The Ultimate Guide to Custom AI Software Development Costs: What to Consider Before Building

Artificial intelligence is no longer just an experimental indulgence that only major technology companies can afford—it has become a necessity to compete. 

Whether it be process automation, customer behavior prediction, or transformation of the whole product line, failing to integrate AI into your business will only give the market share to your competitors. Despite being aware of it, many executives are stuck because of one reason only—the fear of cost uncertainty.

If you’ve ever asked a development agency for a machine learning cost estimate, you’ve likely felt the frustration of hearing the classic response: 'It depends.'

Seems like an escape to you, right? there is something that most of such agencies prefer not to tell their clients. 

Even though the prices on artificial intelligence projects are variable, it does not mean that the figures appear randomly. In other words, AI software pricing is fully predictable provided that you know which variables matter.

Once you are familiar with the fundamental aspects behind the development price of custom AI software development solutions, you will be able to calculate all expenses correctly, eliminate possible surcharges, and convert your idea into a profitable and efficient solution. 

Let's uncover the truth behind the scenes in order for us to see what your money pays for and how to handle your finances effectively.

Why Is It So Hard to Price Artificial Intelligence?

Before discussing in detail the line items of the cost structure of an AI project, one question needs to be tackled – what is so different about the pricing of artificial intelligence compared to a regular mobile application or website development?

The answer to this lies in the scale of complexity. If you compare, for example, a simple customer support chatbot, which provides automated answers to the most frequent questions, to an advanced machine learning algorithm, which predicts the future of a global supply chain, it can be compared to riding a bicycle and a space shuttle respectively. 

Even though these are both means of transportation, their complexity is incomparable. It depends on the complexity of the problem you are facing, how much an AI-powered solution will cost you.

One has to realize the huge difference between generic and custom-developed software. While general-purpose APIs are easy to integrate and carry predictable monthly fees, they are inflexible, generic, and available to your competitors 

Custom-developed AI software development will cost you more upfront, but this is what you have to consider: the custom-developed AI model will belong solely to you; it will be trained on your specific data, thus giving you a great competitive advantage.

What should one remember about this topic? The key thing is not to ask a developer for a quote without knowing the problem to be solved first.

7 Key Factors That Drive Custom AI Software Development Costs

When getting a proposal for your custom-built AI, you do not only pay for writing code. Instead, you pay for a process that involves multiple disciplines. These are the seven basic components which form the majority of the expenses for your AI project.

1. Data Collection, Cleaning, and Preparation

The absolute reality of machine learning is that your AI's predictions are only as good as the data it’s fed. No matter how sophisticated your algorithm is, feeding it bad data is simply going to produce poor results.

That is why data engineering takes up to 50% of the total time and resources spent on the whole project. For any model to start being trained, you need to prepare your data. 

Extracting historical data from legacy systems is extremely difficult work. Should the data within your company be in poor condition—containing duplicate rows, missing values, or formatting problems—scientists will have to write scripts for cleaning it up manually. 

If you are working on supervised machine learning algorithms, then your data should be labeled. Should your data be not abundant enough, you will have to purchase third-party data to complement your own.

2. Algorithm Complexity and Model Training

Not all AI is created equal. Your needs will dictate what kind of model you need. And depending on which one it is, you may end up spending much less or much more on your machine learning project.

If you need a tool that will predict next quarter's sales based on past information, you could ask a developer to create a machine learning algorithm that uses linear regression. 

The process will be relatively simple and inexpensive to implement. But if you're an automobile manufacturing plant that wants to use computer vision cameras on the production line to detect defects of your product, you need to use deep neural networks.

Deep learning is a highly complicated process of designing mathematical architecture. Even more crucially, the training of the algorithm is done in a cycle of trial and error. 

You have to adjust parameters, run the model, measure its accuracy, and run it again and again until it achieves the desired level of accuracy. And since you pay for the highly-specialized skills of data scientists, this part is particularly expensive.

3. Cloud Computing Infrastructure and GPU Power

Below is one reality that usually takes most corporate leaders by surprise: the computational power required for artificial intelligence is absolutely massive. There is no way you can train your complex neural network using a regular office computer.

