With the proliferation of AI technology, businesses are eager to adopt and leverage the power of AI. But the challenge for many businesses is that AI projects aren’t like regular IT or software development projects - they are often more experimental and complex. In fact, AI projects are so challenging that 85% of them fail to deliver on their intended promises to the business.
In this AI development cost analysis guide, I'll help you overcome these challenges by sharing the top 8 factors that have impacted my own clients' AI project costs. By understanding these factors, you can more accurately estimate your own AI software development costs with greater confidence.
Why is estimating the cost of AI solution important?
Over the last few years, AI has been all over the news, and businesses in all industries have been excitedly embracing this emerging technology. But the reality is just that - it’s still an emerging technology. There’s limited data available to help businesses plan and budget for artificial intelligence and machine learning costs. But that doesn’t make it any less important.
It may take some work up front, but estimating your artificial intelligence costs upfront can help you to:
- Avoid unexpected expenses, which could halt or delay the project.
- Ensure that there are sufficient resources to support the project throughout its lifecycle.
- More accurately assess the potential ROI of the project and determine whether the benefits of using AI outweigh the costs.
- Make informed decisions about which artificial intelligence project to pursue and which ones to defer or abandon.
- Optimize resource allocation by identifying areas where costs can be reduced or efficiency could be improved.
- Build trust and transparency with stakeholders by demonstrating a clear understanding of the project's costs and benefits.
AI development costs in 2024: What to expect
The global AI market is expected to reach a massive $301B by 2028, growing at a rate of 29.4% CAGR. While we’re seeing massive growth in the AI market, It’s important to keep in mind that this industry is still very young. The technology is moving so quickly that off-the-shelf software pricing and AI development and consulting costs haven’t leveled out. Furthermore, there are significant disparities between the preparedness of industries to embrace and integrate AI into their businesses, leading to a wide range of implementation costs.
So, what does this mean for you? What costs can you expect in 2024?
AI costs are all over the map. Some startups are using free third-party tools and spending close to $0 integrating AI into their workflows, while some businesses are building custom AI systems for $50k - bare minimum, with expensive consulting costs often over and above that.
For a very-high level estimate, we can break down AI development costs into three categories:
| AI Type | Initial Development Costs | Monthly Maintenance Costs |
|---|---|---|
| Basic AI solution | $0 - $20,000 | $0 - $1,000 |
| Intermediate AI app | $20,000 - $50,000 | $1,000 - $5,000 |
| Enterprise AI systems | $50,000 - $200,000+ | $5,000 - $20,000 |
8 Factors to assess AI development costs
As we’ve seen, AI costs can vary wildly. This wide range of costs, along with the relative immaturity of the industry, makes it particularly difficult for businesses to estimate their AI project costs.
In this section we’ll review the 8 factors that will affect your artificial intelligence cost most dramatically. Use this section as a guide to better estimate your own AI project costs.
Type of AI solution
AI development can encompass anything from simply setting up a free, open-source AI chat tool to training AI algorithms or models from scratch along with custom AI development. The type of AI product you intend to build, therefore, has massive implications on the cost. When we’re talk about AI solutions, we can roughly break it down into four categories:
- Free, third party AI software: ChatGPT has gained massive news coverage lately for providing free access to their advanced AI chat bot. Other companies have followed suit offering totally free AI tools to the public. Using tools like this can be a great free (or ultra low-cost) way to dip your toes into AI and get a better understanding of the use cases that would be appropriate for your business.The costs you’ll see are the people-resources spent learning and working with the tool.
- Subscription, third party AI software: There are thousands of AI tools that you can purchase access to. While you can certainly integrate them into your own tech stack or even use APIs to extend their functionality, they are more often intended to work out of the box, without any development or custom integration work required. The costs for these tools can vary quite dramatically, but you’re likely looking at something between $10 and $3000+ per month.
- Customized AI solutions: More advanced AI solutions, especially those designed for enterprise applications, allow teams to use them as part of a larger, custom AI solution. Typically this means you’re saving on development costs because you’re not building a full AI platform from scratch, and you have more flexibility and control than you would simply buying and using something off-the-shelf. Costs for this include the software itself plus development and/or consulting work to help you integrate the tool into a new or existing tech stack.
- Fully custom AI solutions: The most expensive type of AI product is a fully custom solution built to your unique specifications. This type of business-specific solution is often referred to as narrow artificial intelligence, meaning that it is built for a narrow, very specific business purpose. This may or may not involve gathering your own training data and building a model or AI algorithms from scratch.
