7 Reasons Why AI Is So Expensive

A custom AI model is undoubtedly helpful for a business of any type. But creating and training one can cost upwards of 10 million dollars, so it’s only reserved for highly profitable companies. So, what are the reasons why AI is so expensive?

AI is expensive due to the significant computing power, electricity, and data needed to create and train an AI model. Additionally, companies are required to pay machine learning engineers a competitive salary to design, train, integrate, test, and fine-tune their AI models.

Let’s go over why building and implementing an AI tool can cost millions of dollars in more detail.

1. Research, Plan, and Design of the AI Model

The initial stage of creating an AI tool for any business is to first determine what it’s going to be used for. This often means months, or even years, of careful planning and research.

Companies typically employ AI consulting firms, which can charge hundreds of thousands of dollars. For instance, IBM offers various consulting packages that range from $65,000 to $500,000.

And remember, that’s just for consulting services to help you figure out how an AI tool could benefit your business. That includes gathering information, determining the scope and goals of the model, feasibility analysis, and more.

Once you figure out what the purpose of the AI is, it’s time to design it. The cheaper option is to use a pre-existing pre-trained third-party model like GPT and then customize it to fit your needs.

But for ultimate flexibility, you’d need a custom solution. The cost of building a model from the ground up varies significantly:

Simple AI models can cost $10,000 to $50,000.
Medium complexity AI costs from $50,000 to about $200,000.
Advanced complex models start at $200,000 and can cost up to several million dollars.

Remember, these are just rough estimates, as the cost varies significantly based on scope, complexity, customization needs, and many other factors.

2. Data Collection and Processing

One of the often-overlooked costs of creating an AI tool is collecting and labeling data. There are some free datasets available in the public domain, such as Kaggle, but the quality is rarely perfect.

To get the best quality data that was labeled by humans and is legally clear of any future copyright infringements, you have to pay.

As you might already know, AI needs an enormous amount of data to be accurate and good at its task. So, the costs vary significantly based on your satisfaction with the results.

For instance, DataSet Shop charges $25,000 for 500,000 images in FullHD resolution for AI training.

Moreover, depending on what industry a company is, there may not even be sufficient data to train the model. Some highly regulated industries like finance, healthcare, and law have plenty of data, but it’s often protected by privacy laws and NDAs.

The same is true for highly specialized industries that have unique requirements.

So, merely finding the right datasets can be a costly procedure, let alone paying for access.

If you also have to label the data in-house to improve the AI’s accuracy, the cost of labor can skyrocket the cost of data.

3. Hardware

Most computing for AI is done on GPUs, thanks to their excellent parallel computing power compared to CPUs.

And if you’re a gamer, you know that a single high-end gaming graphics card like the RTX 3090 Ti can set you back a whopping $2,000.

But that’s nothing compared to Nvidia’s $10,000 A100 chip that’s designed specifically with machine learning in mind.

Even a smaller business likely needs at least a few of these chips, depending on the computing power needed to train and run the model.

Naturally, the more chips a company has, the faster it can complete the training.

4. Training

Training is often the most expensive part of creating a custom AI tool.

To give you a rough estimate, CNBC estimates that Meta’s LLaMA model cost around $2.4 million to train, per Amazon Web Service’s pricing. ChatGPT’s training is estimated at around $4 million.

This is because AI tools often need a mix of supervised and unsupervised learning techniques to train.

In supervised learning, the model learns by processing labeled data and associates data points with their corresponding target outputs. In simpler terms, ML engineers and data scientists adjust parameters until the output and input data match.

This can take hundreds of labor and computing hours, significantly increasing the cost of the AI model.

But unsupervised learning isn’t a whole lot cheaper.

For instance, in the same CNBC report, it took 2,048 A100 GPUs about 3 weeks to train the LLaMA model.

Naturally, an average business isn’t going to spend nearly as much to train its AI model. Its scope would likely be much smaller than a general AI chatbot, so it would need to train less to achieve the same results.

Still, we see how it could cost hundreds of thousands of dollars just to train AI.

Training AI model is an expensive aspect of AI development, as it requires many resources and qualified expertise. It’s important to remember that using inappropriate AI techniques can significantly impact the outcome of AI project.

If you’re interested in understanding technical reasons behind AI projects failures, you might find our article “Reasons Why AI Projects Fail” insightful. The article discusses common pitfalls in AI projects development.

5. Integration

Now that we have the hardware, the data, and the training done, what else could possibly add to the cost of AI?

Integration. It’s integration.

According to Harvarad Business Review, a company using AI also needs a good AIOps (AI Operations) team. This team is responsible for integrating and deploying the AI model within the company. AI aims to make things easier for its users, so AIOps ensures that the project will lead to success.

Depending on the complexity and scale, an AI tool can take months, or even years, to integrate into a business’ workflow. The team of professionals has to be paid a hefty sum as they work closely with the employees. This ensures that the model will actually save time and money in the long run rather than be an expensive paperweight.

6. Testing and Optimization

After an AI model has been integrated, it’s time to further test and optimize it. Again, this is the AIOps team’s responsibility.

The team works with a company’s employees to understand what the AI is doing well and what it struggles with. And while this is happening, the company is losing out on potential revenue generated by the employees, further adding to the cost.

This process can also take months to finish, depending on how good the initial results from the AI are.

It’s often implemented through an iterative approach, during which the model is continuously adjusted based on feedback from testing.

7. Operating Costs and Maintenance

Even after the AI model has been successfully implemented, the monthly operating costs are no laughing matter.

For instance, Latitude, an AI dungeon game, was spending $250,000 a month to run their generative AI model and Amazon Web Services. This was largely for computation and licensing fees.

They then decided to make some changes, such as using open-source models, but the operating costs still amounted to around $100,000 a month.

This is because AI requires a ton of computing power to operate, which also means hefty energy bills.

On a larger scale, the costs rise exponentially.

Google estimates that adding a 50-word answer generated by AI to only half of search queries could cost $6 billion dollars.

In addition to that, maintaining and updating an AI model also adds to the cost. And this is almost mandatory, as a company’s needs grow and change constantly.

If the model’s scope increase significantly, the additional costs for more data, training, and hardware also add up.

Final Thoughts

Creating a custom AI tool costs millions of dollars because the technology is resource-intensive. It must be integrated, retrained, and optimized constantly to ensure it remains productive.

Despite the high expenses, successful companies that can afford AI still believe in its potential to streamline their business.