Published on

May 2, 2024


Cost of AI in 2024: Estimating Development & Deployment Expenses

Cost of AI in 2024: find out how much does AI cost now depending on your use case learn effective strategies to manage and optimize expenses.
Jon Reilly
Co-Founder, Co-CEO, Akkio

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.

This post explains the various costs associated with implementing AI features in a business. Depending on the specific AI features and use cases needed, costs can vary greatly. We'll overview the key factors that drive AI expenses so you can budget appropriately and implement AI without burning a hole in your pocket.

Key Takeaways

  • AI implementation can cost as little as $25 per month if all you need is an AI-empowered team using ChatGPT for teams, or it can amount to millions of dollars if you're aiming to develop a recommendation engine that rivals Netflix's;
  • The most significant costs in AI come in when you develop your own solution. Using a SaaS like Akkio or a pre-packaged solution that fits your business can cut costs significantly; 
  • For hardware costs, the most significant charge will be for GPUs and CPUs, which are required to train AI solutions. OpenAI spent over $100m to train GPT-4.

Types of AI

First of all, you should ask yourself what you are trying to build. A simple AI chatbot could be free or incredibly cheap, but a fully-fledged fine tuned AI model can cost millions of dollars.

A few common types of AI use cases include: 

  • Chatbots and Virtual Assistants: These AI systems simulate human conversation, providing customer support and automating routine tasks. Chatbots are the cheapest AI solutions to develop and deploy, and platforms like BotSonic makes it fast and efficient;
  • Predictive Analytics: Utilized for forecasting future events or behaviors by analyzing historical data, ideal for finance, marketing, and operational planning. Akkio can help you with predictive analytics and no-code machine learning starting from $49/m;
  • Image Recognition: AI algorithms that identify and classify objects in images, widely used in security, healthcare diagnostics, and retail. Slyce uses image recognition for its visual search engine, allowing shoppers to take a photo of an item and instantly find where to buy it;
  • Natural Language Processing (NLP): This involves processing and understanding human language, enabling applications like sentiment analysis, language translation, and content generation. This is crucial for topic modeling or text analysis use cases, such as sentiment analysis;
  • Recommendation Systems: AI that analyzes user behavior to suggest products or content, significantly enhancing user experience in e-commerce and streaming services. All social networks use some sort of recommendation system to suggest the best posts for you to consume;
  • Autonomous Vehicles: AI systems that enable self-driving cars, using sensor data for navigation and decision-making. Tesla is a clear leader here, and is working on distributing the same AI capabilities to humanoid robots with Optimus;
  • Fraud Detection: AI algorithms that detect unusual patterns and potential fraudulent activities, particularly useful in finance and cybersecurity. Stripe notoriously employs GPT-4 from Open AI for fraud detection;
  • Robotics: AI-driven robots perform tasks in manufacturing, logistics, and even in surgical procedures, improving efficiency and precision;
  • Personalized Marketing: AI that tailors marketing content to individual preferences, optimizing engagement and conversion rates. Google Ads is now offering Generative AI capabilities in their Performance Max campaigns;
  • Speech Recognition: Transforms spoken language into text, enabling voice-activated control systems and transcription services.

Your company might need AI for one or multiple or these use cases. Or even have a custom, enterprise-grade use case in its own right.

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 also depends on multiple factors like the size of your startup, industry type, and so on. 

Hardware Costs

Hardware Type Description Cost Estimate Additional Notes
GPUs Essential for neural network training due to parallel processing capabilities. ~$10,000 per unit for Nvidia A100. Rental costs: ~$1.14/hr on Google Cloud, ~$3.06/hr on AWS.
FPGAs Suitable for AI applications; less expensive than GPUs but challenging to program. From $5 to over $100,000, depending on capability. Cost varies widely based on required performance.
ASICs Custom-built for specific tasks, offering high performance for complex AI applications. Variable, often high. Cost dependent on customization and performance needs.

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. 

Some companies decide to offload the hardware costs to cloud solutions like AWS and Microsoft Azure. Even then, training GPT-4 in Azure cost OpenAI $100m.

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.

There are a few different types of hardware that are commonly used for AI:


GPUs, or Graphics Processing Units, are crucial in the training of neural networks due to their architecture, which is exceptionally well-suited for the parallel processing demands of machine learning. Unlike traditional CPUs (Central Processing Units), which are designed to handle a wide range of computational tasks but typically process tasks sequentially, GPUs are designed to handle multiple tasks simultaneously.

Nvidia GPUs like the A100 data center GPU can cost around $10,000 per unit. If you rent them in the cloud, you’ll be spending approximately $1.14/hour on the Google Cloud Platform.


