Why AI is So Expensive? Exploring the Nature of AI Pricing

Based on the years of dealing with AI projects of various functional specifications, scale and complexity, we can highlight five key criteria that dramatically affect the cost of artificial intelligence solutions. 

Project scope and complexity 

The first thing that you must define is how massive a desirable solution is.  

For example, you need a simple chatbot that will solve primitive communication or searching tasks (like chatbots used on some social media or in customer service). These solutions typically base on a fixed set of rules (so-called ‘rule-based’ chatbots with ‘if-then logic’) or strict conversational pathways (scripted chatbots). 

These chatbots are not AI tools in modern understanding, as they have no reasoning or learning capabilities, they don’t comprehend the context and follow rather primitive logic.  

At the same time, you can seek a more complicated AI tool based on the work of large language models.  

LLM-based tools are good at contextual understanding of requests along with semantic analysis of tests and queries. For example, Silk Data developed procurement software that allows to process customer requests, extracting the keywords and analyzing the intention of the message. In the end, the request is automatically transferred to a specialist needed with an AI-appointed status.  

The result of the software’s implementation was the fact that the procurement company could automate workflows regarding customer requests’ processing, improve retail and supply management practices and implement real-time analytics that allows to get the freshest data on user queries.  

If you look for the same solution (or solution of the same complexity and similar functionality), you should understand that its development may take months, and the advanced technologies used along with the greater number of specialists required mean that the development and implementation costs can easily exceed 50 000 USD.  

One more factor that defines the final costs is the way you get access to LLM capabilities. You can ask for custom LLM development, which is more expensive at the first stages, but provides you full control over app’s or software’s functions. At the same time, you can ask for implementation of the popular LLM API , for example, ChatGPT. It is typically quicker and cheaper, but the costs can intensively increase because of the pricing policies of the API’s provider. 

However, this type of solution is far from being the most expensive and sophisticated. Some businesses may look for truly advanced AI solutions for enterprise needs, requiring smooth automated work with large data amounts and enhanced predictive analytics capabilities.  

Such a solution can be implemented via AI agent development and deployment. AI agents are advanced artificial intelligence tools aimed at solving complicated tasks with minimal human intervention. These tools obtain advanced capabilities of real-time data analysis, reasoning, self-learning and self-correction, making them extremely useful for medium and large enterprises.  

At the same time, AI agents’ outstanding features imply greater costs of development. For example, Cleo AI, being a prominent example of an application based on an AI agent’s capabilities. It’s an AI financial assistant that helps individuals understand their spending habits, optimize budgets and improve their financial literacy through financial data processing, AI-driven insights and predictive analytics. According to some estimates, the project could cost the company at least 150 000 USD with the highest estimations of 500 000 USD.    

One more specific type of AI solution is the one based on computer vision technology.  

Projects oriented on computer vision put the ability to perceive, process and analyze graphical and video content at the first place. In doing so, computer vision can work with real-time analysis (for example, permanently tracking the movement of real objects) or pre-recorded content processing (for example, photos or video records).  

In addition, computer vision projects can also differ by complexity, scale and the variety and amount of hardware needed (special cameras, sensors, etc.). Through that, all the cost numbers are only estimates, though even small project rates may start from dozens of thousands of dollars. 

The summarized comparison of different projects is presented on the following table. 

Data availability and quality 

Next factor that inflicts AI software prices is the condition of data used while AI solution development. 

In many cases working with data of any type (market insights statistics, CMS monitoring data, user requests examples, etc.) takes a lot of time of the development team and the spent resources estimations are typically mentioned in project specification or vendor’s invoices (if you choose true professionals able to highlight the project stages and expected costs). 

The resources spent on working with data as well as the final project price directly depend on how structured your data is along with its availability. It's clear that unstructured data requires longer periods of processing, while limited availability (for example, when the business needs any extended data) means more time spent on research and data gathering.  

Altogether these factors lead the potential AI project to a higher cost. 

For instance, the software’s functionality in one of Silk Data’s projects fully depended on the quality of the initial data. 

No small part of the project was dedicated to gathering data regarding the health conditions of thousands of cows for the last 6 and 12 months. This data had become the basis for a testing dataset used in further AI model training.

Furthermore, during the features engineering process our specialists came across a certain amount of erroneous data that contained false information. Through that, additional data cleaning steps were added, and we provided a set of recommendations for company specialists on how to avoid it in the future. 

Development approach 

When you get to the option of outsourced AI development, you must know that different outsourcing approaches imply both different costs of solution creation and its delivery. 

We have thoroughly overviewed the key differences between outsourcing models , so we’ll just barely touch each of them in the following comparative table. 

Project specifics 

Do you want to get a cloud-based AI solution or that working on local processing and maintenance (on-premise)? Perhaps, you require the product of high scalability or limited requests handling will be enough? Do you need any task-oriented functionality, developed especially for the industries of healthcare, finances, public services or legal? Answering all these questions can be summarized in one term - ‘project specification’.  

For example, you need a solution that provides no upfront hardware costs, based on an AI model permanently managed by a provider that will demonstrate vast scalability capabilities. Then, you can apply to the solution that implies implementation of popular LLM’s API (for example, ChatGPT). This solution has a certain number of advantages and disadvantages for business, which were highlighted in our comprehensive overview of how ChatGPT changed the AI landscape .   

At the same time, any range of tasks specific for the industry may require additional time (for example, specifically fine-tuning an AI model), which inevitably leads to additional costs.  

For instance, you are a bank looking for an AI-based solution that will deal with customer requests for loans and will help automate the processing of such requests. Through that, you need a tool that can extract information even from printed or scanned documents, find the key information (also classifying the documents according to the content contained) and provide smart recommendations on further decision-making, like Silk Data’s AI-based document analysis software.   

Adding such functionality still requires additional work for AI specialists, and any task-specific project should be thoroughly discussed at the very beginning of the planned cooperation. As a result, the project costs can increase, and the medium point can range between 50 000 to 120 000 USD and more.  

AI team rates 

One of the most obvious criteria that defines the price of AI projects is the experience of AI specialists and present tendencies of their native outsourcing and hiring markets.  

The experience directly inflicts the expertise of participating in various AI projects, familiarity with technological stacks, tools and development approaches, while market tendencies highlight the rates the AI specialists tend to work for. 

The following table presents the AI developers hiring costs gathered and summarized by Index.dev agency in February 2025.  

Post-launch operations 

Finally, you should consider the necessity of regular monitoring and updating your solution which, if applied, also leads to additional costs. Post-launch operations are especially critical for AI-based mobile applications that always must align with the new versions of mobile operational systems. 

In other cases, monitoring and updating can be crucial, as all AI models, even the most perpetrated demonstrate hallucinations and performance downfalls, when exposed to new, real-world data. For example, a customer service chatbot trained on historical queries may struggle with emerging slang or industry trends, requiring periodic fine-tuning to maintain accuracy.