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How Much Will AI Cost in 2024?



How much does AI cost?


The JustSoftLab team estimates that you will spend at least $50,000 on an MVP version of an AI solution, with the cost of artificial intelligence increasing proportionally to its complexity and supported use cases. It’s important to note that the above price only refers to the AI component of your system; the efforts required to create custom web and mobile applications that support its logic will be billed separately.


However, this doesn’t prevent your company from implementing AI on a smaller scale and with a smaller budget.


There are many ways to introduce AI into your business: from purchasing plug-and-play chatbots for call centers to creating a custom self-service BI solution that draws data from multiple enterprise systems. Therefore, the cost of artificial intelligence will vary depending on the approach you choose and the type of solution.


In this article, we focus on custom and fully tailored AI solutions. As an AI consulting company, JustSoftLab will help you identify the factors that influence the cost of development, enhancement, and maintenance.


Additionally, our AI developers will give you rough estimates for several AI projects from our portfolio, as well as tips on approaching your AI pilot project and maximizing ROI.


What Are the 5 Key Factors Determining AI Costs?


1. The type of software you intend to create. 

Artificial intelligence is a broad term that refers to any device or application that makes decisions based on the information it consumes, thus mimicking human intelligence. Voice assistants that understand natural language questions, security cameras that recognize people in real-time video, and expert systems that detect cancerous tumors on CT scans—all can be described as artificial intelligence. However, their complexity, performance requirements, and therefore cost vary greatly.


2. The level of intelligence you aim for. 

When it comes to AI, people tend to imagine Boston Dynamics robots and holographic avatars from "Blade Runner 2049." In reality, most business AI solutions can be described as narrow artificial intelligence, meaning they are programmed to perform only a specific task—like recognizing text in PDF files and converting it into editable documents. To be classified as truly intelligent, AI algorithms must be capable of detecting patterns in data with minimal or no human intervention, assessing the likelihood or unlikelihood of an event, justifying their assumptions, continuously processing new data, and learning from it.


3. The quantity and quality of data you plan to feed your system.

Artificial intelligence is only as good as the data it was trained on, and the more data the algorithms consume, the better they become.


The availability of pre-trained AI application development tools, such as large language models (LLMs), significantly simplifies the training process. Some commercially available solutions, like ChatGPT or DALL·E 3, can be used directly without additional customization. However, the best results can be obtained by fine-tuning algorithms on data unique to your company. This data can be structured, meaning it’s properly organized and stored in relational database management systems (RDBM), or unstructured, such as emails, images, and videos, typically uploaded en masse to data lakes. In terms of AI costs, working with structured data is cheaper, especially if there is a significant volume of information to increase the accuracy of your algorithms. With unstructured data, AI experts need to put in additional effort to organize and label it, while software engineers need to set up the full infrastructure to ensure a continuous flow of data between the components of your system. In some cases, such as training AI-driven medical imaging solutions, data may be hard to obtain due to privacy or security concerns. To overcome this obstacle, AI engineers can artificially expand the size of a limited dataset, reuse existing classification algorithms, or create synthetic data to train the model using generative AI solutions. Such operations will inevitably increase the cost of building an AI program.


4. The accuracy of the algorithm you hope to achieve. 

The accuracy of your AI solution and its predictions directly depends on the type of application and the requirements you place on it. For example, a customer support chatbot is expected to handle only up to 60% of typical user requests; for complex issues, a human specialist will always be on standby. On the other hand, an unmanned delivery drone that transports blood and human organs must maneuver around objects with flawless precision, relying on advanced computer vision algorithms. Higher accuracy and reliability of AI predictions directly affect the lifespan of your project and increase AI development costs. Additionally, it should be noted that AI algorithms will continue to absorb new data as they work alongside human specialists, which may entail additional training and maintenance costs.


5. The complexity of the AI solution you’re working on. 

Artificial intelligence is the brain of a technology system that inputs and outputs data from your business application and provides information to users, including those without technical education. When discussing the cost of artificial intelligence, the cost of developing the actual software should be taken into account. This includes cloud backends, ETL/streaming tools, APIs for internal and external application integrations, and some type of user interface, such as a cloud dashboard, mobile app, or voice assistant.

Light AI, like the customer support chatbots mentioned in the previous section, can exist within a corporate messenger and doesn’t require complex infrastructure to function. AI-driven data ecosystems that provide a 360-degree view of your company’s operations are a different story.


Additional AI implementation challenges will arise when you begin scaling your intelligent system from one or several use cases (e.g., customer churn prediction or sales data analysis in a specific brick-and-mortar store) to company-wide deployment. In fact, this is why only 53% of enterprise AI projects move from prototypes to production.


Speaking of failures, it should be noted that only a small portion of AI projects (Gartner estimates this to be 20%; VentureBeat is even less optimistic) ultimately live up to their promises. The staggering failure rate can be attributed to several factors, including a lack of collaboration between data specialists and software engineers, limited or low-quality training data, and the absence of a company-wide data strategy.


However, most failed AI projects are described as "moonshots"—overly ambitious ventures led by visionary data specialists and CIOs aiming to "completely transform the way our company has operated for decades." Such projects can drag on indefinitely, and it’s only natural that at some point, a company’s senior management stops pouring money into a bottomless pit without seeing a glimmer of real value.


How Much Does AI Cost? These Examples from JustSoftLab's Portfolio Can Give You a Clue.


Project 1: AI-Driven Telemedicine Solution

A healthcare technology company approached JustSoftLab to upgrade a telemedicine system deployed in several hospitals across the US, with video recording capabilities.



