Finance

A cost breakdown of artificial intelligence in 2022

by
Jon Reilly
,
May 23, 2022

Want to know how much your AI implementation will cost?

AI is a powerful tool that can be used in many different ways. It can be used to improve internal processes, predict customer behavior, and optimize marketing campaigns. However, it’s not as simple as just installing an AI system or software. You need to understand how it works and what it will cost you.

While machine learning is making its way into every industry, it can be difficult to understand how much it will cost. This post will explain the various costs associated with AI in a business and show you how to implement AI without burning a hole in your pocket.

Various costs associated with AI

There are many different costs associated with implementing AI. These include hardware costs, software costs, labor costs, and more. The total cost of implementing AI depends on multiple factors like the size of your startup, industry type, and so on. 

Hardware Costs

One of the biggest factors in these costs is the hardware required to run AI algorithms.

To run AI algorithms efficiently, specialized hardware is needed that can handle the high volume of data and computations involved. This hardware is generally more expensive than standard computer hardware, and so the cost of setting up and running an AI system can be significant.

However, it is important to remember that the hardware costs of AI are not static – as the technology develops, the costs associated with hardware will decrease. This means that, in the long term, the costs of implementing AI systems are likely to fall significantly, making the technology more accessible and affordable for businesses and individuals.

There are a few different types of hardware that are commonly used for AI, each with its own advantages and disadvantages.

GPUs are often used for training neural networks as they can provide the high level of computational power required. However, they can be expensive, and so may not be suitable for all budgets.

FPGAs are another type of hardware that can be used for AI applications. They are generally less expensive than GPUs, but still provide a high level of computational power. However, they can be difficult to program, which can make them less suitable for some applications.

ASICs are purpose-built chips that are designed for specific tasks. They can be expensive, but offer high performance and are sometimes used for complex AI applications.

This hardware can be provisioned in a few ways, the most common being on-premises (in your own physical servers), in the cloud (using somebody else’s servers) or via a hybrid approach (using a combination of on-premises and cloud-based resources).

On-premises hardware can be expensive to set up and maintain, but offers the advantage of complete control over the environment. Cloud-based hardware can be more cost effective, but you may have less control over the underlying infrastructure.

As a Centerbase article reports, a single “middle-of-the-road server will land you north of $10,000,” with another $2,000-plus for a backup system.

Software Costs

We're now familiar with the hardware costs of AI - the computational power and data storage required to train and run algorithms. But what about the software costs, such as for data collection, analysis, and processing?

These software costs are often hidden or underestimated, but they can be significant. For example, labeling data for training can be a costly and time-consuming process. And once data is collected, it still needs to be cleansed, organized, and processed before it can be used by AI algorithms.

The software costs of AI can therefore add up quickly, especially as data sets grow larger and more complex. As businesses increasingly turn to AI to gain a competitive edge, they need to be aware of these costs and factor them into their budget planning.

The same Centerbase article highlights that software costs, such as access licenses, quickly run into the thousands of dollars per server.

Labor Costs

In order to create and implement AI, businesses traditionally needed to hire data scientists, machine learning engineers, and software developers.

In the United States, the average base salary for a data scientist is over $102,000 (according to Indeed). A machine learning engineer can expect to earn a salary of $112,421. And a software developer can expect to earn a salary of $110,140 (according to US News). After all, skills like data science, natural language processing, computer vision, and deep learning are in high-demand, with a limited supply of talent.

This means that even a small AI team can cost a business upwards of $320,000 per year in technology development costs alone. And that's not even taking into account the cost of benefits, office space, and other overhead costs.

Training and maintenance costs

Training AI models requires computational resources, which come at a cost. In addition, maintaining an AI system requires both hardware and software resources, which also come with costs.

The training of AI models is often done on GPUs, which are expensive. For example, the Tesla V100 GPU costs around $10,000.

The maintenance of AI systems also requires computational resources. For example, Google's DeepMind Alphago system required up to 1,920 CPUs and 280 GPUs to operate. Not only do these resources come at a cost, but they must be continually updated as new data is generated. Additionally, hardware failures can occur, which can lead to downtime and lost data.

Other Costs

A range of miscellaneous costs can quickly add up when implementing AI technologies – from data collection and annotation to legal fees.

Data collection is a critical part of training most AI models. This can be a significant expense, particularly if the required data is not readily available internally. Even if data is available, it may need to be annotated or labeled – a process that can be both time-consuming and costly.

Another cost that is often overlooked is the legal fees associated with AI. As the technology evolves, new ethical and regulatory concerns are emerging. Businesses must ensure that their AI systems comply with applicable laws and regulations, which can require expert advice.

