AI Development Cost Breakdown

Artificial intelligence is no longer a buzzword but a business reality. From automating workflows to powering entirely new products, AI solutions are getting more adoption each year. A recent McKinsey report shows that 78% of organizations now use artificial intelligence at least for one business function, compared to 55% in 2023. According to Statista, the AI technology market is projected to surpass $1.8 trillion by 2030.

Yet, artificial intelligence development is not cheap, and budgeting wrong can sink your project before it even launches. Some companies assume that because they can “play” with a free API key or spin up a demo chatbot, production-ready will cost almost nothing. However, serious AI applications require substantial investment. Therefore, the AI app development cost can range from $10,000 to $1,000,000 and more, depending on the project’s scope, complexity, and other factors. But where are such numbers coming from?

In this article, we will break down every aspect of the AI development cost and highlight hidden expenses, so you know what to expect and how to make smart investment decisions.

Before we dive into the detailed cost breakdown, please note that all prices in this article are approximate and reflect general market conditions. SoftTeco provides artificial intelligence consulting and development services, so if you want to estimate the accurate cost of your AI project, contact us.  

Factors influencing AI development costs

When clients ask “how much does it cost to make an AI?”, they’re usually hoping for a simple number. According to Clutch, the average AI app development cost can range between $10,000 – $49,999. But it’s more intricate and largely depends on your goals and needs. For example, there are a lot of ready-made AI solutions, the customization of which will cost you far less than developing the model from scratch. And that’s roughly just one of the aspects. Therefore, let’s take a closer look at the main factors that form the cost of artificial intelligence.

1. Scale and complexity of the project

We will start with the obvious – the major part of the total AI app development cost depends on the scope and complexity of the project. Turning a business idea into a working solution requires varying levels of time, expertise, and infrastructure, depending on what you aim to achieve. 

For example, a basic chatbot can be developed within a few weeks with minimum resources, while a more advanced product, like a recommendation engine or image recognition tool with essential features, requires more development time and budget.

Full-scale development of a custom AI solution is on another level. A fraud detection system for banking or a multilingual AI assistant with real-time processing are far more expensive and can take more than 24 months to develop.  

2. Type of AI solution

Another major factor influencing the AI pricing is the artificial intelligence solutions that you want to invest in. Here are the most common types of AI solutions.

Chatbots

AI-powered virtual assistants that are designed to simulate conversation with users. They can range from rule-based bots that follow pre-defined scripts to advanced conversational AI systems powered by natural language processing. Simple chatbots can be built with ready-made frameworks, but context-aware bots with multi-language support, integrations, and learning capabilities require custom development.

Recommendation systems

These systems analyze user behavior and preferences to suggest personalized products, services, or content. Since recommendation systems rely on large volumes of user data, costs increase with complexity, scalability, and integration requirements.

Generative AI

These systems use machine learning and large language models (LLMs) to create new content (like music, images, text, code, etc) based on existing patterns. The most obvious example is ChatGPT, whose model can be used for other AI systems. However, the development is often expensive because it involves complex pre-trained models, their fine-tuning, and infrastructure setup for high computational power. The more unique or domain-specific the use case, the higher the costs of Generative AI development.

Fraud detection

These solutions can spot suspicious patterns or anomalies in real-time. They are common in industries where security and compliance are critical, for example, banking or insurance. Development costs are usually high because fraud detection demands real-time data processing, high accuracy, and often regulatory compliance, making development complex and resource-heavy.

Image recognition systems

Image recognition uses computer vision and deep learning to identify and classify objects within images or videos. Developing such systems requires large labeled datasets and specialized neural networks. 

Predictive maintenance software

This software uses AI to predict when machines or equipment are likely to fail, helping companies reduce downtime and maintenance costs. Predictive maintenance is among the most expensive solutions, since it often requires IoT sensor integration, data pipelines, and advanced predictive analytics, all customized to specific industries.

3. Data preparation

Data is basically the heart of any AI system; therefore, its quality, quantity, and accessibility can greatly affect development costs. When starting a project, you have several sourcing options: 

• Use pre-existing or public datasets
• Collect and process your own data

The choice depends on the project’s complexity and specific requirements. Many low-cost or free datasets are available online for common tasks such as image recognition, text analysis, or tabular data processing. Custom datasets, however, are often necessary for specialized applications or high-stakes projects. For example, a healthcare startup might spend $200,000 acquiring a curated dataset of X-rays, while an autonomous vehicle company could invest $500,000 or more to collect real-world driving footage.