PRO TIP: It’s not always obvious which path you should take. Each option has their own set of constraints and tradeoffs:
- If you are looking to minimize costs, you’ll need to work with existing, off-the-shelf AI tools, which will not be as secure as a fully-custom solution.
- If you’re looking to prioritize security, your costs and implementation time may be higher as you’ll need to build a custom solution that doesn’t expose your data.
- If you’re looking to minimize implementation time, you’ll likely need to work with existing AI tools, which may not be as secure or you’ll need to work with high-cost AI experts to help optimize AI development and deployment.
There are always going to be tradeoffs with a development project. It’s important to sit down with your stakeholders and assess your current business priorities to understand which path may be right for you.
Read More: AI in Business - 5 Proven Strategies to Get Started
Complexity of the project
The complexity of an AI project can impact its cost in various ways. More complex projects may require more data, more computing power, more sub-projects, more hours from an AI development team, and more expertise, which can all contribute to higher costs. Let’s look at a few of the key factors that contribute to a project’s complexity:
- Amount and quality of data required: Managing large amounts of data can be costly and time-consuming. The current state of your data often determines your “readiness” for AI. If your data is readily available, as it might be in a mature IT organization, the complexity of the project will be lower. If, on the other hand, you’re dealing with a high-volume of unstructured data from many different sources, the complexity of your project will be higher.
- Structure of the AI solution: How you structure your AI solution can have a significant impact on its complexity. Many teams are starting to break tasks up into sub-tasks, with each sub-task being processed using AI, and then fed into another AI or automation tool. If you’re creating an AI tool to complete a narrow, single task, the complexity will be far lower than if you’re building a network of artificial intelligence projects that feed into a larger, AI platform.
- Complexity of the algorithms used: Developing and fine-tuning complex algorithms is a time-consuming process that requires machine learning engineers and data scientists. The complexity of algorithms can also impact the computational resources needed to train and run AI models, making infrastructure management more challenging.
- User experience required: Developing a user-facing application adds complexity to the project. Additional design and testing efforts are required to ensure an intuitive user interface, accessibility, and regulatory compliance. Furthermore, third-party tools may need to be added to the tech stack to effectively provide a human-interface element, as developing AI-human interfaces to process and analyze AI outputs can be very complex and expensive.
- Level of integration required with other systems or platforms: Integrating with other systems or platforms can add complexity to an AI project. Think about how many tools you intend to link together and how that might impact the level of testing and development efforts to successfully ensure compatibility, interoperability, and security.
PRO TIP: Complexity impacts not only early exploratory work and AI development costs, but also, deployment. If you’re dealing with a complex AI model, you’ll need to have a full team of experts to help you achieve full scale deployment into your business environment. This process is referred to as “ML Ops” (a combination of ML and DevOps).
Depending on the exact complexity you’re dealing with, this process could take days or weeks and require the expertise of data scientists, business analysts, data engineers, AI engineers, risk managers, machine learning engineers, and IT operations managers, among others.
Read More:
MLOps: Continuous delivery and automation pipelines in ML / Vertex AI
MLOps: Model management, deployment, and monitoring with Azure ML
Expertise required
The level of expertise required for an AI project can impact the cost of AI, as AI consultants and other AI experts must have a deep understanding of both the available AI tools and the business processes involved to deliver an effective AI solution. This level of expertise is difficult to find and doesn’t come cheap.
Key expertise you may require includes:
- Expertise in data science or AI development: More complex projects or projects requiring specialized AI knowledge may require more experienced data scientists or AI developers, which can increase costs. Teams will need to look for developers who are specifically skilled in sourcing, evaluating, and integrating AI solutions available on the market today.
- Domain expertise in a specific industry or field: Certain industries may have specific regulatory or compliance requirements that need to be considered when developing an AI solution. Additionally, developing an effective AI system often requires a deep understanding of the business processes and workflows that the solution is intended to improve. For some industries, it may be a challenge to find skilled and experienced experts, further driving up the cost.
- Experience with specific technologies or platforms: Certain technologies or platforms may require a high level of expertise to use effectively. For example, deep learning algorithms, natural language processing tools, or specific cloud computing platforms may require specialized skills to develop and implement. Hiring or training staff with the necessary skills can be expensive, driving up the cost of AI implementation.
Key questions to ask to assess costs:
- Do we have the necessary expertise in-house to develop and implement this AI solution, or will we need to hire an AI app development company?
- Does this project require specialized AI knowledge or expertise, and if so, do we have staff with the necessary skills and experience to manage the project effectively?
- Does this project require domain expertise in a specific industry or field, and if so, do we have staff with the necessary knowledge and experience to understand the relevant processes and workflows?