The new version of the system will allow healthcare providers to use facial recognition and natural language processing technologies to analyze videos recorded during consultations and potentially improve doctor-patient interactions.




During the research phase, we ruled out potential technological barriers and chose optimal tools for the project—primarily Python and related frameworks and SDKs for speech recognition and analysis. For the pilot version of the telemedicine system, the client chose only speech-to-text functionality, without the expected delivery of user-oriented components. The solution conducts linguistic analysis of videos to identify possible communication style changes that could shed light on patients’ well-being and help doctors develop more effective treatment plans.

The basic version of the AI platform for video/speech analysis could cost $36,000–56,000.


Project 2: Intelligent Recommendation System

A tech entrepreneur wanted to add AI capabilities to a B2C platform connecting users with local service providers. The idea was to replace cumbersome search filters with advanced machine learning algorithms that would analyze user input and generate a list of service providers that matched the user's request.



We chose Amazon Personalize as the core technology stack for the AI part of the project. In addition to providing personalized recommendations based on user queries, the recommendation engine comes with fully managed cloud infrastructure for training, deploying, and hosting machine learning models. The system's backend will be written in Python, and user data will be securely stored in the cloud (Amazon S3).


Development, testing, and deployment of a similar AI platform (MVP) will cost somewhere between $20,000 and $35,000.


Project 3: AI-Powered Art Generator

A renowned artist approached JustSoftLab with a request to create a generative AI solution that would generate new paintings based on their work and the works of other artists that inspire them. The client wanted to have an MVP version of the system within weeks to showcase at an exhibition.



The JustSoftLab team proposed creating a neural network based on Python frameworks (PyTorch, TensorFlow) that would process abstract paintings, learn the artist's signature style, generate similar images, and display them on the artist's official website. For the MVP version, we suggested using a 1000 x 1000 resolution, similar to Instagram, and deploying the AI solution locally, leaving the option to move the system to the cloud in the future.

Depending on the type of training data (e.g., abstract or figurative art), image resolution (high-res or low-res outputs), and deployment approach, the cost of creating an MVP version of such an AI system could range from $19,000 to $34,000.


How to Reduce AI Costs and Start Benefiting from Artificial Intelligence As Soon As Possible


According to a recent Forbes Technology Council article, developing and implementing an AI-based solution could cost your company 15 times more than you initially estimated if you don’t already have a well-structured data ecosystem in place.


Major AI development costs are typically associated with significant infrastructure optimization, data integration, security, and AI management and governance efforts. However, you can minimize these costs by carefully planning your project and starting small while keeping the bigger picture in mind.


Here are a few steps to help you implement your pilot project:


  1. Gather Stakeholder Feedback: Before starting the development of an AI system, consult with internal and external stakeholders to identify key processes and decision flows that can be supplemented or automated with AI.


  2. Prioritize Use Cases: At this stage, you should use a product prioritization framework (e.g., MoSCoW, RICE, or Kano) to select the business cases that will bring the most value in the intermediate term and serve as a foundation for further AI implementations.


  3. Choose the Optimal Technology Stack: To create a vendor-independent solution and reduce overall AI development costs, use a combination of custom open-source components and ready-made solutions (e.g., plug-in face recognition modules, voice assistants based on APIs, and cloud services that support the creation and training of AI algorithms). Special attention should be paid to UI/UX design: your future AI system should include a user-friendly interface that allows stakeholders to ask questions to the AI, receive instant insights, or automate tasks without seeking help from your IT department.


  4. Prepare Data for AI Analysis: To help algorithms understand your business data, it is necessary to collect information, assess its quantity and quality, and bring it into a unified format. Several methods of data collection, preparation, and normalization can be applied to this end. More details can be found in our blog on preparing data for machine learning. Remember that identifying the right data and carefully curating it for model training is critical to reducing AI costs when developing a system that delivers consistent results.


  5. Create an MVP Version of Your AI System: Building a minimum viable product (MVP) that supports key use cases is one of the best AI development practices. With an MVP in hand, you can test the feasibility of your concept, identify areas for algorithm improvement, and start scaling the system for various use cases and departments. However, an MVP should not be confused with an AI proof of concept (PoC); the latter serves as a validation of your idea and is intended for internal use only. It is often recommended to start your AI journey with a PoC to test the feasibility of your idea and eliminate technological barriers early on.


  6. Treat AI Implementation as a Work in Progress: Once you launch AI, you may not get perfect results right away; as your AI system consumes new information under the supervision of human specialists, it will produce more accurate predictions and become more autonomous. Therefore, it is important to continue collecting feedback from your company's stakeholders, making necessary adjustments to the system, and repeating the above steps when implementing new features and use cases. This will not only help you optimize AI development costs but also address the issue of AI scalability.


How Much Does AI Ultimately Cost?


While it is difficult to estimate the cost of creating and implementing an AI application without delving into the details of your project, you can easily spend $50,000 on a very basic version of the custom system you want to build. However, you can still start the process with a smaller budget, especially if you are considering a PoC or thinking about using pre-trained machine learning models or plug-and-play services.


Is It Worth It?

By 2030, artificial intelligence could contribute up to $15.7 trillion to the global economy, with the lion's share of this sum coming from productivity growth and automation.


Currently, the AI revolution is still in its early stages. While some countries, industries, and companies may be better prepared for disruptions (i.e., they have the necessary data and IT infrastructure to create and deploy custom AI solutions on a large scale), the competitive advantage remains elusive, as every business has the opportunity to transform its operations and lead the AI race. And your company is no exception.





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