Recent studies show that businesses will continue to prioritize AI and GenAI in the coming years. The reason: AI solutions can speed up the hiring process, improve freight management, increase data security, and so on. All in all, AI systems take business operations to the next level and provide a compelling competitive advantage. No wonder the global AI development market is booming.
It is expected to reach $1,811,747.3 million in revenue by 2030. Investment in AI is also on the rise. At the end of 2024, the global volume of AI deals reached 1,245 transactions. However, the AI development landscape is not without its challenges.
The biggest AI development obstacle is its cost. AI tools are typically very expensive to develop. Generative AI is already driving up the cost of computing. The average cost of computing is expected to increase by 89% between 2023 and 2025. As a result, every CEO surveyed has been forced to cancel or delay at least one AI initiative.
Indeed, the cost of developing AI can be challenging for any company, especially small and mid-sized ones. Let's find out how much AI development can cost, what factors influence these numbers, and most excitingly, how you can reduce AI development costs.
How Much Does AI Development Cost on Average?
In 2024, the cost of AI development projects undertaken by different teams varied widely, from a few thousand dollars per project to tens and even hundreds of thousands of dollars.
However, prices typically range from $10-20k per project. If you need one-time AI integration services, be ready to pay from $25 to $49 per hour, i.e. there is a price difference as well.
It's also worth noting that prices are likely to change or increase in the near future. There are obviously a number of factors at play here.
Main Factors Influencing the Cost of AI development in 2025
The cost of AI software development is influenced by many factors. The key ones are listed below:
- Types of AI solutions
- Project complexity
- AI development tools and infrastructure
- Type and amount of data needed
- Algorithm training needs
- Regulatory and ethical compliance
- Deployment and maintenance
Types of AI solutions
Let's find out which AI solution might be right for you. Below, we'll outline the most common AI solutions that businesses are choosing.
1. Conversational AI (chatbots and virtual assistants)
These AI tools automate interactions using natural language processing (NLP) to improve customer service and user engagement.
COST:
A simple chatbot or assistant operating on predefined scenarios can cost around $5,000. A more advanced AI solution can cost up to $20,000.
2. Computer vision (image recognition solutions)
This AI software enables machines to interpret and analyze visual data for applications such as face recognition, medical imaging, and object detection.
COST:
Basic tools cost about $15,000, while real-time image analysis is more expensive. The complexity of the system and its functions has a direct impact on the development costs, so advanced AI software solutions can cost $200,000 and even $700,000.
3. Recommendation engines
By analyzing user preferences and behavioral patterns, these AI solutions offer customers the products and services they are most likely to buy.
COST:
The price of AI can vary from $10,000 to $200,000. Before approaching the issue of AI development costs, it is better to hire developers with relevant expertise and estimate the approximate scope of work, as the cost of AI development depends on this factor.
4. Natural Language Generation (NLG)
These AI solutions turn data into human-readable text for a variety of use cases, including report automation, content creation, and data storytelling. The key is what data and insights you feed it.
COST:
The cost of developing such an AI solution can start at $20,000. But it is always possible to create more complex, advanced, and therefore more effective solutions, which, however, will cost more to develop.
5. Fraud detection and prevention systems
These AI systems use machine learning to analyze customer behavior and transaction patterns to detect suspicious activity in real time, protecting organizations in sectors such as finance, eCommerce, and insurance.
COST:
In this case, we can classify basic, mid-level, and advanced software solutions. The price is appropriate: from $30,000 for the basic version, from $100,000 for a mid-level solution, and more than $300,000 for an advanced solution.
6. Predictive analytics
An AI tool with predictive analytics capabilities uses historical and real-time data to predict future trends, helping to make strategic decisions, and improve business efficiency.
COST:
The cost of developing AI with such capabilities can vary. Creating an AI system with basic settings can cost between $20,000 and $30,000, while the cost of creating a more advanced version starts at $40,000.
7. Speech recognition systems
This is an AI solution that converts spoken language to text, simplifying voice interaction.
COST:
The cost of a simple speech-to-text application can start at $10,000. As AI systems become more sophisticated, their cost increases.
Project complexity
AI-based systems vary in complexity and therefore in the amount of resources (knowledge, tools, technologies, etc.) and specialists involved. A small, specialized AI project with limited functionality is easier and cheaper to develop. However, projects with broader capabilities, such as systems requiring real-time decision-making or integration with multiple platforms, increase the workload and may involve an entire team of specialists. Even more challenging are high-performance requirements, such as achieving near-perfect accuracy or processing massive amounts of data in real-time, since they require complex models and rigorous testing.