- Are there any specific technologies or platforms that we need to use to develop and implement this AI solution, and if so, do we have staff with the necessary expertise to use these technologies effectively?
- Have we considered the impact that the level of expertise required will have on the overall cost and timeline of the project?
Data availability & quality
The availability and quality of data can significantly impact the cost of an AI project. One piece of this is the scale of the data that you need to collect. Data collection from many different sources, especially high volumes of this data, could require hiring a data scraper or utilizing significant internal resources to collect and organize the data.
The other piece of this is the quality of the data you have available. This is a bit subjective, but generally, quality data is assumed to be clean, complete, calculable, credible, comprehensive, and specifically relevant (chosen) for the application at hand. Projects with poor quality data may require additional resources to clean or preprocess the data, which can increase costs.
It is crucial to perform data quality checks throughout the AI project - during implementation and maintenance phases - to ensure that the data is of an acceptable quality. But you should balance the scope of the data quality oversight with what’s reasonable to ask of your data engineers. Too much work monitoring data can overload the data team, lead to false positives or overarching data quality nightmares.
To assess data availability, consider:
- Is the required data readily available?
- Is additional data needed, and what is the cost associated with obtaining it?
- Is the data structured or unstructured, and will additional data cleaning or labeling be required?
To assess data quality, consider:
- Is the available data accurate and reliable? What data analysis is required to vet this?
- Will additional data cleaning or preprocessing be necessary to ensure data quality?
- What is the cost associated with data cleaning or preprocessing if needed?
- Will additional resources, such as data analysts or subject matter experts, be necessary to ensure data quality?
PRO TIP: Use a tool like DeCube to help your data team more efficiently monitor and maintain your data sets during ML algorithm implementation and maintenance. Tools like this can take the burden off data and help you:
- Boost productivity for data engineers, as they can focus on more strategic tasks
- Reduce data teams’ debugging time by 45%
- Reduce the cost of managing data quality via open-source or manual scripts by 60%
Read More: Automating Data Pipelines — Types, Use Cases, Best Practices
Hardware and software infrastructure
The hardware and software infrastructure required for an AI project can have a significant impact on its cost. Here are some specific factors to consider when assessing the impact of infrastructure on the cost of an AI project:
- API calls: Many third-party tools, such as IBM Watson, Google Cloud AI, and OpenAI’s API, charge based on the number of requests made. Higher request volumes lead to higher costs, which can become significant if your application relies heavily on external APIs.
- SaaS subscription costs: Leveraging a third party AI tool can save on custom development costs, but you’ll still need to factor in monthly or annual subscription costs, typically based on number of users and/or usage.
- Licensing fees: Some AI development tools and frameworks come with licensing fees. Popular frameworks like TensorFlow, PyTorch, and others might be free to use, but certain enterprise features or add-ons could incur additional costs.
- Hardware: Typically the cost implications on the hardware side don’t come into play unless you’re training your own AI model, in which case the costs are very high for sufficient GPUs and CPUs.
Key questions to ask to assess costs:
- What AI development tools and frameworks are we planning to use? Are there licensing fees required?
- How many API requests do we anticipate on a daily, monthly, or yearly basis?
- What is the volume of data that will need to be stored and processed? Will additional storage or processing power be required beyond what is currently available?
- Will a minimum viable product (MVP) be built first? If so, will there be any changes to hardware or software requirements after the minimum viable product?
Front-end requirements
When it comes to AI projects, it's crucial not to overlook the human element. How will teams interface with the AI tool? Will they rely on a UI to process and analyze data that the AI tool outputs? If so, they'll need to consider the cost of UI development when estimating project costs. Here are some key factors to consider:
- UI design: A well-designed UI is critical to the success of an AI project. The UI should be intuitive and easy to use, allowing teams to process and analyze data efficiently. UI design process can be expensive, especially if the UI is being built from scratch and if it requires many screens.
- UI features: Teams should consider the features they need in their UI, such as data validation, data processing, and data labeling. Think about how the user will interact with the AI tool and what data they’ll need to see, and in what format. This can provide a guide for the level of complexity of the UI - and whether buying a tool off the shelf that provides these features would be worth it or not.
- Data integration: The UI should be designed to integrate seamlessly with the data sources and processing pipelines used in the AI project. This can increase UI development costs, especially if complex data integration is required.
- User testing: It's essential to test the UI with real users to ensure that it meets their needs and is easy to use. User testing can be time-consuming and expensive, but it's critical to the success of the AI project.
Key questions to ask to assess costs:
- How will the user interface with the AI tool?
- What data will the user need to see and in what format?
- Will additional resources be required for user interface design and development?
