How much does AI cost? Here are the industries average values

I tested AI tools for the first time in 2019 to automate repeating parts of my workflow. It seemed the perfect solution to be: let the code run the manual material for which I was too bored.

However, I quickly discovered hidden costs for everything I did. My automation script needed a server to continue living. The server required maintenance and surveillance. And if the backend -APIs changes, my script would no longer work.

Today, AI has similar overhead costs, but on a much larger scale. For example Sam Altman Recently tweeted The saying “yes” and “Please” to chatted cost tens of million dollars of arithmetic resources.

That made me think: how much does AI do and when does it make sense to integrate AI?

In this article I will lead you through different types of AI models and how many AI solutions based on the model type. Regardless of whether you are a startup founder, an SMB or a company business, you will learn how you have a budget for AI in your company.

Table of contents

How much does AI cost?

The AI ​​costs vary greatly from the type of solution, business model, data quality, the model variant, the usage pattern and much more.

Let us break down the costs for AI based on four frequently used AI model types.

The costs of large voice models (LLMS)

LLMs are trained in massive amounts of data (think of tokens) to understand and generate a human language. I use LLMs extensively in my workflow – regardless of whether he prompted Chatgpt to design an e -mail, to analyze photos via Gemini or to receive content ideas from Claude.

Companies can use LLMs across several departments to do things like:

• Answer user complaints via chatbots.
• The screen continues, create SPS or help with the decision Business -Pricing models.
• AI supported code development or debugging.

LLMs are powerful – but that also makes them expensive. User inquiries are calculated via resource -hungry GPUs, the costs, the costs To train and wait millions of dollars.

Here is a rough examination of the costs for the integration of an LLM into your company.

Model as a service

LLMs like Chatgpt and Claude fall into this category. You “rent” your computing power via a natural language interface (chatbots) or API calls. Chatbots calculate a monthly flat fee, while the API price design is more complex.

LLMS witness your input prompt (what you send) and the output (the generated answer) in Token. Each token is a text unit – a full word, part of a word, a room or even signs such as “/”.

For API calls, they will be charged based on the entire token use. Here are the costs for OpenAPI from May 2025:

• Individual level: 20 to $ 200 per month For restricted access to your chat bot interface.
• GPT O3 (Pro 1 -M -Token): $ 10.00 Entrance; $ 40.00 Output.
• GPT 4.1 (Pro 1 -M -token): $ 2.00 Entrance; $ 8,00 output
• GPT 4.1 nano (Pro 1 -M -Token): $ 0.100 Entrance; $ 0.400 Output.

Are you not sure how many tokens you will use? You can carry out your input request Openai’s mobile phone tokenizer tool and received an estimate. Also remember that documents or earlier history of conversation that you include as a context for your token use!

Open source LLMS

Open source models such as Lama or Mistral are an inexpensive alternative to commercial LLMs such as Openaai. Access to open source model weights is free of charge, so you do not have to pay API costs.

The main costs for open source LELMs come from Calculate + hardware requirements. Companies can expect to pay around $ 200 $ 500/month for smaller models, but it can also reach upwards from $ 5k- $ 10k/month For large -scale corporate use.

Of course, open source models require a bit of technical expertise to implement, provide and update your systems. However, the fine-tuning of an open source model can significantly reduce its total costs.

Train your own LLM

If your company deals with very complex or sensitive data, you can choose to develop your own AI infrastructure. LLMS require computer resources (high-end GPUs), memory (databases) and specialized technical talents.

If you train your own LLM, you can easily cost in between $ 100k $ 1m For the initial development. And then there is maintenance, fine-tuning, immediate engineering, case back logic and model monitoring.

The costs for predictive analysis platforms

Would you like to know which products could become holiday bestsellers? Or if a new function receives enough market demand? Instead of relying on your intestine to use answers, you should use predictive analytics platforms.

These platforms identify patterns in massive data records such as customer behavior, historical market data, etc. to make data -controlled decisions. For example, you can estimate potential customer tracks by analyzing the frequency of use and support ticket history.

Predictive analytics platforms tend to be more affordable than other AI models because they do not need a strong computing power. The costs depend more on the data quality and the number of users.

SaaS-based platforms

The pricing is based on users, monthly prediction volume or on-demand use.

Solutions such as Tableau or Powerbi premium costs $ 15- $ 100/User/month. Enterprise SaaS solutions such as Alteryx begin for a single user at $ 4,950 per year. More comprehensive plans, including the AlteryX AI platform, can be rich from AB 10,000 to 50,000 $ or more per yearEspecially for larger teams.

Custom solutions

Basic predictive systems cost between the costs $ 20k $ 30,000while advanced $ 40k+. You can reduce the development costs by using open source libraries such as Scikit-Learn or Tensorflow. However, expect a bonus of 20 to 30% for maintaining the model and the associated infrastructure.