As a result, the cost of developing such AI will be higher.
AI development tools and infrastructure
Tools and infrastructure affect the cost of AI development. The following should be considered:
- Hardware typically involves an upfront cost.
- Most cloud platforms operate on a pay-as-you-go model.
- Open source software tools (TensorFlow, PyTorch, Kubeflow, etc.) are free, but may require hosting costs.
- Data storage tools charge based on the amount of data stored.
Now let's look at the key differences between the hardware, software, and other tools typically required to work with artificial intelligence.
Type and amount of data needed
AI needs data to work, and the quantity and quality of data affect the final cost of development.
Here's what impacts costs:
- Data collection: Collecting specific data from scratch (such as user behavior or market data), buying data sets, or finding your own information can be expensive.
- Data preparation: This includes cleaning, labeling, and preprocessing the data, which is time-consuming and therefore expensive.
- Data management: Larger data sets require more storage and processing capacity, which increases costs.
Algorithm training needs
In this case, the cost of AI development is affected by the complexity of training and iterative development. Simple machine learning models are faster and cheaper to train, while deep learning models require more time and computing resources since they require large data sets and numerous iterations to fine-tune. Meanwhile, fine-tuning an AI model to improve accuracy requires numerous rounds of testing and adjustments.
Regulatory and ethical compliance
Compliance with regulatory requirements and ethical standards can increase development costs:
- Data privacy laws: Mandatory compliance with standards such as GDPR or HIPAA may require additional security and data handling protocols.
- Bias mitigation: Ensuring that algorithms are fair is one of the modern challenges and tasks that must be addressed. All of this requires additional testing and customization, and that costs money.
Deployment and maintenance
Once an AI system is built, it goes through the next stages:
- Integration: Adding AI software to existing workflows or software systems.
- Monitoring and updating: Performing regular updates and performance tracking to adjust the AI.
- Scaling: Scaling the AI solution for more users or data.
All of this comes at an additional cost.
IT Outsourcing vs. In-house AI Software Development
You can outsource AI software development or build it in-house. Which is the cheaper way to build AI? That depends.
The difference lies in the average hourly rate of the developers:
- The average hourly rate for AI developers in the US and the UK is $62;
- The average hourly rate for developers in Poland is $25.
Assuming the project would take about 200 hours, the estimate might look like this:
Now let's compare annual salaries for specific positions in the US and Poland:
*Salary data from sources including Talent.com, Erieri.com, and others.
Note: The above estimates are approximate and intended to give a general idea of the cost difference between outsourced and in-house AI development.
Cost of Ready-made AI vs. Custom AI Development
You have two choices:
1. Go for ready-made AI solutions that require little customization. This can save you money when developing an AI solution for your business.
2. Create an AI product from scratch, customizing it to your business needs. It is more expensive but more flexible and profitable in the long run.
Both options have their pros and cons.
Ready-made AI solutions
These tend to be affordable and quick to deploy. Various chatbots are the most common AI solutions implemented today.
Fees for ready-made AI software for business include several pricing models, including the following:
- Subscription. You pay a monthly or annual fee to use this pre-built solution.
- One-time license. This is a one-time payment for access that is updated as agreed.
- Pay-as-you-go. You pay only for the resources you use (it can be data processing, transactions, etc.).
- Freemium. There are basic functions that are free, but to extend them you have to pay some money.
Additional costs include:
- Initial system setup: From $5000 (depending on the conditions of use of the AI solution).
- System maintenance: While the system is in use, you may need to expand its capabilities or upgrade it. Expect to spend at least $1000 per year.
- Extra needs: You may need to license certain technologies or hire additional expertise to deploy an AI system. Costs can range from $1,000 per year to $10,000 or more.
Ready-made AI software solutions are typically easy to use, even for a non-tech-savvy user. But they're not flexible, not scalable, and not customized to solve specific tasks for your business. They can also be difficult to integrate.
Custom AI Development
Custom AI solutions are flexible and scalable, so they can be adjusted the way you want them to be to solve your business problems. As a result, building such AI products is much more expensive.
Costs associated with developing a custom AI solution:
- Hiring AI developers or a whole dedicated team (each specialist involved needs to get paid).
- Working with data: collecting, processing, cleaning, labeling, etc. This type of work costs not hundreds, but thousands of dollars.
- Creating an AI algorithm and training it. This is the most labor-intensive part of the work, and therefore it costs a lot - from $10,000.
- Computing equipment and cloud services, which also cost money. Costs can start at several thousand dollars.
- Testing and support. You need to maintain and monitor the system constantly.
How Much Does it Cost to Build an AI System in 2025?
