How Much Does AI App Development Cost? Your Only Guide

Artificial Intelligence is constantly transforming various industries by automating operations, improving user experience, and providing new opportunities. However, for multiple businesses, these benefits of AI come with a significant challenge: rising AI app development costs.

This is especially true for small to mid-size companies with limited resources. Despite having hopes, the increasing AI app development costs are a significant barrier to entry and growth in a competitive market. 

Whether you are an entrepreneur taking your first step in AI app development or an enterprise with knowledge of AI, knowing the costs is non-negotiable. Clarity about the cost of building an AI app helps you strategize and plan accordingly. Lack of knowledge about this can lead to overspending and missing valuable opportunities.

To simplify this task, we will explain how much does it cost to develop an AI app based on some of the most important factors and strategies to optimize the AI app development costs. 

Let’s dive deeper into it. 

Why AI App Development is Booming: Key Market Insights

Here are some of the most recent market insights of the AI world:

• As per GrandViewResearch, the global AI apps market was valued at USD 2,940 million in 2024 and is anticipated to grow at a CAGR of 38.7% from 2025 to 2030. 
• Around 78% of companies worldwide have started adopting AI in their businesses. 
• AI apps generated revenue of $4.5 billion in 2024, which is 136% higher as compared to 2023. Moreover, it is estimated that the industry will continue to grow over this decade and surpass $156 billion in revenue in 2030.

The above statistics indicate that the AI app development market is growing at a pace like never before and is expected to impact small to big businesses across the globe in a positive way.

Key Factors Affecting AI App Development Costs

Developing an AI app involves a wide range of strategic, technical, and operational factors directly influencing the total investment required. From the project’s complexity to the number of tools, APIs, and third-party integrations needed to the development team’s expertise, every factor plays a vital role in deciding the final budget. 

Let’s look at the most essential factors that affect the cost of building an AI app.

#1 Scope & Complexity of the App

The cost to develop an AI app can range from $20,000 to $120,000 or more. The cost is influenced by a wide range of factors, the most important ones being the scope and complexity. 

Here, scope involves what the application is intended to do, and complexity includes how advanced and layered the solutions need to be. 

An effectively defined scope helps to set proper expectations and even streamlines the development process, ensuring that the project aligns with the business goals. When the scope becomes bigger with more features & functionalities, it requires more resources, which ultimately increases the development costs. 

Here is a general estimation that answers your question: how much does it cost to make an AI app?

• Basic App: These basic AI apps cost between $20,000 and $50,000. They have simple features and limited capabilities. They are primarily developed using inherent APIs such as image recognition or basic chatbots. 

• Medium-Scale App: This kind of AI app development costs between $50,000 and $1,20,000. It involves tailored AI model integration, real-time analytics, and backend work. It requires more development time and a bigger team. It suits businesses that want customized AI solutions, such as an AI-based recommendation engine or a chatbot with NLP capabilities.

• Complex AI App: It costs around $120,000 or more. These apps involve custom-built AI/ML models, deep-data pipelines, and real-time machine-learning capabilities. For instance, an AI-based financial forecasting app includes ERP, user behavior analysis, and future insights.

#2 Data Requirements

Accurate AI models and predictions require a massive amount of superior-quality and structured data. Hence, data is the core of AI app development. Structured data even helps train AI models, from initial data collection to online management to optimization and compliance.

Now, as AI app costs involve data, this consists of multiple phases, each with its own cost. Let’s look at the key stages of the data process in AI app development.

Data Collection: Collecting a massive amount of raw data from diverse sources, such as data-sensors, APIs, IoT devices, third-party sources, or user interactions.

Data Cleaning and Preprocessing: Eliminating errors and duplicates, fixing consistency in the 

data, managing any missing values, and transforming the data into a usable format. 

Data Labelling: Tagging data manually or via automated tools to ensure that it is readable for the supervised machine learning models. (For instance, labeling images or text)

Data Storage and Management: Securely storing a massive amount of structured and unstructured data in databases, data warehouses, or cloud storage solutions. It leads to better accessibility and compliance.

Data Integration: Connecting data from a vast number of sources to build an extensive data for AI model training and evaluation.

Data Governance and Compliance: Complying with a particular state or region’s privacy laws, ethical standards, and data quality needs in the entire app development process. 

Model Training and Evaluation: This involves using all the collected data to train, test, and validate the AI models, ensuring they perform effectively.

Monitoring and Maintenance: This involves multiple steps, such as constantly tracking the data quality and AI models’ overall performance, modifying data as needed, and archiving or deleting unnecessary data.

#3 App Development Team’s Size & Expertise

The overall composition of the AI app development team and their expertise greatly impact the quality and cost of your AI application. 