To process millions of data points and perform complex mathematical operations at the same time, developers need serious cloud computing power in the form of GPUs. It does not matter whether you use Amazon Web Services, Google Cloud, or Microsoft Azure; you will definitely incur huge cloud computing expenses while training your model.

There is more than training to take into account. When your AI is deployed in the inference phase, it uses the computational power every single time a user makes a query. Being able to accurately calculate your server expenses associated with running AI is crucial in the process of AI software development pricing.

4. Intuitive UI/UX Design for AI Applications

It doesn’t matter how intelligent and predictive your AI engine is, how seamless its background operation. It would be useless for you if your team members cannot use it efficiently.

User interface and user experience designs are often underestimated in calculating the cost of custom artificial intelligence software development. Artificial intelligence software is supposed to enhance human decisions, which means people using it should know what information they are being provided with.

It takes a lot of design expertise to develop custom dashboards displaying complex algorithmic calculations in comprehensible chart form. 

In case you develop an artificial intelligence chatbot or virtual assistant, developing conversational user flows will require a special type of UX copywriting and design expertise. In order to save money on the design, you may sacrifice user adoption rates, which will ruin all of your efforts eventually.

5. Seamless Integration with Existing Legacy Systems

Your new artificial intelligence can’t survive in isolation. In order to provide real value to you, it must connect with all the other software that is running in your business every single day.

It may be your customer relationship management software, enterprise resource planning software or even your marketing automation software. Your new AI will need to integrate into each of these systems to get information flowing in and out smoothly. This process poses a huge technical challenge.

The integration costs will skyrocket for any company running an old-school legacy software package that is not designed to connect with newer cloud-based applications. Developers have to come up with very elaborate ways to make this happen. Make sure to budget for your environment when building a budget for your AI.

6. Ongoing Maintenance and Combating "Model Drift"

The idea that the product is finally complete when the software gets released is the most hazardous misconception in the world of software development. The truth is that software is never complete, especially AI software.

In contrast to typical software, which performs exactly what it was programmed for eternally, the AI software interacts with the constantly changing reality. As a result, there appears a concept of "model drift". 

When consumer behavior changes, market dynamics shift or new competition arises, the actual world begins to differ from the past one where the AI was trained. Eventually, the AI software loses its accuracy.

To prevent model drift, one has to take into account the fact that there will be additional expenses related to algorithm monitoring, providing it with updated information and training it to maintain its accuracy. Otherwise, within one year, AI will turn into obsolete technology.

7. The Pedigree of Your AI Development Team

Lastly, the people making your software will be your largest variable expense. The global demand for skilled professionals in AI currently far outstrips supply. Machine Learning engineers, data scientists and AI architects that can actually do the job are in very high demand and very expensive to hire.

By attempting to build an in-house AI team from scratch, you are incurring significant overhead costs. These costs include hiring and recruiting fees, very high six-figure salaries, benefits packages, and a very high cost of employee attrition.

That is exactly the reason why it is smarter for businesses to outsource their development needs to experts in this field. 

The overhead cost incurred by the company becomes predictable and milestone-driven due to outsourcing. When outsourcing to companies like Gigmint.AI, there is no need to go through the process of hiring employees.

You instantly gain access to a pre-formed team of highly experienced and capable developers that can help you make sure that your budget goes directly into the product and not on your HR expenses.

Hidden AI Development Costs Most Businesses Miss

Though you have an excellent understanding of the basic development stages, there will always be some unexpected costs that may shock the business owner. For the creation of a bulletproof budget, you need to take care of those unexpected expenses.

Regulatory Compliance and Data Privacy Security

If your AI needs to deal with any sensitive information—such as emails from users, their financial information, and even their medical history—regulatory compliance issues will become a real pain. The development of such systems in order to comply with heavy regulations such as GDPR in Europe and HIPAA in America, will require a lot of engineering work.

It is simply not possible for you to create a privacy policy for your organization and assume it will suffice. 

The engineers will need to create powerful data anonymization systems, sophisticated role-based access controls, and military grade encryption for all your data. 