Dataset expenses also include data preparation, which is critical to ensure AI models can learn effectively. This typically involves:

• Data cleaning: Removing duplicates, correcting errors, handling missing values, and standardizing formats. Depending on the dataset size and complexity, costs can range from $1,000 to $20,000.
• Manual labeling: Annotating images, text, or other data types so the model can learn correct associations. This process is complex and time-consuming; specialized manual data labeling can exceed $100,000 for just 10,000 items.
• Privacy and compliance: Ensuring data meets regulatory requirements such as GDPR or HIPAA. This includes anonymizing sensitive information, securely storing datasets, implementing encryption, and maintaining documentation for audits. The approximate cost for privacy and compliance measures ranges from $5,000 to $50,000, depending on the industry and level of regulatory scrutiny.

Don’t stress too much over data preparation

In fact, with modern LLMs, it’s not such a big deal anymore. There are a lot of pre-trained models that can perform tasks without you gathering a custom dataset. You can also generate synthetic datasets with LLMs to train your model for your specific use case, or use LLMs to help with labelling. These can significantly reduce costs and speed up the labeling process.  

ML Engineer at SoftTeco

Roman Kyrychenko

4. Tools and infrastructure

AI development requires powerful infrastructure, specialized frameworks, and third-party services to train, test, and deploy models efficiently. When it comes to running AI workloads, you basically have three options: cloud, on-premise, or a mix of both.

• Cloud infrastructure. This is the most common choice today, with platforms like AWS, Google Cloud, and Microsoft Azure. They give you ready-to-go machine learning environments, easy storage, and access to powerful GPUs or TPUs, all without having to buy your own hardware. Costs depend on your needs. For example, small projects with a few GPUs might run approximately $500 – $2,000 per month, medium setups with multiple nodes could be $5,000 – $20,000, and really big projects with dozens of top-tier GPUs can exceed $50,000 per month.
• On-premise infrastructure. You host all your AI workloads on your own physical servers. This option requires a high upfront investment for hardware, as well as its setup and costs for ongoing maintenance. For example, you might spend $50,000 – $150,000 on a high-performance AI server with multiple GPUs, plus another $10,000 – $30,000 per year for electricity, cooling, and maintenance. In exchange, you receive full control over data and no dependency on external providers. It’s a good choice for highly regulated industries such as healthcare or banking.
• Hybrid infrastructure. A hybrid setup gives you the best of both worlds. Sensitive data stays on local servers, while heavy training jobs run in the cloud. For example, a company could keep an on-premise server for storing and preprocessing data and rent cloud GPUs for training. This way, you get flexibility and cost efficiency, scaling up compute power when needed, without compromising data privacy.

Choose wisely

As a rule, cloud solutions are cheaper at the start, since you don’t need to purchase hardware and you only pay to rent servers. On-premise infrastructure, on the other hand, is more expensive initially but can become cheaper in the long run. Cloud providers typically offer packaged solutions, which may include features developers don’t always need, meaning you might pay for more than you use. With on-premise setups, there is a risk of a “cheap man pays twice” scenario: mistakes during initial configuration can be costly. In the cloud, these issues can usually be resolved quickly by upgrading or replacing machines with just a few clicks.

ML Engineer at SoftTeco

Roman Kyrychenko

As for the tools for AI development, here are some of the reasons why the choice of technologies also influences the AI price:

• Licensing and subscription fees. Many AI development tools, such as data labeling platforms, MLOps solutions, or commercial ML frameworks, come with subscription or licensing costs. While some frameworks like TensorFlow or PyTorch are open-source and free, enterprise-grade tools (e.g., Databricks, AWS SageMaker, or Azure AI) can cost thousands of dollars per month, depending on usage.
• Specialized hardware needs. AI systems, particularly those involving computer vision or large language models, need powerful GPUs or TPUs. High-performance chips like NVIDIA A100s can cost $10 per hour in the cloud or tens of thousands of dollars per unit if purchased for in-house use. This significantly raises the budget compared to projects that run on standard CPUs.
• Integration with existing systems. The more advanced or proprietary the tools, the higher the integration costs. For example, integrating an AI recommendation engine into an existing ecommerce platform may require custom APIs, middleware, and specialized developers familiar with both the AI tool and the client’s software ecosystem.