- Will a front-end be built or will a third-party tool be used to meet the requirements?
- What resources will be required to implement and test the front-end?
Data storage
The more data you need to store, the higher the costs. Businesses need to evaluate the amount of data required for their project and ensure that they have adequate storage capacity available as well as the budget available to purchase sufficient storage. Here are few examples of costs you should consider:
- Cloud storage costs: Cloud storage is a popular option for businesses looking to store and manage data for their AI projects. Cloud storage providers charge based on the amount of data stored and the amount of data transferred, with costs ranging from a few cents to several dollars per Gb per month.
- Data center costs: Large-scale AI projects may require dedicated data center infrastructure for data storage. Costs for data center infrastructure can range from thousands to millions of dollars depending on the size and complexity of the project. In addition to the initial costs of setting up data center infrastructure, businesses also need to consider ongoing maintenance and management costs for data center infrastructure.
- Backup and archival costs: Storing data for long periods of time or archiving data for compliance or regulatory purposes can increase costs. Most businesses will need to invest in hardware and software for backups and archival, as well as ongoing management and maintenance costs.
Key questions to ask to assess costs:
- How much data will the artificial intelligence project generate, and what is the estimated cost for storing and processing this data?
- Are there specific requirements for where or how the data is stored?
- Do backup and archival costs for the AI system need to be considered?
- What are the requirements for ongoing maintenance of the data?
- As the project scales, has the potential for data growth and additional storage and processing requirements been considered?
Regulatory & compliance requirements
Businesses need to carefully evaluate the specific regulatory and compliance requirements that apply to their project and factor in the additional costs associated with meeting these requirements. Failure to comply with regulatory and compliance requirements can result in significant financial penalties and legal consequences, so businesses must ensure that they are fully compliant with all relevant requirements.
Here are some specific factors to consider when assessing the impact of regulatory and compliance requirements on the cost of an AI project:
- Industry and jurisdiction-specific regulatory and compliance requirements
- Development process modifications to meet regulatory requirements
- Additional costs associated with data storage and security measures
- Potential financial penalties and legal consequences for non-compliance
Key questions to ask to assess costs:
- What specific regulatory and compliance requirements apply to the AI project, and what are the potential financial penalties and legal consequences for non-compliance?
- What modifications or additional measures are needed to implement in our development process to meet regulatory requirements?
- How much will it cost to store and secure the data required for our AI project to meet regulatory and compliance requirements?
- Are there any industry-specific or jurisdiction-specific regulatory and compliance requirements that we need to consider for our AI project?
- Have we factored in potential cost increases associated with changes to regulatory and compliance requirements as our AI project evolves or expands?
Strategies for reducing Artificial Intelligence costs
After reviewing the different factors that can impact the cost of AI projects, you may be looking for ways to reduce your overall costs. Reducing costs without sacrificing business impact requires careful planning. Here are a few tips to get you started:
- Narrow in on a clear objective: It might feel obvious, but this is crucial to avoid scope creep and reduce the likelihood of additional software costs associated with changing requirements mid-project. Document your objectives and project parameters in writing, and share these with all stakeholders to avoid any misunderstandings.
- Work with the experienced AI engineers: Working with experienced AI team can help businesses avoid costly mistakes and ensure that the project is completed on time and within budget. AI software developers can provide guidance on the most cost-effective solutions and help businesses avoid potential pitfalls that can lead to higher costs.
- Utilize open source AI solutions: Utilizing open source software can help businesses reduce costs associated with licensing fees and software development. Open source tools can provide businesses with a low-cost way to test the waters of AI without committing to building an AI model from the ground up.
- Focus on data quality and accuracy: The accuracy and quality of data used in an AI project can have a significant impact on the project's effectiveness and AI costs. To achieve better results, take the time to thoroughly pre-process your data sets by:
- Ensuring that data is clean, accurate, and reliable before using it in an AI project.
- Using automated labeling tools to improve accuracy and reduce costs.
- Removing data inconsistencies to improve the reliability of the system.
- Establishing quality assurance processes to ensure that the AI model performs as expected.
Conclusion
Estimating the cost of artificial intelligence is not a simple process. There are many factors that can affect the total project cost - from data quality to software requirements and other overhead costs like data storage. If you’re looking to kick off a new AI project, take the time to thoroughly assess and document your project requirements, and use this guide to better anticipate your own project costs and avoid surprises during AI experimentation, development, and deployment.
If you're looking for help developing an AI application, consider our AI development services. Our team can help you put together a comprehensive AI strategy and guide you through the development process. With our software development consulting services, you can leverage the power of AI models to build a market-ready product that stands out from the competition.