Before you release your application you will have to spend thousands of dollars on third-party security audits and penetration tests to ensure that your system is unbreakable for anyone. And although it will cost a fortune, the penalties for data breach will be disastrous.

Third-Party API Dependencies and Tooling

In order to fast track the development process, engineers will usually resort to outside paid APIs or third party tooling that is only required for non-essential functionality of the application. 

Rather than designing and building a proprietary speech-to-text functionality from scratch, they can simply integrate one provided by Google or OpenAI as an API.

Although this greatly reduces the time needed to develop the app initially, there is an indirect but constant cost associated with this. Such third-party services are priced based on consumption. 

If the app starts scaling up unexpectedly well, your API fees will start skyrocketing along with it. You have to factor this cost into your budgeting process at varying levels.

How to Accurately Estimate Your AI Project Budget

Since you know what the money is for, now, how do you go about getting a reasonable number? You defend your budget with your choice of strategy at the onset of the construction process.

Why You Must Start With a Proof of Concept (PoC)

There is just one mistake that you can make, which is giving away a large amount of money on a full-scale enterprise AI from day one. You need to start off with the Proof of Concept (PoC).

Imagine that the PoC is your safety buffer of finances. The PoC is basically the little model of the AI that is used to validate your hypothesis with a sample of your data set to see whether it works well in solving your specific problem with sufficient accuracy. 

By doing PoC you will be validating your hypothesis with the smallest part of your budget. If PoC fails, then you switch course without losing all of your money. If the PoC succeeds, then you have an accurate estimate of your full-scale project.

Defining Rigid Business Objectives to Prevent Scope Creep

"Scope Creep" is the unseen killer of the AI project budget. This occurs when stakeholders keep requesting "one more neat feature" throughout the development cycle, extending the project schedule and increasing costs exponentially.

In order to avoid scope creep, you have to set rigid and inflexible business goals from the very beginning before writing even one line of code. Each and every feature request has to be tied up to a certain, measurable metric. 

When the main goal is stated as "to decrease customer support ticket resolution time by 20%," each and every feature has to work towards this goal. In case somebody proposes a sophisticated predictive text feature, which doesn't affect the resolution time, you can use the metric to say no to the proposal.

Strategies to Reduce Costs Without Sacrificing Quality

You need to create a world-class product with AI technology, but you need to protect your runway as well. With today’s development process, there are many great options available to optimize your custom AI software development costs.

Utilizing Pre-Trained Foundation Models

Several years ago, when creating AI, it was required to train a model from scratch. This has become rather unusual now. Effective development teams use pre-trained foundation models.

The reason for this is a concept known as "transfer learning." It involves using not a blank slate but an enormous open source model, which has been trained beforehand on billions of parameters to comprehend the basics (for example, human languages or basic image identification). 

You fine-tune this model by giving it your proprietary data. As a result, the AI will be able to learn your business logic without any basic training at all, saving you many costly computing hours.

Partnering with Specialized AI Agencies

As was stated above, the staffing of your project will be your main financial resource. The choice of outsourcing the development of artificial intelligence to the elite partner is financially sound.

Partnering with a specialized agency like Gigmint.AI offers more than just access to top-tier coders; it provides immediate speed and efficiency 

There will be no expenses associated with the recruitment process. It will give you immediate access to the frameworks and boilerplates that have been already created by the agency. 

Is the Cost of AI Software Development Worth the ROI?

Viewing the cost involved in creating custom artificial intelligence solutions for your company as simply an expense fails to capture the true gravity of the matter. 

Creating custom, efficient, intelligent, and secure software which has been seamlessly integrated into your processes will be the way of guaranteeing your company’s survival and scalability in the future.

Developing a custom AI solution will cost you quite a lot of money. When a system built in accordance with these requirements saves your company from spending a total of $300,000 annually and, at the same time, enables additional profit, such a solution cannot be considered a business expense anymore. This is what will guarantee its survival and success in the long run.

Stop guessing how it works and develop the right software. Schedule a free consultation with our team here at Gigmint.AI.