaMA 13B, GPTQ, Mistral 7B – CPU) | approx. $300–500 |
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From the above comparison, several key takeaways can be drawn:
Small-scale usage (100 queries/day) favors API solutions
With relatively low query volume, using a commercial API (OpenAI, Anthropic) is highly cost-effective—especially with lower-priced models like GPT-3.5 or Claude Instant, where monthly costs can be as low as a few dozen dollars. For higher-end models, monthly costs may rise to several hundred dollars. Still, at this scale, running your own GPU server at $300+ per month would be less economical than relying on cloud-based APIs.
Large-scale usage (thousands of queries) changes the equation
If your assistant becomes successful and the number of queries increases by 10x or even 100x, the monthly API bill could grow to thousands or even tens of thousands of dollars. In such cases, investing in an open-source, self-hosted model starts to make financial sense. With a high enough query volume, the per-request cost of running the model locally becomes lower than the API cost—since the purchased or rented hardware is being used more efficiently. In extreme cases of massive scale, some organizations may even consider training their own model from scratch—but this is typically reserved for the largest players with very substantial budgets.
Use Case Matters (Quality vs. Cost Efficiency)
Choosing the right model shouldn’t be based solely on cost—it also depends on the quality of output required for your use case. In a document analysis scenario, precision in extracting information is the top priority. A lower-cost or open-source model may be sufficient here, especially if fine-tuned to the task. A model with 7B–13B parameters can offer adequate performance at a much lower cost. Moreover, when processing sensitive documents (e.g., contracts), running the model locally ensures that the content never leaves your organization—an invaluable benefit from a legal and data privacy standpoint. On the other hand, in customer inquiry handling, where natural language quality, politeness, and contextual understanding are critical, GPT-4 can significantly outperform smaller models. In this case, a company may find it worthwhile to pay more for superior customer experience.
Hidden Costs Around the Project
It’s important to note that the above calculations cover only the technical costs—such as token usage or infrastructure. In practice, there are also “soft” costs to consider, including staff time for preparing the implementation, integrating the model with systems like a CRM or knowledge base, testing, and ongoing iterations and improvements. For example, if the assistant needs to retrieve data from a company’s internal document repository, those documents often need to be organized or cleaned before they can be effectively used by the model.
Cost Example: AI Assistant for Analyzing Emails and PDF Documents
Here we also present the cost breakdown of our assistant based on Google’s Gemini model, which we described [here]. Its task is to automatically analyze incoming emails to identify insurance policies and extract key data from attached PDF documents—such as policy number, insured party address, or payment confirmation.
Average Token Count per Email:
Input: 3,500 tokens
Output: 220 tokens
Analyzing 100 emails with attachments using the Gemini 2.0 Flash model costs approximately $1.50.
Summary
Can We Afford Our Own “ChatGPT” in the Company? As we’ve seen, the answer is: it depends—primarily on the scale of usage and quality requirements. The key lies in selecting a model and deployment method that aligns with your specific needs. An iterative approach is often the most practical: start with a lower-cost model or API, evaluate the results, and scale up to a more powerful model or self-hosted solution as the project matures. Regardless of the path you choose, careful planning and cost monitoring across all categories is essential. We hope this comparison helps you make informed decisions and prepare a realistic budget for implementing a dedicated LLM in your organization.
If you’re considering implementing an assistant in your company, it’s worth finding answers to the following questions:
Do I need high-quality responses (e.g., GPT-4), or is an approximate answer sufficient (e.g., Claude Haiku, Gemini Flash)?
Am I processing sensitive data (e.g., customer documents)?
Do I have an IT team capable of hosting a model in-house?
What is the expected number of queries per day/month?
Is it more cost-effective to maintain my own infrastructure, or should I pay for API access?
For small to medium-scale applications, the cost of using a dedicated LLM can be quite reasonable. Thanks to cloud-based services, it’s possible to get started for just a few dozen dollars per month with models like GPT-3.5 or Claude Instant—an excellent option for experimentation and early prototypes. If you need top-tier performance, such as what GPT-4 offers, you’ll need to account for higher costs. However, even a few hundred dollars per month can be justified if the business value is significant—for example, by automating tasks that would otherwise require many hours of manual work.
On the other hand, for large companies planning intensive AI use, costs can grow exponentially—making it worth considering open-source options and greater investment in in-house infrastructure. Open models like LLaMA or Mistral offer freedom from per-token fees, but shift the cost burden to hardware and staffing. They become cost-effective when operating at scale or when full control over data is a top priority.