Factors affecting the cost of AI

There are a number of factors that can affect the cost of AI, including the type of data available, the complexity of the problem being solved, the number of people involved in the project and how long you’re willing to wait for results.

The type of data you have available is a key factor. Different types of data require different levels of training, so if you have more complex data, it will cost more to train the AI model. The quality and quantity of data also play a role – more data requires more processing power, and low-quality data may not yield the best results, so you may need to use more complex models.

The complexity of your problem is another important factor. More complex problems require more training data and more processing power, so they will be more expensive to solve. The number of people involved in the project can also affect costs – if there are more people working on the project, it will naturally cost more.

How long you’re willing to wait for results is yet another consideration. If you want faster results, you’ll need to spend more money on training and processing power. Similarly, if you want to constantly update your models or make predictions on demand, rather than waiting for data to be processed, that will also incur additional costs.

Moreover, the number of applications you want to use AI for can impact costs. The more applications you want to use AI for, the higher your overall costs will be. Similarly, the number of devices you want to use AI for can also contribute to increases in costs.

All in all, there are a number of factors that can affect the cost of AI. It’s important to consider all of these factors when deciding how to budget for your project.

So, how much does artificial intelligence cost?

According to a recent report, Netflix spends $1.5 billion on technology annually. A chunk of their tech budget is spent on artificial intelligence. AI helps them personalize recommendations for each individual user and also automate many of their processes, like creating subtitles. 

Netflix isn’t the only company spending big bucks on AI. Google, Facebook, and Amazon are all investing billions in AI research and development every year. So, what does AI cost? 

There is no one answer to this question since it depends on the specific needs of your business. However, you can get a rough idea by looking at how much companies in size similar to yours are spending on AI. 

For example, if you’re a small business with a limited budget, you might want to start with something simple like using AI software to automate your customer support with a chatbot. This can be done relatively cheaply and will free up your staff to focus on other tasks.

On the other hand, if you’re a large company with deep pockets, you could invest in more complex AI applications like developing a personalized recommendation system for your customers. 

In short, the cost of artificial intelligence depends on the level of functionality you wish to achieve, and the level of business processes you’re trying to augment. A simple MVP, or minimum viable product, can be built using open-source technology for close to no cost, but to create a high-quality AI project with a high accuracy rate, you’ll want a custom solution that learns from a large amount of data.

How can you implement AI in your business without burning a hole in your pocket?

In recent years, Artificial Intelligence has become one of the hottest topics in the business world. Everyone wants to get in on the action but very few know where to start or how to implement AI without burning a hole in their pocket.

There are many tools available that make implementing an AI solution much easier. These tools typically charge based on usage or per-month, have a steeper learning curve and are better suited for experienced developers who know what they're doing. 

Alternatively, you might also need to hire data scientists to analyze your data and make decisions. However, there is one way that stands out above all others - using an easy-to-use tool like Akkio which reduces the complexity of implementing AI by orders of magnitude!

Akkio makes leveraging machine learning and AI incredibly easy - it allows anyone with no technical knowledge whatsoever to build custom ML models using their own data which they can then use to make predictions about future events. It's perfect for all kinds of businesses - small businesses who are just starting out, businesses with marketers who want to predict customer behavior or optimize their campaigns accordingly, for financial institutions and big companies looking at market trends, and more. 

To give you an idea of just how easy it is to use Akkio, let us share a brief case study of one of our clients. Christina Valente, Senior Director of Product Operations for an electronics company, and her team of engineers were spending 75% of their time reviewing feedback and trying to reproduce and solve issues. This was a growing financial burden for the company.

At the same time, project managers were spending 3 work days selecting the right customer to participate in tests. Using Akkio's no-code AI models, the team was able to categorize and prioritize feedback in minutes, using only their data and domain expertise. This meant that adoption was sky-high, as the barrier to usage of a no-code tool is as low as it gets.

If you're thinking about incorporating AI into your business but don't know where to start, look no further than Akkio.

Get started with AI today using Akkio

If you're like most businesses, you've been considering using AI in some capacity but have been put off by the cost and complexity associated with it. After all, implementing AI can be a major undertaking - from hiring data scientists to sourcing and labeling training data, there's a lot to think about.

It doesn't have to be this way, though. Akkio is a comprehensive AI platform that makes it easy to get started with machine learning, even if you're not a coder. It comes with pre-built models that you can use out of the box, and it doesn't require a lot of data to get started. Best of all, it's scalable and very affordable. 

Akkio makes AI development and deployment effortless, so you can focus on augmenting decision-making across a wide range of use-cases, instead of needing to build slow and expensive in-house solutions.

So if you're thinking about using AI in your business but have been put off by the cost, Akkio is the perfect solution. Get started with a free trial today.

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