The team’s structure generally relies heavily on the project’s scope and complexity, timeline, and budget. This means that smaller projects need a smaller team of experts; however, a complex project demands a larger and more specialized team. 

Let’s look at the teams’ composition based on the project scale:

Basic Projects: 3-5 members (Project Manager, Backend Dev, Frontend Dev, QA, AI Consultant)

Mid-Level Projects: 6-10 members (Add dedicated AI/ML Engineers, Data Analysts, UI/UX Designer)

Complex Projects: 10+ members (Include multiple AI/ML experts, Data Scientists, Cloud Architects, DevOps, Security Experts, and more)

Keeping a balanced team is the most important thing. Why? A small team can result in burnout and project delays, ultimately harming your budget. On the other hand, a big team can result in high costs and organizational issues. 

Therefore, choosing a team size and experts is crucial to ensuring the project stays on track, within budget, and meets your business objectives.

#4 App Development Team Location

The location of the app development team has a direct impact on the overall project costs. However, developer rates might differ based on the labor market, expertise, and overall cost of living. 

For instance, the cost of developers in North America or Western Europe is higher because of their expertise; however, the developer cost for offshore and onshore teams might be lower with the same quality. 

Let’s look at the average hourly rates of the developers in different regions of the world.

#5 AI App Development Costs Based on the Platform

Multiple platforms are available in the market, which includes native, hybrid, cross-platform, and web. The selection of these directly impacts budget timelines and long-term maintenance. Each option has its own pros and cons and ideal use cases based on the target audience and performance expectations.

Native: In this approach, apps are developed separately for Android and iOS using Swift (iOS) and Kotlin/Java(Android). These apps utilize the device’s built-in functionalities, such as sensors, microphones, and software, to provide the best performance and user experience. 

However, as an application is built for a particular platform, the development cost is high.

Cross-Platform: In this approach, developers can utilize the same codebase to build applications for Android and iOS. Thus, it helps to save resources and even development efforts. Although the apps built using this approach look the same, several differences exist.

Cross-platform apps can be hybrid, web-based, or built using compiled mobile app frameworks(React Native, Flutter). Not all hybrid apps are cross-platform, but the primary 

The purpose is to offer a consistent experience using the same codebase on different platforms.

The significant benefits of cross-platform apps include broader reach, lower development costs, diverse market accessibility, and simpler maintenance. Popular examples include TikTok, Pinterest, Instagram, and more.

Web App: A web app is completely platform-independent and can be accessed from web browsers. It can provide the same functionality in an app as a website. Moreover, by considering some creativity, you can design a web app that looks similar to a native app. 

You even have the option to build different types of web apps, such as static web apps, dynamic web apps, eCommerce web apps, progressive web apps, single-page applications, and more. 

Web apps deliver some of the most amazing benefits, such as cost-effectiveness, accessibility, scalability, enhanced revenue, high efficiency, and more. 

Some of the popular examples of web apps include Google Docs, Netflix, Trello, eBay, Canva, and more.

Hybrid App: Hybrid apps are made by merging native and web solutions. Like native apps, users can download, install, and access hybrid apps from the app stores. However, the main structure of these apps is not the same. 

On the core level, hybrid apps look like web apps. They are somewhere between native and web apps. 

These apps comprise a web view component inside the native containers. Hence, AI developers need to write code using web development languages, such as HTML, CSS, and JavaScript, and insert the code in a native app with the help of Apache Cordova or Ionic’s Capacitor. 

Moreover, the developers can make apps for multiple platforms at a cost-effective price, launch them quickly into the market, and maintain them easily. However, they might have to deal with specific performance and UX issues.

Some of the popular hybrid apps are Nerd Wallet, eBay Motors, Tesla App, BMW app, and more. 

#6 Technology Stack and Tools

The technology stack is the backbone of any AI application. Hence, selecting the right one will directly impact development cost, speed, scalability, and long-term flexibility. Various advanced tools are available in the market; however, they have a steeper learning curve and higher prices. 

Here are the most essential components of the AI technology stack that you should know:

• Programming Languages: Python, Java, Swift, Kotlin, and JavaScript
• AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, OpenCV
• Cloud Platforms: AWS, Google Cloud, Azure (often used for scalable compute, storage, and AI services)
• APIs and Libraries: OpenAI, Dialogflow, IBM Watson, Hugging Face, etc.

Here, we have provided the components of the technology stack along with different options. 

However, selecting the right programming languages, frameworks, and more should be made considering the project goals and budget.

You can also utilize built-in APIs to reduce the development cost here; however, this wouldn’t be possible in the case of tailored solutions. In addition, the usage of databases, DevOps tools, and other paid tools increases the AI app development costs.

