Table of Contents
AI can be used as an important part of daily work in practically every industry. Companies have recognized that AI has enormous advantages. Therefore, they want to develop AI applications, but are concerned about the costs. As with any other application, the cost of developing an AI application depends on various factors, such as how complex it is.
To give you an approximate figure: The development of an AI app costs between 30,000 and 300,000 euros.
Several cost-related misconceptions need to be dispelled. This article examines the costs of AI development and the many factors that influence them.
What you will learn from this article?
At the end of this article you will:
- Understand the key factors that influence the cost of developing AI applications
- Find out how the choice of functions affects the overall budget
- Discover the cost implications of platform selection
- Gain insight into the cost of hiring AI talent
- Understand the importance of MVP for controlling development costs
- Compare the options for in-house and outsourced development
Functions of the AI app
The cost of your AI app is significantly influenced by what you want to achieve with the solution. Think of the functions like items on a menu - each additional function increases the final bill.
An example from practice: IBM Watson Assistant offers different price levels depending on the functionality. Basic chatbot functionality starts at around $140 per month, but when Vodafone implemented Watson with CRM integration, the company reported implementation costs of over $85,000, according to the Digital Transformation 2022 report.
The more complex your AI requirements are, the more specialized the development needs to be. Simple rule-based chatbots can cost 10,000 to 15,000 euros, while advanced machine learning applications can cost over 100,000 euros. AI is an investment. If you want accurate and fast data analysis, you need an AI platform that your users trust. Quality comes at a price, but usually delivers better results in the long run.
Choice of platform
The underlying app platform is one of the key elements that affect the cost of creating an AI app. Despite small cost differences between the creation of Android and iOS apps, it is often more cost-effective to start with one platform.
An example from practice: Duolingo initially launched its AI language learning app for iOS only. According to their tech blog, this approach saved around 40 % of the initial development costs compared to developing for multiple platforms at the same time.
Android has a larger global user base that can provide more data for your AI to learn from. Later, you can invest in a cross-platform solution as your application gains traction.
Platform decisions also have an impact on running costs. Cloud-based AI platforms such as Google Cloud or Microsoft Azure have different pricing structures. Microsoft has reported that companies switching from on-premise AI solutions to Azure AI services save an average of 30-35 % on infrastructure costs.
Salary of an AI engineer
According to Indeed, the average salary for a machine learning engineer in the US is $142,859 per year. Other AI job titles earn more than $100,000 each year. Algorithm Engineers earn $109,313 and Data Scientists earn an average of $126,927 per year.
An example from practice: Netflix's AI team, which is responsible for the company's recommendation engine, costs the company an estimated 1.5 billion dollars annually. Netflix's 2022 shareholder report shows that this investment creates approximately $1 billion in value through reduced subscriber churn.
It is clear that the cost of hiring AI engineers is very high compared to other software developers. These figures also show a clear trend in the demand for AI engineers today. The location of your development team also has an impact on costs. AI engineers in Silicon Valley command top salaries, but you can find qualified talent at lower rates in emerging tech centers like Austin, Texas, or internationally in countries like India or Ukraine.
Computing power
Running a trained AI model is usually not computationally intensive. But training it is a completely different story. To train neural networks effectively, the company developing AI should have access to enormous computing power.
An example from practice: OpenAI reports that training its GPT-3 model alone cost $4.6 million in computing power. While most enterprise AI applications don't require this scale, even mid-sized language models can cost $10,000 to $50,000 in computing resources to train properly.
This challenge is usually solved by running the calculations in the cloud, but this in turn incurs costs. Companies such as Google Cloud, AWS and Microsoft Azure offer specialized AI computational resources that can be scaled as needed. Tesla uses a combination of custom hardware and AWS services to train its AI for autonomous driving, with quarterly cloud computing costs reported to be around $30 million.
The type of AI you develop makes a big difference to data processing costs. Simple machine learning algorithms can be trained on standard hardware, while deep learning requires specialized GPU resources that are significantly more expensive.