FPGAs (Field-Programmable Gate Arrays) 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.

FPGAs range from around $5 for low-end models up to $100,000+ for high-capability versions required in advanced applications


ASICs (Application-Specific Integrated Circuits) 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.

Google Verpex, Microsoft Azure, and Amazon AWS are all shipping new products to entice developers and companies to develop AI solutions in their cloud.

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. Going fully on-premise is not a feasible option for the vast majority of businesses.

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 AI software development cost, 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, frequently involving manual labor. 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. 

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

Labor Costs

Role Average Base Salary (U.S.) Additional Notes
Data Scientist $123,775 High demand for skills like data science and machine learning.
Machine Learning Engineer $161,590 Specialized role, higher salary due to expertise in AI and ML.
Software Developer $119,030 Essential for developing AI applications, in demand across industries.

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 $123,775.

A machine learning engineer can expect to earn a salary of $161,590, and AI engineers at big companies like OpenAI can get to median salaries (including bonuses) up to $925k per year.

Software developers can expect to earn a salary of $119,030

After all, skills like data science, natural language processing, computer vision, AI development, and deep learning are in high-demand, with a limited supply of talent. Salaries are growing at a fast pace, and hiring talent in the US will become more expensive.

This means that even a small AI development team can cost a business upwards of $400,000 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

an image of a person working on data, showcasing data labeling for machine learning models

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.

Type of Data

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. 

For example, training models and operating an AI that requires lots of images costs significantly more than one that only uses and outputs text.

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.

Developing a customer service chatbot or a spam filtering solutions requires orders of magnitude less resources than an autonomous vehicle or drug discovery.

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.

Time to Deploy

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.

Number of Apps and Devices

Moreover, the number of applications you plan to use AI for can impact costs. For instance, implementing AI for both customer service chatbots and inventory management will increase overall costs compared to using it for just one application. 

Similarly, deploying AI across multiple devices, such as smartphones, tablets, and laptops, as opposed to a single platform, can also contribute to increased 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?

Simple chatbots and basic ML models tend to cost thousands, while advanced computer vision or NLP systems leveraging big data and deep learning can cost millions in terms of software, hardware, talent, and management overhead. Specific realm-specific apps can provide AI features at a fraction of the cost for a monthly price.

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’s the cost of AI? 

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.

Nowadays, no-code platforms to build AIs can assist with all sorts of application use cases at very affordable prices. For only $49/m, you can build no-code machine learning models and chat with your data using Akkio.

Tools like Bubble or Flutterflow lets you develop cross-platforms applications using different LLM models according to your use cases.

No-code AI platforms like the custom GPT store, MindStudio, or Airops, let you develop AIs with embedded workflows.

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.

Implement AI in Your Business Without Burning a Hole in Your Pocket

showcasing generative reports from Akkio, a platform developed to drive the cost of ai down for small businesses and agencies
Akkio uses AI to generate reports

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. 

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. Agencies frequently use Akkio for its white labeling capabilities, now offered in the “Build On” package.

Here are a few examples of how customers are using our platforms to drive AI adoption in their business: 

  • LA/VIE, a media buying group, uses Akkio's predictive models to optimize ROAS and achieve better results for their clients. Their results? A 247% increase in revenue and 208% increase in ROAS; 
  • Sterling Strategies used Akkio to talk to the right donors for one of their political campaigns. They saved 6 months of development time and achieved a 5x annual revenue growth; 
  • Ellipsis developed custom AI models on Akkio to predict rankings on Google. Their new “Falcon AI” platform, powered by Akkio, improved the average SEO results for their clients by 4 times.

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 a free 14 days trial, no credit card required. Or, if you’re too busy to get started on your own, book a call with our sales team and they’ll help you out.


The journey into AI implementation in 2024 is a complex yet potentially rewarding venture. As we've explored, costs can range dramatically from the affordable monthly fees for simple AI tools like chatbots to multimillion-dollar investments in cutting-edge systems akin to those used by Netflix or OpenAI. 

Crucially, understanding your specific needs – be it in hardware, software, or talent – is key to making informed decisions. The evolution of AI technology promises a decrease in costs over time, making it increasingly accessible.

Whether you're a small startup or a large corporation, tools like Akkio offer a streamlined, cost-effective entry point into AI, reducing the need for extensive technical expertise. 

Ultimately, the cost of AI is not just measured in dollars but in the value it brings to your business processes, customer engagement, and competitive edge. As AI continues to evolve, so too will the opportunities for businesses to harness its power efficiently and economically.

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