How Much Does It Cost to Run AI Infrastructure?

The AI systems market is growing super-fast in the past few years. It’s expected that the market will soon reach $58 billion as more and more businesses and companies develop their own AI systems. But have you ever wondered about how much AI costs?

The answer to that question is not that easy. The price depends on the size of the company and many other factors as well. Most companies today are paying between $6,000 and $300,000 for AI software, depending on the solution itself. Stay with us, and we’ll tell you everything you need to know if you’re looking for an AI solution for your business. 

What Are The Main Cost Buckets?

When it comes to creating working AI solutions for companies, many different factors have to be considered to work out the software costs. There are two types of AI businesses can get. The first one is known as a custom AI solution that is specifically made for one company and its needs. 

Generally speaking, custom AI solutions are much more expensive as programmers and software experts have to create the entire system from scratch. The second type of AI is pre-made and can be used to run things, but it often doesn’t have all of the features you need. This type of software is much more affordable.

If you decide to develop your own AI solution, you can expect many challenges along the way. Many different factors affect the solution’s final cost, so you have to be very careful during the planning phase. All of the costs can be divided into data-related issues and performance-related issues. Here’s a quick overview of the costs you can expect.

Data-related Issues

Developing any AI solution starts with a reliable machine learning system. Creating the best machine learning system requires a lot more effort than just excellent coding. Making sure that the solution has access to high-quality training data is critical for success. With that said, here are some of the data-related considerations you must overcome to create a working AI solution.

  1. Dealing With The Lack Of Quality Data

Every ML solution needs access to datasets that allow it to pinpoint the relationship between the input and output features. The chances are that you won’t be able to generate all of the data needed to get things done, which means that you’ll have to turn to third-party data sources.

You need a large sample to make sure that the solution is based on high-quality information. One solution is to use data augmentation methods to increase the original sample, but that will negatively impact the quality of the data. 

  1. Complex Extract, Transform, and Load Procedures

The data you use to train your ML system has to be well-organized, stored, and formatted correctly to allow the system to make the right connections. That usually includes some form of data storage such as a database, warehouse, or a cloud. The key is that all information is stored in the same place. If that’s not the case, you will have to use other methods such as ETL processes to compile all of the data.

  1. Unstructured Data

The structure of the data use has a significant role in determining the overall costs. If the data used is structured correctly, the costs are lower. On the other hand, if you need to first invest in cleaning and structuring the data, the costs go up. Most AI solutions are built using unstructured or semi-structured data by using special ML-algorithms developed for that specific type of data. Naturally, that drastically increases the cost of the entire process.

Performance-related Issues

Ensuring that the algorithm’s performance is optimized correctly also impacts the costs of running an AI solution. Even the best algorithms go through multiple rounds of testing and fine-tuning. Here are some of the performance-related issues you can expect to run into.

  1. The Accuracy Rate

Your business objective and predictions have a massive impact on the performance rate. While a system that’s 60% accurate predictions on profits might seem good enough, it won’t be useful in some specific cases. If you’re looking for a system that can predict and effectively prevent lethal diseases, a 60% accuracy won’t be good enough. 

  1. Algorithm Processing Performance

Training a Machine Learning model usually takes a few tries before it’s able to provide high-quality results. The number of times it needs to be tested depends on the quality of data it uses and the features extracted by the algorithms. 

Complex data often requires more than simple model training. Algorithms can become a bottleneck during the feature extraction process. The issue can be eliminated by scaling up the processing power of algorithms on cloud-based servers. It’s also important to know that computing power also plays a significant role. If the data is complex, you will need high-performance computer systems in order to process the data.

Other Factors That Impact The Final Costs

Depending on your needs, you can expect other costs that include:

  • Chatbots 
  • Analysis systems
  • Virtual assistance
  • Project duration

How Should an Enterprise Think About ROI Against AI Investments?

Since developing an in-house AI system costs a lot of money, businesses need to think about their investment return. If the costs are higher than the estimated ROI, getting a third-party AI solution may be a better choice. 

However, AI became more popular during 2020 partly because of the massive push for full digitalization. AI made its way to multiple business applications, extending deeper than just upper management. AI today moved into operations with high expectations. 

Businesses are struggling to figure out the best ways to use AI to improve productiveness to a point where the ROI is higher than the cost. Many business owners turn to third-party AI solutions designed for specific industries. That drastically lowers the costs, but even so, these AI solutions can still be very expensive. Here are some of the most critical factors you should consider when investing in AI.

  1. Company CEOs expect ROI from AI project projections.

Businesses can now compare the ROI of existing business operations to those depending on AI. With the right AI, companies can now save costs and personnel-hours while improving their operations.

AI can now be leveraged to improve ROI across all business processes. AI can help enhance logistics, find better transportation routes, and even improve the manufacturing process and distribution of products.

  1. AI can help leverage ROI across all systems, people, and processes.

During the AI/ML testing phase, companies can use their systems to complete all APIs they come in contact with. The process usually involves the use of integration tools designed to simplify system interfaces until a new legacy system is set up.

  1. AI projections never stop.

AI solutions are made to replicate the human mind, but at much faster speeds. However, the efficiency of AI solutions largely depends on the algorithms and the data they are facing. 

That has pushed many companies into investing in well-structured data. Data cleaning and preparation is key for creating a highly-capable AI. Once the AI becomes a part of the company system, organizations have to see if it can achieve the projected ROI. Then they compare results with traditional methods such as hiring an expert.

Since AI solutions are never 100% accurate, most companies use a combination of expert guidance and AI when making the final decision. 

  1. The cost of infrastructure and accounting should be covered by the ROI.

Well-designed AI solutions can save a lot of time and reduce overall operating costs.  However, that doesn’t include the costs of obtaining expensive computing systems, data storage, and other investments needed to create a working AI solution. 

Other costs include restructuring business processes, integrating existing systems with new AI platforms, training employees to use the IT solutions, the consumption of energy, data center expenses, implementation costs, and so on. When the system is finally in place, businesses also have to consider ongoing support costs into the total ROI calculations. All of these expenses should be considered, and the ROI has to cover everything.

But that’s not all; the people responsible for AI projects should also be able to create ROI projections years in advance. These projections should include all costs for running AI/ML, including new software tools, hardware, clouds, training costs, and everything in between. 

The goal is to achieve an ROI that covers all costs initially and builds value through advanced business processes over time. 

  1. Factor in the positive ROIs delivered by the AI.

An AI solution means nothing without people with the right skills. That’s why you should factor in positive ROIs the AI can deliver. Invest in your employees, help them develop the skills and capabilities needed to complete AI projections, and the ROI should have a positive return in the long-run.


Most people think that implementing ML and AI technology costs a fortune. While that might be correct in some cases, with the right people, some careful planning, and the use of advanced software solutions, businesses can develop excellent AI solutions for a reasonable price. 

The final bill depends on the project’s complexity, customer requirements, and all other factors covered above.