5. Development team size and expertise

The cost of building an AI system isn’t determined only by the technology; it also heavily depends on the size and expertise of the development team. AI projects are resource-intensive and often require a mix of highly skilled specialists, each contributing to a different stage of the process, from data preparation to deployment. An AI project may require: 

• Project Managers
• Data scientists
• ML engineers
• Frontend Developers
• Backend Developers
• QA Engineers
• UI/UX Designers

A small project may only need a few people, while a full-scale AI platform for an enterprise might require a large cross-functional team. The larger the team, the higher the labor costs, especially in projects that run for months or years.

AI professionals are among the most expensive in the IT industry. A senior ML engineer or data scientist with experience in neural networks, NLP, or computer vision can command much higher rates than general software developers. Moreover, projects in regulated industries (e.g., healthcare or finance) often require domain experts, which adds another layer of cost. For instance, a senior ML engineer in Eastern Europe may cost around $80 – $120/hour, while the same role in the US or Western Europe can easily exceed $150 – $200/hour.

Here is a table with the approximate per-hour rate of AI specialists in the US and Eastern Europe:

If you’re curious what might be the cost of an AI development team at SoftTeco use our calculator to get an instant estimate.

Estimate The Cost Of Your AI Development Team

6. Regulatory compliance and legal considerations

In regulated industries like healthcare, finance, and education, compliance with laws such as GDPR, HIPAA, PCI DSS, and emerging AI-specific regulations is non-negotiable. Compliance adds costs in the form of security features, audit trails, encryption, consent management, and explainable AI functions. Non-compliance risks not only include financial penalties but also reputational damage.

7. QA testing and validation

Unlike traditional software, where testing often focuses on functionality and bugs, AI systems require more extensive and specialized testing to ensure accuracy, fairness, and reliability. As they generate probabilistic results, their outputs can vary. This requires more rounds of testing and validation to measure performance across different scenarios and datasets, increasing both time and cost.

Therefore, to validate an AI model properly, companies often need vast amounts of test data that represent real-world conditions. Regular software QA engineers may not be enough. AI projects often require domain specialists (e.g., doctors for medical AI) to ensure outputs are accurate and unbiased. Hiring these experts drives up labor expenses.

Moreover, AI models are rarely perfect after the first training. You need to run multiple validation cycles before deployment. Each cycle can take days or weeks, adding to project timelines and costs.

The hidden price of artificial intelligence revolution: cheap AI won’t last

The cost of AI development in 2025 remains volatile, which is a direct consequence of the ongoing AI revolution. Fierce competition between giant corporations and startups for AI leadership drives massive price dumping of AI services as companies race to capture market share and later enjoy near-monopoly status. They can afford these losses this year since AI investments are astronomically high, but this era of affordable prices for customers will eventually end when AI leaders need to turn a profit.

Another consequence of this AI competition is skyrocketing demand for computationally efficient chips, which not only boosts NVIDIA’s stock but also drives up AI infrastructure costs for any company. The silver lining is that this demand has sparked the development of many new high-efficiency chips, now affordable even for IoT devices, as NVIDIA works to meet market needs.

The most stable part of AI development costs remains talent, even though companies like Meta are willing to pay millions in signup bonuses for rock-star AI developers.

ML Engineer at SoftTeco

Roman Kyrychenko

How much does AI cost: price breakdown

Now that we discussed what factors influence the cost of implementing artificial intelligence, let’s break them down according to application complexity, AI solution type and industry influence. 

AI app complexity cost breakdown

To make it simple, we will divide AI solution complexity into three categories:  

Simple AI solutions

Approximate cost range: $10,000 – $80,000

These projects are often PoC models or MVPs designed to validate an idea or demonstrate functionality. They usually involve a single AI model performing a simple task, such as a chatbot answering basic questions or a small recommendation engine suggesting products. Development is relatively quick, often taking a few weeks to a couple of months, and relies on publicly available or low-cost datasets. Infrastructure requirements are minimal, and integration with other systems is usually limited. Because of these factors, basic AI solutions are an accessible entry point for startups or businesses that want to test their ideas without large investments. 

Advanced AI solutions

Approximate cost range: $50,000 – $150,000

These AI systems are more polished and ready for real business use, perfect for small or mid-sized companies. They can do things like handle detailed customer chats, suggest products shoppers might like, or spot potential fraud. Building them takes more work (usually 2-6 months) because they often combine several AI tools, require real-time data processing, and connect smoothly with your existing systems. You’re also paying for better design, cleaner data, and thorough testing.

