How Much Does It Cost to Develop AI Software in 2025?

These days, every startup founder or CEO of a mid-size enterprise recognizes the potential of AI technology, but the cost of developing AI software presents a substantial barrier to adoption. 

The critical question for many businesses is: How much does it cost to develop AI systems? While there’s no universal price tag, the overall cost of AI development varies depending on multiple factors such as – 

But these are just a few factors. Read this blog to understand the exact cost breakdown and explore the critical factors that influence AI software pricing. 

What’s the Cost of Developing AI Software?

The cost of developing an AI system depends on how complex or simple your application is. 

Let’s say you want to build simple AI software; then the cost would vary from around $20,000 to $50,000. 

If you want to build a complex or advanced software solution for a mid-size startup or a large-scale enterprise, the cost would be between $100,000 and $500,000. 

But it can go higher depending on training data, infrastructure, and ongoing maintenance costs. 

If you opt for developing custom AI solutions, then app development cost would be $500,000 and even more, as it requires machine learning model integrations and cloud computing resources. 

Your software development cost differs depending on the choice of AI frameworks, libraries, or any specialized hardware you use. 

However, the development expenses might increase depending on the changes in project requirements and when a client demands certain features to be added to the product. 

What Factors Affect the Cost of Developing an AI System?

Let’s discuss what factors can affect your AI development cost: 

1. Complexity of AI Application

The cost of creating a simple AI chatbot is less compared to creating a deep learning model that can predict market trends. This simply means that the more complex the AI system you want, the more expensive it will become. 

The cost would also increase depending on the project scope, the number of resources required, and the choice of technology stack. 

2. Data Availability and Quality 

Without high-quality data, you can’t build a successful AI product. 

The data on which the model is trained should be of high quality. This means that AI needs high-quality data, and if the data isn’t collected, cleaned, and labeled, it results in high development expenses. 

If the data isn’t labeled or structured, businesses would require extra time and resources for collecting high-quality data to train their AI systems. When developing an AI software, the cost of data collection and preparation takes 2-3% of the overall AI app development cost. 

To give you a rough estimate, the overall cost of data preparation and collection ranges between $10,000 to $90,000. The cost may vary depending on how complex your dataset is, as different datasets go through different levels of training. 

For instance – Healthcare industry requires highly specialized datasets for applications like medical imaging where annotated data can cost $50,000 per project. 

This means that developing a simple chatbot’s model is more cost-effective than training an advanced model. The latter requires more processing power and resources. 

If you deal with large amounts of data, you’ll need to invest in technologies such as big data. 

Additionally, the technology vendor must ensure that the data used to train the model is safe and secure. 

3. Custom AI Solution and Prebuilt AI Software 

If you need an exclusive AI solution tailored to the specific demands of your business, the cost of building custom AI software would be high. 

The rising development cost is due to more resources and time required for creating a custom AI solution that’s as unique as your business needs. 

Alternatively, if you need a quick, cost-effective option, you can opt for prebuilt (ready-made) AI software that can be integrated with existing software. 

3. Expertise of Developers 

The cost of developing AI software is influenced by the expertise of developers. 

Hiring experienced developers, such as data scientists and AI engineers, costs more than working with a software development company that has a team of less experienced engineers.  

Obviously, hiring an experienced development team costs more, but the product they will create will be of higher quality. 

4. In-House or Outsourced Developers 

In-house management means your team will develop, maintain, and update your AI software. Hiring an in-house AI team is costly and time-consuming, but outsourcing can reduce expenses. 

In an in-house development model, you bear the cost of recruiting, hiring, and retaining employees. Plus, you’ll need to pay fixed monthly salaries and offer extras such as: 

According to WebFX, the in-house salary of data scientists ranges from $50,000 to $140,000 per year. These numbers are average estimates and can vary depending on location and project requirements. 

In fact, smart companies tend to outsource tasks they’re not proficient in. If you lack access to trained AI engineers and data scientists, outsourcing AI development is worth considering. Hiring offshore developers is cost-effective due to lower labor costs. 