So, let's summarize what we’ve discussed above. The following factors influence the cost of developing an AI solution:
- Types of AI solutions
- Project complexity
- AI development tools and infrastructure
- Type and amount of data needed
- Algorithm training needs
- Regulatory and ethical compliance
- Deployment and maintenance
- IT outsourcing or in-house AI software development
- Ready-made AI or custom AI development.
Don't forget that you may need to hire specialists with different skill sets - mobile and web app developers, data analysts, machine learning engineers - and each of them will cost according to their expertise.
To get a realistic answer to the question of how much artificial intelligence costs, conduct a competitive analysis. You can find out how much companies similar to yours in size and type of business are spending on AI development.
Keep in mind that leading companies (such as Meta and Google) are actively investing in AI research and developing new technologies. However, this does not mean that you should spend the same amount of money on AI. It may be enough to create an AI tool with basic functionality.
9 Proven AI Cost Reduction Strategies
Good news: You can reduce AI development costs. Here's how to do it:
1. Leverage off-the-shelf technologies and tools
Consider using off-the-shelf solutions and open source tools. Platforms such as Google AI, Microsoft Azure AI, and AWS AI offer pre-built models and infrastructure that can significantly reduce development time. In addition, open source frameworks such as TensorFlow, PyTorch, and Scikit-learn, supported by active communities, offer robust capabilities at no extra cost.
2. Align budget and goals with stakeholders
You should discuss your budget with all stakeholders, as well as the processes AI is expected to improve. This transparency can help you avoid budget overruns.
3. Use good-quality data
Well-organized, high-quality data reduces the cost of AI development. Conversely, scattered information requires more resources to organize. You can use off-the-shelf tools to collect, prepare, and organize data.
4. Work with experienced outsourcing teams
To avoid additional AI development costs, work with IT outsourcing companies or remote engineers. You can significantly reduce costs by working with professionals in regions with competitive prices, such as Poland.
5. Start small with an MVP or PoC
Start with a minimum viable product (MVP) or proof of concept (PoC) to test the viability of your AI solution. In this way, you can learn more about what artificial intelligence can do and decide how to move forward without wasting your money.
6. Scale up gradually
By scaling as you go, you can avoid wasting money on features that are useless, and adapt to changing needs in an incremental and timely manner.
7.Be Agile
Adhering to the Agile methodology when creating AI projects provides a clear idea of the project roadmap, which leads to more efficient budget management.
8. Delegate non-core tasks
To streamline processes and optimize resources, partner with third-party teams to delegate tasks (such as organizing data before dealing with it) that are beyond your scope or timeframe.
9. Network with AI communities
Join AI communities to get advice, share resources, and collaborate. These connections can bring support and ideas at no additional cost.
Tips for Implementing AI in Your Business in 2025
Want to save more money when implementing AI technology? If so, here are some tips to help you get there:
- Start only when the goals and business tasks you want to solve with AI are clear, unambiguous, and achievable.
- Select an IT outsourcing team with proven AI expertise and strong communication skills.
- Along with agile, consider a lean approach that focuses on minimizing waste and delivering value quickly.
- Put data first. This means collecting and refining high-quality data to make AI work better. You can do this by adopting robust data management practices.
- Invest in team training to keep your people up to speed on the latest AI advances.
- Create a detailed budget for AI development and track expenses closely to avoid cost overruns.
- As you build an AI solution and plan its costs, make sure your product can be easily updated or expanded as needed.
Now let's take a look at what to expect when implementing AI.
The Long-term Value of AI Investment
For a business owner looking for cost-effective AI software, the long-term value of this investment can be as follows:
Competitive advantage
AI can differentiate your business by offering unique solutions and improving the customer experience.
Cost savings
Although the cost of developing an AI solution can be high, it reduces your long-term operating costs by automating all routine tasks.
Data-driven decision-making
No more guesswork. Instead, you can use AI that analyzes data to provide your organization with actionable insights, thus improving your decision-making and helping you reach new heights.
Innovation that drives growth
AI software positions your business as innovative and makes you attractive to your customers and partners.
Customer satisfaction
By delivering personalized experiences, such as chatbots and recommendations, AI can make your customers more satisfied with your services.
You are future ready
Whatever the future holds, you'll be ready to embrace it without compromising business performance. Why? Because you'll be armed with the most advanced technologies to protect you from any market disruption.
AI pricing: DDI Development Cases
Now that we have an idea of how much AI development costs, let's take a look at real examples of such solutions. Below are AI systems created by the DDI development team.