Custom large-scale AI applications

Approximate cost range: $100,000 – $1,000,000+

These AI projects are fully custom-built systems that do serious heavy lifting for large companies. We’re talking about things like self-driving car brains, factory equipment that predicts its own breakdowns, or bank fraud-detecting systems. These projects are highly tailored and include multiple interconnected AI models, advanced analytics, real-time processing at scale, and full integration with enterprise systems. They may also require multilingual support, high availability, and strict security and compliance standards. Building them isn’t quick or cheap (think 6+ months and serious budget), since they require:

• Massive amounts of perfectly labeled training data;
• Specially designed algorithms;
• Rock-solid infrastructure;
• Solid QA and validation level.

Here is a sum up AI app development cost estimate table based on the project complexity:

AI application type cost breakdown

Here is a sum up cost table based on the type of artificial intelligence solution:

AI app development costs depending on industry specifics

Even if two companies build the same type of AI (say, an image recognition system), the final price can vary a lot depending on regulations, risks, and technical requirements in their industry. 

For example, AI that analyses medical images (like X-rays or MRIs) must comply with strict data privacy laws (HIPAA/GDPR), undergo rigorous clinical validation, and meet healthcare certification standards. In retail, a similar AI used for inventory management or customer analytics may have fewer regulatory hurdles but could demand high scalability and cloud infrastructure costs to handle millions of product images.

Here is a summary of typical cost ranges for AI solutions in key industries: 

Do not forget about some hidden costs of artificial intelligence 

Hidden costs in AI development are the less obvious expenses that often emerge during or after a project, and they can significantly affect both budget and timelines. It’s better to be safe than sorry, so plan ahead and consider factors that may be overlooked during initial planning but could impact the allocated development budget later. That includes: 

Operational expenses 

Once deployed, AI systems often incur continuous costs that are easy to underestimate during planning. These include cloud compute for inference, storage for embeddings in vector databases, and charges for high-volume API usage. If not optimized, these costs can quickly surpass the initial development budget.

Maintenance and updates

AI models are not “set and forget” systems. Over time, their performance can degrade due to data drift, changing user behavior, or evolving patterns in the target domain. Maintenance can also involve model retraining, updating feature pipelines, patching dependencies, and revalidating compliance with regulatory standards.

Employee training

Successfully adopting AI often requires more than just deploying the technology; it also involves preparing the people who will use it. Employees may need training sessions, updated workflows, and new documentation to ensure the AI is used effectively and responsibly.

4 useful tips to reduce AI app development cost

While AI solutions often come with hefty price tags, smart strategies can keep your budget in check. Here are some tips that can help you reduce AI costs.

1) Outsourcing AI project development to an experienced vendor

Hiring an external AI development partner with proven industry expertise can save time, reduce trial-and-error, and minimize the risk of budget overruns. Experienced vendors typically have established workflows, reusable components, and in-house specialists who can tackle challenges quickly. For example, instead of spending months building an in-house team for a natural language processing project, a company could contract a vendor who has already delivered similar solutions.

2) Using open-source AI tools and pre-trained models

Of course, you can develop artificial intelligence model from scratch, but why go the hard way when there are numerous pre-built solutions and APIs? Leveraging open-source frameworks such as TensorFlow, PyTorch, or Hugging Face, along with pre-trained models, can save teams a ton of time and money.

Take a chatbot, for example. You could start with an open-source language model that’s already been trained on general text, then tweak it with your own industry-specific data. SoftTeco did just that and developed a smart AI chatbot for the new website on the basis of GPT-4. That way, we significantly reduced development time, costs, and acquired a great digital assistant, which can be helpful not only for our clients but for the employees as well.  

3) Start small and scale later

Many companies overspend on AI by jumping straight into complex, large-scale projects that require big datasets, expensive cloud resources, and a full senior team from day one. But it will be cheaper to start with a proof of concept or an MVP and scale according to the feedback. This will help you avoid pouring money into features that might not deliver value, quickly test the AI system in real-world conditions, and allocate resources to areas with the highest impact.

4) Opt for cloud-based AI services

Cloud platforms such as AWS, Google Cloud, and Azure offer ready-to-use AI services for computer vision, speech recognition, NLP, and model hosting. Using cloud-based infrastructure means there’s no need for large upfront investments in hardware like GPUs, and teams can scale resources up or down depending on demand.  

Final Thoughts

There’s no turning back – artificial intelligence solutions will only continue to advance, becoming more integrated into business processes across every industry. But let’s be real, AI costs can be all over the map. What you’ll pay depends on what you’re building, the tech you choose, and those pesky compliance hoops you might need to jump through. The market is always changing; however, the main factors influencing AI development cost will remain.