Outsourcing AI development services in India or other regions means AI developers will charge less.  

For example – Outsourcing software development projects to AI engineers in Ukraine costs around $25-$49 per hour. 

5. Development Timeline of AI Software 

The timeframe for developing an AI system affects development costs.  

If you want a technology vendor to create AI software in two months, while the typical development process takes 3-4 months, your outsourcing partner will charge more. This is because speedy development requires additional resources and effort. 

Just because you want to develop a complete AI system doesn’t mean the outsourced partner will cut features to enable a faster release. 

So, 

Shorter development time = More resources = More development expenses 

How to Optimize AI Development Cost?

You can reduce the AI development cost through any of these strategies- 

1. Define Clear Objectives 

Before designing your AI, specify the goals and objectives you want to achieve and how you plan to do so. 

This will simplify the development process and save money spent on unnecessary activities outside the project’s initial scope. 

2. Consider Open-Source Solutions 

Building custom software solutions from scratch is expensive and resource intensive. Though businesses want a unique software solution developed from scratch, it comes with challenges like long development cycles and high maintenance overhead. 

Using pre-trained AI frameworks and open-source models with robust APIs and active communities can help lower development prices. 

Instead of building everything from scratch, use open-source tools and frameworks to save time and resources (saving 30-40% time and money when compared to starting from scratch). 

Platforms like TensorFlow, PyTorch, and scikit-learn provide developers with pre-built algorithms and models, speeding up development and reducing custom coding efforts. 

Thus, the cost of building AI solutions will decrease with open-source frameworks, pre trained models and cloud-based services are going to improve.  

For Instance – AWS and Google cloud have reduced the cost of scaling AI tools which makes it easier for smaller businesses to adopt. 

3. Iterative Development Approach

Be agile and iterate. Build your AI model in small increments and test constantly.  

This will help you immediately spot the problematic points as well as ensure that most of the rework and cost overruns will be minimal. 

4. Use Serverless AI Instead of Dedicated Architecture

Many cloud providers offer Serverless machine learning options, allowing AI models to run only when needed rather than maintaining expensive instances continuously.

This is especially useful for applications that do not require continuous AI processing, such as chatbots or fraud detection systems that only run when triggered.

5. Outsourcing Non-Core Activities

Outsource activities not aligned with core operations, such as data labeling, annotation, and model training, to specialized service providers. 

This approach maximizes efficiency, reduces operational expenses, and generates tangible results from allocated resources. 

6. Train Your AI Model on Smaller, High-Quality Datasets

One key requirement to build AI is training, and for training the AI model, you need past or historical data. If the data isn’t valid or correct, then no matter what technology you use or developers you hire, it will give you incorrect details. 

Many businesses assume they need vast amounts of data to train AI models, but carefully selected, high-quality data often produces superior results with less computing power. 

A great approach to get high quality data is by filtering and curating it before input. This reduces training time and storage costs while improving accuracy. 

7. Choose the Right Development Partner

Instead of hiring an entire team, outsource tasks to technology vendors in low-cost regions. 

Working with an experienced AI development company helps save time and money in the long run.

Thus, it’s important to pick a partner who has expertise in developing AI software for businesses.

What Would Be the Future Cost of Developing AI Software?

The future of software development costs can be influenced by various factors which can either result in cost reduction or increased expenses for businesses.

In a nutshell, while developing basic AI software costs less due to available tools, specialized solutions cost more as they require greater infrastructure investment and computing power.

Why Choose BigOhTech for Custom AI Software?

Whether you need a simple AI chatbot or advanced natural language processing software, we offer comprehensive AI development solutions tailored to your business goals.

Our expert AI/ML engineers have extensive experience developing custom software to streamline business processes and enhancing customer experiences.

More than that, we developed AI software for luxury hotels to take feedback from customers. The software was trained on different models such as BERT, CNN, and other machine learning models. 

The hotelier was able to get a quick overview of how their business is performing, identified service gaps, and improved their services. Result? They got a 12% increase in their revenue per available room.

Do you want to get similar results for your business? 

Let’s work together to build custom AI software for your business.