Project 1. AI-based LMS
The client's request was to develop a dynamic, AI-powered Learning Management System (LMS) that would meet the specific needs of learners, whether they are students, employees, or customers. As a result, we created an intelligent learning platform that uses advanced machine learning, deep learning, and data analytics to deliver personalized and adaptive learning.
This LMS adapts learning paths to individual progress in real-time, provides data-driven insights, and increases engagement through interactive features. Technologies such as predictive analytics, adaptive assessment, text-to-speech, and gamification make learning more effective and engaging.
By investing in this AI software, our client has simplified workflows, reduced training costs, and increased overall company productivity. In addition, the system increases administrative efficiency and facilitates more informed decision-making.
COST:
The cost of building a custom LMS is high because it requires a high level of personalization. This means the use of advanced technologies such as ML, AI, data analytics, and the involvement of highly specialized experts. The cost of developing such an AI LMS starts at $50,000. As the system's functionality expands, the initial investment naturally increases.
Project 2. AI-powered ATS
We were asked by a client to create an advanced Applicant Tracking System (ATS) using Artificial Intelligence to improve and streamline the hiring process. The system was expected to make the hiring process faster, more accurate, and less biased than traditional methods. And we succeeded.
In developing the AI-based ATS, we used natural language processing (NLP), machine learning, semantic search, ML bias reduction algorithms, and predictive analytics.
So this is what an AI-powered ATS delivers:
- NLP-based candidate screening.
- Recommendations on the best candidates based on their skills, qualifications, and experience as defined by ML algorithms.
- Improved keyword matching through AI, as the system can understand the context and meaning of search queries to pick out the right candidates.
- Automation of manual tasks such as analyzing resumes, extracting relevant information about candidates, and making initial contact with candidates.
- Optimization of recruiting strategy and candidate sourcing channels.
- Fast and unbiased recruitment with the desired results.
As for the cost of such software, it depends on the number of options implemented. At the same time, the client can choose the set of functions and the level of complexity of the system.
COST:
Any large AI software solution requires budgeting for each stage of creation, from design and development to testing and support. The complexity and number of features are also taken into account. Development of an advanced AI-based ATS, similar to the one described above, starts at $60,000 for the basic version. A more sophisticated version can cost $200,000 or more.
Project 3. AI-based recommendation system
The client needed an AI-powered system to personalize product and content recommendations for customers to increase engagement and sales. We were happy to take on the project.
The idea behind the AI-powered recommendation system we created is to use AI to analyze user data, such as behavior and preferences, and then provide them with tailored suggestions. All of this leads to increased user satisfaction and sales.
As for the technologies involved, it’s mostly machine learning, especially GPT-3/GPT-4, to learn from customer interactions and improve recommendations later on. To build the AI, we hired specialized software developers based on IT outsourcing and operated within an agile project management approach. Thus, we managed to create an MVP of an AI recommendation system that meets the client's goals.
The cost of developing an MVP of an AI solution differs significantly from the cost of developing a comprehensive system. Therefore, if you are planning to create such an AI solution, you should think about what goals you want to achieve with it, so that we can tell you what features it should have and what the cost of AI integration might be in this case.
COST:
The cost of developing an AI-based recommendation system starts at $40,000 and can reach over $100,000. In the first case, you can get a basic AI-based recommendation system for small-scale purposes, while in the second case we are talking about a comprehensive enterprise-oriented recommendation system for larger-scale purposes.
Bottom line
We've outlined the key components of the AI development pricing structure and provided tips on how to save your budget and reap the long-term benefits of AI implementation. Still have questions?
Of course, there are still questions, because the cost of AI development requires specific calculations rather than general considerations. That’s why feel free to contact us for a clear, detailed consultation. With our experience and expertise, we’ll help you understand AI costs, what to expect, and how to manage your budget effectively.
FAQ
How much does it cost to develop a basic AI solution?
If you need a basic solution, the cost of such AI software starts at $10,000.
What are the ongoing costs associated with AI?
Ongoing AI costs can include AI system maintenance, data management, cloud services, training, scaling, and compliance costs.
Why is AI so expensive?
This technology is advanced and currently very popular. Working with it requires deep knowledge, extensive programming experience, and costly tech.
What are the hidden costs of implementing AI?
These include budget overruns, unforeseen data management costs, integration issues, ongoing training needs, additional security measures, and the cost of correcting potential errors or improving efficiencies.
Are there cost differences between AI applications across industries?
Yes, there are. AI costs can vary depending on the complexity of the AI, the type of data required, industry regulations, and the level of specialized skills needed.
What is the expected return on investment (ROI) for AI?
The ROI of AI can come in the form of cost savings, increased efficiency, and increased revenue, among other benefits.