MVP development
A model that performs a task is one thing - but a model that performs that task for the benefit of a company is quite another. Before a complete AI application is implemented, companies must develop a minimum viable product (MVP).
An example from practice: Airbnb developed an MVP of its AI for price optimization before full implementation. As the company writes in its technology blog, this made it possible to identify and fix several critical problems that would have cost an estimated 2.5 million US dollars if they had been discovered after full implementation. The MVP cost around 120,000 US dollars, but saved many times that amount.
An MVP shows that all the machine learning in the app is not just a gimmick, but has the potential to bring significant improvements. This is also a cost factor in the AI development process, but a necessary one.
Developing an AI app without an MVP is a clear path to disaster, as numerous defects and errors can occur during the app's development phase. The MVP helps to validate both the technical feasibility and marketability of your AI solution, potentially saving significant resources.
Outsourcing vs. internal development
A typical AI development process requires a multidisciplinary team of AI/ML engineers, data scientists, web developers and other IT experts such as designers, project managers, etc.
An example from practice: Spotify initially outsourced parts of the development of its recommendation engine to specialized AI companies before building its own team. According to the company's technology blog, this hybrid approach reduced initial development costs by around %, while allowing the company to gradually build up in-house expertise.
The salary and total cost of hiring internal IT talent and training them can be extremely high. Consider the following approximate annual costs for a basic AI development team in the US:
- AI/ML Engineer: $150,000
- Data scientist: $130,000
- Backend-Entwickler: $120.000
- Front-end developer: $100.000
- Project manager: $110.000
Outsourcing your AI app development can provide some cost relief as not all costs need to be borne by you. Companies like Deloitte and Accenture offer development services at prices that are often 30-50 % less than an in-house team, especially for projects with a defined scope.
Many companies opt for a hybrid model in which they keep the core AI expertise in-house and outsource the supporting development work.
Scalability and maintenance
The ML model is not like a refrigerator that works unchanged for years until it breaks down. The solution should be maintained and updated according to changes in your business model and environment.
An example from practice: Pinterest invests around 2 to 3 million US dollars annually in the maintenance of its recommendation AI, according to its 2022 Technology Investment Report. This ongoing investment helps the company maintain a 35 % lead in user engagement over competitors with less frequently updated systems.
A good example is image recognition software. Over time, the quality of images increases and users get access to better cameras. The system should be able to handle the larger images or receive an update that changes the size if it is not to become obsolete.
Maintenance costs typically amount to 15-25 % of the original development costs annually. These costs include:
- Retraining the model with new data
- Adaptation to changing user behavior
- Integration with new systems
- Optimization of performance
- Security updates
According to McKinsey, companies that allocate at least 20 % of their AI budget to maintenance and updates achieve an average of 35 % better performance of their AI systems over a three-year period.
Conclusion
Setting up an AI-supported solution is neither easy nor cheap. However, the savings and revenue it brings to businesses are often worth the investment. If you take a long-term view, the investment can pay off in the form of lower expenses and higher revenues.
For companies without existing AI infrastructure and specialized talent, working with an experienced app development agency like Appleute can significantly reduce both cost and risk. Outsourcing AI development eliminates the cost of hiring, training and setting up infrastructure, which can save you 40-60 % compared to building an in-house team from scratch.
When budgeting for AI development, you should consider the following:
- The complexity and number of AI functions required
- Your platform strategy (focus first, then expand)
- Talent costs and procurement options
- Requirements for the computer infrastructure
- How important it is to start with an MVP
- Ongoing maintenance costs
Specialized AI development agencies like Appleute already have established processes, experienced teams and a proven track record of implementation success. This means faster development, fewer costly mistakes and access to best practices without a steep learning curve.
Wondering how to develop a cost-effective AI app solution for your business? Appleute is the right solution for you. Contact us todayto discuss your requirements and learn how our expertise can help you navigate the complex world of AI development while optimizing your investments.
EN 
DE 
FR 
ES