The costs for recommendation engines

Recommendation engines are an excellent way to adapt the user experience. You analyze user data and activities to propose products, services and content that may like your customers next. For example, at the end of this article you will find a list of “related articles” – this is a recommendation engine in action.

These recommendations are a win-win situation: Customers find what they want and companies can increase user loyalty on their platform.

But what exactly are the actual costs for companies, my preferences (and their!)? The answer depends on the type of recommendation engine you use.

• Platform integrated: usually free of charge. Many E -Commerce, marketing or CMS platforms contain basic recommendation functions free of charge or at minimal costs. Examples are Shopify product recommendation -api And Recommendations of Drift Kings Medias Intelligent content.
• Off-the-shelf: $ 2000 $ 12,000. These are typically SaaS-based solutions with a pay-as-youou-Go model. For example, Personalize amazon Calculate its pricing using data that is sent to the model, training and real-time or batch recommendations.
• Use: USD 10,000 – 200,000 US dollars. A custom recommendation engine may be the right fit if your business model depends on the curating of good content or products. These can be expensive, but you can use open source libraries such as LightfM and Faiss to create fast prototypes. Examples are Netflix, Amazon and Spotify.

The cost of process automation solutions

As I already mentioned, my AI trip started with tools for process automation. I have created a dashboard for the administration of access to internal company.

Instead of checking and approving each user request manually, my script would check the authorization, issue authorizations and automatically notify users. It would also mark unusual access inquiries or propose probable permissions based on data of similar teammates.

During working on this project, I found that the process automation can perform any repetitive task. These tools can open new browser tabs, click on buttons, send user -defined e -mails, protocol activities and more. If you add the MIX AI, these systems can even manage decision -making and analysis based on previous data.

Intelligent process automation solutions like this have two components:

• Automation tool. You can select a SaaS no code solution like Make.com or a robust company solution such as UIPATH. Make.com has a subscription -based pricing (9- $ ​​29 per month for 10,000 ops) while Uipath with a pricing of per time ($ 1000 – $ 10,000 per bot per bot).
• KI models for special tasks: Automation tools can access AI components to edit special tasks such as document analysis, intent -classification, etc.

How is the pricing for AI determined?

AI costs not only for the model you choose. It is about how often it is carried out, how much data it needs and how well it scales it.

Let’s look for specific factors that affect the costs for AI models.

1. Data costs

AI runs on data. The quality of your data determines how exactly your model will be. If you are not careful with the data you specify, AI can be able to Spitting nonsense into customer communication Or include its own prejudices.

I saw how internal company data have become chaotic. Valuable data is saved via several CRMs, cloud solutions and internal tools. The result? Inconsistent, unnecessary and often unreliable data.

While raw data can be purchased, clean, labeled data can be expensive. The data processing includes several steps: collecting, cleaning, labeling and structuring in AI-friendly formats. Each step is usually charged on the basis of the data volume or the hours spent. For example, CVAT, a data cleaning platform, estimates the costs of Annotate 100k pictures at $ 300k.

If your internal data is not sufficient, you can add external data records from providers such as Bloomberg or data marketplaces such as Kaggle.

As soon as your data is finished, the next step is to save it. Depending on the data volume, Cloud data storage stairs can cost between the costs somewhere $ 1k- $ 10k A month. Your cloud storage should be able to collect and process new data.

Data management is another factor that must be taken into account. I recommend budgeting around 10-20% of your costs to meet data security and compliance with laws such as the GDPR.

2. Infrastructure costs

The infrastructure costs come into play when you choose custom AI solutions or use open source models. Saa’s platforms contain these expenses in their monthly prices, but the establishment of their own infrastructure requires a significant budget.

For example, high-performance nvidia GPUs like H100 can cost between the price USD 15,000 and $ 40,000 per unit. For most production environments, several GPUs are required to optimize the performance. A modest AI cluster could easily cost hundreds of thousands of dollars. You also have to consider energy and electricity costs for the management of this cluster, which can increase the total costs by 30-40%.

Cloud solutions such as Google Cloud Ai or AWS are inexpensive and a pay-as-you-go price model. The costs are usually range from $ 2 to $ 80/hourDepending on the specifications of the GPU instance. A single H100 80 GB GPU within the A3 HohgPU 1G instance costs approximately $ 11.06 per hourWhile an instance with 8 H100 80 GB GPU $ 88.49 per hour.

3 .. Training and development costs

Most companies underestimate the development costs for the successful implementation of a AI model. You must create custom integrations so that the model works with your existing systems, the model trains and the answers can be finely divided for your application.