#7 Integration with Existing Systems

If you plan to integrate your AI application with existing systems like CRMs, ERPs, databases, or third-party APIs, be prepared for a significant level of customization. Such integration directly impacts the app’s functionality and the overall user experience.

Here are the key factors that influence integration costs:

• Additional development time and expenses are required if legacy systems are outdated or incompatible.
• The number and complexity of systems that require integration.
• The effort needed to build secure and scalable interfaces for seamless data exchange.
• Availability and quality of API documentation for existing platforms.

Planning your integration approach early in development helps minimize bottlenecks and ensures seamless, scalable system connectivity.

#8 Maintenance and Support

Many businesses think they don’t have to pay anything after deploying the AI application. However, this is not the reality. You need to consider the best practices for app maintenance to keep your app functional, secure, and up-to-date for a long time. In addition, updates are also necessary to integrate new features and functionalities and remain competitive in the market. 

Here are some of the key areas that you need to consider during app maintenance and support:

• Fixing bugs and software updates
• Optimizing model performance and retraining
• Checking the infrastructure and modifying it as per necessity
• Security patches and compliance updates 

AI app maintenance costs are a small portion of the overall development cost. For instance, they can be 15 to 25% of the entire cost of building an application. 

The maintenance cost here varies based on the resources and team required to maintain a well-maintained application, modify it according to the user’s needs, and advance according to the technology, leading to long-term longevity and customer satisfaction.

#9 AI App Development Costs Based on Industry

AI app development costs vary widely depending on the industry. This is due to differences in use cases, features and functionalities, compliance needs, system complexity, and data sensitivity. Moreover, industries face different challenges depending on the technology used, expertise required, and investment in the development time.

Let’s look at the estimated cost to develop an AI app for diverse industries.

Apart from this, there are also various other industries for which AI applications can be made. The cost for building apps for these industries will differ based on the requirements and goals.

Hidden Aspects Affecting AI App Development Costs

Apart from the visible AI app development costs, some hidden costs impact the AI app budget. Initially, there is a high possibility that these might be overlooked; however, they play a crucial role in the AI app development costs. Therefore, it’s vital to consider these in the AI app development costs.

Let’s look at the hidden aspects affecting the cost of building an AI app.

#1 AI Model Training Costs 

Training AI models, particularly custom or large-scale models, is resource-intensive. It requires a vast amount of datasets and robust computing environments. The cost further varies based on the model’s complexity, overall data, and the total number of training sessions required. 

To conduct intensive computations and achieve optimal training performance, you require some advanced hardware (such as GPUs or TPUs), cloud computing resources, and the energy needed for exclusive computations. These hardware costs increase if you want better quality, performance, and scalability in the case of deep learning or large language models. 

In the end, AI model training using different processes and hardware affects the app development cost.

#2 AI App Publishing

AI app publishing is one of the most neglected factors when dealing with development costs.

Whenever you launch an app on mobile app stores (app stores or Play Stores), enterprise platforms, or SaaS marketplaces, you need to pay a specific fee, which ranges between $25 and $100 or more, depending on the platform. Besides this, there can also be subscription charges or revenue-sharing fees. 

Additionally, you need to comply with the regulations according to the region, which might also increase the cost. This could involve getting certifications and making sure the app complies with the regulatory requirements of different markets.

#3 AI App Data Privacy & Security Costs

Keeping the entire business and users’ data secure should be your top priority. Here, keeping the data secure involves implementing robust encryption mechanisms and complying with the region’s data protection regulations. 

All of this requires a significant amount of investment in data governance frameworks, risk assessments, and ongoing monitoring. 

Here are some of the common costs involved in maintaining the security of the region. 

• Applying solid data protection measures
• Conducting security audits at periodic intervals
• Resolving any vulnerabilities and errors in the data 
• Legal consultation charges for compliance and documentation
• Updating the infrastructure to protect data storage and processing

If your app doesn’t comply with any of the country’s regulations, it can lead to fines of up to a certain amount. Therefore, investing in security is necessary to keep data secure.

#4 Data Monitoring and Analytics

Data monitoring and analytics are the most essential aspects of AI development, as they help keep your app reliable, secure, and effective. After deploying the AI application, constant tracking includes model performance, detecting model data theft, identifying anomalies, and addressing data quality issues. 

For the same, you will need to set up dashboards, logging mechanisms, and automated alerts to visualize data trends, measure ROIs, user behavior, model accuracy, and system health, and continuously improve the system.

In addition, you might also have to make necessary investments, use cloud-party services, and implement third-party integrations. All of these services help optimize AI performance and prevent costly errors or compliance issues in the near future. 

Key Strategies to Optimize AI App Development Costs

AI app development is expensive; however, by using some essential strategies, businesses can maximize ROI and achieve great results without spending much. Here are some essential strategies that you can consider to simplify development, reduce waste, and speed up the time to market. 

#1 Use Built-in AI Models and APIs

Utilize pre-trained models and APIs from some of the best providers, such as Google, OpenAI, and AWS, to avoid building anything from scratch. With these plug-and-play models, you can speed up the development, reduce risk, and enable the team to focus on tasks that add more value to the AI application.

#2 Use Open-Source Tools and Libraries

Various tools and third-party integrations increase the AI app development costs. Instead, open-source frameworks, such as  TensorFlow, PyTorch, and Hugging Face, provide various essential features and functionalities, robust community, and constant innovation at no cost. By considering these resources, you can reduce the app development cost. 

#3 Agile Development Methodology 

The agile development approach works on the principle of sprints. Here, sprints are the small units of the entire project. In each sprint, developers need to complete a dedicated feature. This helps with early issue detection and resolution.

In other words, agile development allows iterative development, frequent testing, and constant feedback. All of this results in reducing the rework and unnecessary features. This approach enables the team to evolve with the changing business and audience needs, stay focused, and within budget from start to finish.

#4 Develop an MVP

An MVP (Minimum Viable Product) is the most basic version of your AI application, built with just the core features. It’s part of an app prototyping, which allows you to test your concept in public without committing to full-scale development from the start.

By launching an MVP, you can gather real user feedback, assess app performance, and make data-driven decisions for future app development. This helps prioritize essential features, avoid unnecessary costs, and optimize overall AI app development costs.

#5 Maintain Transparency

Ensure clear communication in the project between the developers, stakeholders, and clients to set clear intentions from the start and avoid uncontrolled scope extensions. By maintaining transparent processes, you can ensure smoother workflows and reduce development costs. 

#6 Choosing the Right AI App Development Partner

Here are some of the simple considerations for choosing the right AI app development partner.

Experience in AI-specific Projects: Go with an AI development service provider with expertise in building AI-based solutions. 

Work Portfolio: Check their portfolio to know the different projects they have worked on. It helps you access technical expertise, design quality, and the ability to deliver AI solutions.

Client Reviews: Check client reviews on different platforms, such as GoodFirms, Clutch, etc. It helps to assess reliability, client satisfaction, and project outcomes.

View Team Composition: Ensure the team comprises AI engineers, data scientists, UI/UX designers, and other domain experts as needed.

Communication & collaboration: Go with a partner that maintains a transparent and seamless communication and keeps you in the loop from start to finish. 

Flexible Engagement Models: Opt for a company that offers flexible engagement options, such as fixed price, hourly, or dedicated team. Flexibility lets you effectively adjust budget and scope.

Post-Deployment Support and Maintenance: Ensure that the partner delivers regular support, performance monitoring, and bug fixing even after the app goes live.

Security & Privacy in AI Models: Check whether the company implements robust security measures, handles data encryption, and complies with regional data privacy regulations.

Navigating the Future of AI App Development Costs

In this blog, we have examined everything related to AI app development costs, from what factors influence them to industry-specific variations, hidden expenses that you should be aware of, and the best strategies to optimize the cost of building an AI application. By understanding and implementing these things in the best possible way, businesses can make effective decisions, invest wisely, and achieve their desired goals.

If you are planning to develop an AI application for your business, we are there for you. Openxcell provides the best AI app development services to businesses, regardless of the industry or the size of the business. Our AI experts utilize the best AI tools, technologies, and frameworks to craft an AI solution that fulfills the needs of your audience and drives business growth.

Frequently Asked Questions on AI App Development Costs

Is developing AI expensive?

AI development can be costly, with basic solutions starting from $10,000 and going up to $2,000,000 and more. The cost of the AI application depends on various complexity, data requirements, and platforms. However, by carefully planning and implementing optimization strategies, you can reduce costs.

What are the benefits of AI apps?

AI apps provide automation, boost productivity, enhance decision-making, improve customer experiences, and allow businesses to stay competitive in the growing market. 

How much does the average AI development cost?

On average, AI app development costs can range from $20,000 to $2,00,000. The cost usually depends on the scope and complexity, features and functionalities, and overall infrastructure needs.

How much does it cost to build a ChatGPT app?

Building an app similar to ChatGPT can cost $50,000 to $500,000+, based heavily on the AI model used, data integration, features, scalability, and maintenance required.

How long does it take to build an AI App?

The time required to build an AI app can range from 3 months to a year. It primarily depends on the application’s scope and complexity. However, other factors like features and functionality, integrations, and more also play a vital role.