“The actual costs are not the token (API calls for an LLM). It is everything you wrap around the model to make it usable – repetitions, intermediate storage, orchestrating, fallback, evals. Everyone who cited the factions of a cent ‘per token”, leaves half of the invoice ” Joe CaineyThe CEO of Sunbeam.

The acquisition of the right developer talent has also become competitive. Salaries for AI developers can be from von $ 200k $ 1 million+. Project -based freelancers burden somewhere between $ 50 and $ 100 per hourDepending on your experience and geographical location.

4. Maintenance costs

AI tools must be updated every 3-6 months in order to take into account newer models, data contexts and the changed business requirements. Maintenance activities can, but not limited to:

• Performance monitoring.
• Retraining based on user interactions.
• Adjust settings or data for a better output.
• Security and compliance updates.

If your business environment is not highly controlled, you should say that a maintenance effort of 15 to 20% expect your AI systems.

How much should you spend on AI in your company?

There is no answer to this question. Determining the right AI budget for your company is not about following industry, but about adapting it to your needs.

Let us break down the key factors that you should take into account before we choose a AI solution.

Business size and budget

Budget predictions can vary depending on your company’s scale.

An IBM study Shows that larger companies are planning to assign ~ 3% of their sales for AI. Around 33.2 million US dollars for a company of 1 billion US dollars annually. In contrast, small and medium-sized business owners budget asked around 5-20% of their total sales for AI.

Small and medium -sized companies (SMB)

If you are an SMB, you should start with AI-integrated SaaS platforms that can be aimed at several departments. For example, Drift Kings Medias breeze Bundle of AI automation for marketing analyzes, customer support and sale in a tool. This integrated approach to AI offers a better ROI than maintaining your own infrastructure, especially for teams with limited technical resources.

source

Of course, this decision also depends on the type of your company. If your company deals with sensitive data or has AI-dependent processes, you will probably need a custom model. Depending on your budget and business priorities, you can select an open source solution or build up a proprietary.

Roman GeorgioThe CEO of Coral protocol And ex-camel AI founding member shared his thoughts: “I would pay a little more to use Claude if I built a SaaS tool like Cursor because my product depends on the best possible LLM output.

“But if I only carry out a text overview for a AI-affiliated CRM, I would optimize the costs and use (an open source solution like) Mistral or Qwen.”

Pursue

Company budgets can be enough in billions. Oracle spent $ 3b in the first quarter for the Cloud infrastructure that supports AI training. In this scale, companies usually pursue a hybrid strategy-defined infrastructure with APIs of third-party providers or internal APIs for sensitive data and SaaS platforms such as Breeze for certain departments.

Many companies use their collective bargaining to negotiate agreements with AI providers. As a rule, these agreements have a minimum period in which you receive land -based discounts and early access to new platform functions. For example, a high -ranking AI leader informed me that he would spend $ 100k/month On Github Copilot licenses for ~ 7000+ team members.

Integration complexity

Here is something you probably didn’t expect from saying that if you prepare your systems for the AI, it can cost so much (or sometimes more than) the AI ​​solution itself.

By implementing AI you have to tackle inefficiencies in your systems.

Bad data? You first have to standardize it to reduce the costs and risk of hallucinations. Separation of systems? You must create custom integrations in your AI tool.

However, the standardization of your systems is not just a AI effort. It improves your overall company with efficient reports, simpler training cycles and smoother integrations in the future.

Budget for integration costs, but also the entire business value.

Risk tolerance

Another thing that you should take into account is your company’s risk tolerance. Souvik RoySenior Ai Development Manager Standard charterunderlines this as important concerns because they deal with financial data.

“Before automating processes, the first thing we consider is whether potential damage is reversible. We do not want to encounter compliance problems or potential fines because we have tried to automate something,” he told me.

For example, if a model “You have to …” instead of “You have to …”, the difference is usually negligible. However, this can lead to critical misunderstandings in industries such as law or finance.

Companies with low risk tolerance should assign additional budget for security lines, tests and human supervision.

When (and if not) invest in AI solutions to invest

When researching AI costs, I saw a clear pattern: AI is slowly changing from small departmental experiments to organizational widths.

Companies do not ask If You should adopt AI, but rather How To integrate it.

Regardless of whether it is a managed solution such as HubSpot Breeze or a custom implementation with API calls, there is AI solutions for each business level. Lifting spots Meice leadership Revealed that 75% of companies that implement AI have achieved a positive ROI.

When I spoke to Cainey, I liked his three-stage decision-making tree for the integration of AI solutions:

1. Scales it linearly with the main occupation?

2. Is it predictable enough to avoid a model?

3. Is it certain to be 5% of the cases wrong?

If so, for all three, it is on the roadmap. If not, it is either guided by humans or completely skipped.

My advice? Resist the temptation to automate or record AI in every business process. Start small, measure the use and then scale your AI investment while validating the ROI.