How Much Does AI Cost? Exploring Pricing Factors and Implementation Types

Jul 01, 2024
How Much Does AI Cost? Exploring Pricing Factors and Implementation Types

AI has revolutionized the world, promising giant breakthroughs everywhere. Many businesses dream of repeating the success of ChatGPT, but there is one simple question to answer:

What’s the price, and is the game worth a candle?

You may be surprised that simple AI models cost as little as $5,000. It’s not such a heavy price tag. Read our article, which is packed with figures and insights, and learn:

  • Factors influencing AI cost
  • Implementation types and their costs
  • Additional cost considerations

Devler.io is a platform that connects pre-vetted developers with businesses. We know how the project costs can vary. And if you need to clarify things and see the price of your specific project, do not hesitate to contact us. We’ll also help you find the right specialists for implementation.

Let’s dive right in!

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Complexity of the AI solution

The complexity of the solution you need can vary. For example, building a chatbot is typically cheaper than complex computer vision solutions. It's often wise to start with a basic integration that addresses your core needs. You can then evaluate the impact and expand functionality as your requirements evolve.

Interesting fact: More complex deep learning solutions go up to $500,000. For example, Sam Altman, OpenAI CEO, confirmed that training ChatGPT costs over $100 million. 


Another critical point to consider while talking about the complexity of an AI system is integration with multiple systems. The more systems involved, the higher the cost.  If the cost of integration outweighs the benefits, there are alternatives:                

  • Manual data entry (time-consuming and error-prone) - is better for one-time data transfers for small datasets or situations where integration costs are prohibitive and the benefits of automation are minimal.                
  • Standardized data formats across systems (reduces manual work but lacks automation) - are best to use in cases such as when dealing with a limited number of systems exchanging similar data types.

Note! Developing an AI system is one thing, and maintaining is another. Real-world data constantly changes; ongoing maintenance is necessary for your AI to become accurate and reliable. We’ll talk more about maintenance below.


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Data requirements

Data is the lifeblood of AI systems. Your AI system will be more accurate and useful with more data you have. It is important to remember that data collection, labeling, and cleaning are time-consuming and costly. For startups, it is better to start with a smaller, targeted data sample and scale up over time. Open-source datasets like the Registry of Open Data on AWS are also a good way to mitigate expenses.

Additional factors to consider:

  1. Data format

The data can be structured and unstructured.

Structured data, typically stored in databases with pre-defined categories and labels, is readily interpretable by AI models. This format minimizes pre-processing needs, leading to lower development costs.

At the same time, unstructured data, like text, images, and videos, requires additional work to extract features and make it usable for AI.

  1. Data storage

A startup building a basic chatbot might utilize a low-cost cloud storage tier for its text data, while a company developing a self-driving car would require high-performance storage for real-time processing of sensor data streams, incurring higher storage costs.

  1. Data processing speed

Real-time processing of data streams, crucial for applications like fraud detection, requires more robust computing resources and specialized storage solutions. This translates to higher costs for real-time AI systems.

AI infrastructure and hardware

AI infrastructure and hardware are the backbone for training and running powerful AI models. For example, training ChatGPT-4 was related to the consumption of 1000 average US households in 5-6 years.

To train your AI model, you need:

  • GPUs
  • CPUs
  • Servers
  • FPGAs
  • ASICs


In addition to this, you also need to pay for:

  • Licensing fees

For commercial AI platforms, expect licensing fees to range from $10,000 per year for basic functionality to well over $100,000 for enterprise-grade solutions. Open-source frameworks typically have no licensing fees, but some might require commercial licenses for specific features.

  • Software maintenance

Maintaining AI systems is crucial for optimal performance. This includes bug fixes, security patches, and feature updates. Maintenance costs can be a significant ongoing expense, often ranging from 20-50% of the initial development cost annually.

Subscriptions for AI frameworks

While many popular AI frameworks are open-source and free, some offer paid subscription tiers. These subscriptions might provide access to additional features, enterprise-grade support, or priority access to new functionalities.

AI talent and expertise

Of course, developing an AI system is not only about technology but also the labor involved in its implementation.

According to Indeed, hiring a data scientist in the US will cost you $123,775 per year. And a machine learning engineer usually takes $161,832 per year. The table below demonstrates whom else you can add to your AI team and what the cost is.


See hourly rates by location and by experience.

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Remember, AI talent is an investment, not just an expense. The right team can deliver significant long-term value for your organization.

Implementation Types and Their Costs

On-premises AI

On-premises AI offers organizations complete control over their data and models, ideal for industries like finance, healthcare, or government, where data security is paramount. But this control comes at a cost. Let's delve into the expenses of installing and maintaining an on-premises AI infrastructure.

What do you need for on-premises AI? 

  • Hardware: The heart of your operation. You'll need powerful servers, potentially equipped with specialized hardware like Graphics Processing Units (GPUs) for computationally intensive tasks like image recognition.  

The hardware for basic AI tasks, like sentiment analysis, will cost $5,000 to $15,000. As for complex AI tasks like image recognition, their price is somewhere between $50,000 and $200,000+ depending on the number and type of GPUs needed. 

  • Software: Licensing fees for AI development tools, frameworks, and libraries can add up quickly. Costs vary depending on the specific software used and the number of licenses needed. 

For example, open-source frameworks (e.g., TensorFlow) are free to use but require in-house expertise for setup and maintenance. Simultaneously, annual licenses can range from $5,000 to $25,000+ per user, depending on features and complexity.

  • IT infrastructure: Here, you need to consider the cost of cooling systems, power consumption, and data center space. 


Cloud-based AI

Cloud-based AI offers a compelling alternative to on-premises solutions. It provides access to pre-built infrastructure, tools, and AI models, allowing for faster deployment and scalability. 

Cloud-based AI has a lower upfront cost, as there is no need for significant investments in hardware, software, and IT infrastructure. This model offers more control and customization compared to SaaS but requires some technical expertise to manage the infrastructure. 

Cloud-based AI platforms often utilize two main pricing models:

  • Software as a Service (SaaS)

Imagine renting an AI toolset instead of buying it outright. With SaaS (Software as a Service), you access pre-built AI functionalities and features through a web interface, similar to how you use webmail or online document editing tools. This makes it incredibly user-friendly – no need for extensive software installations or complex configurations. The cost can be as small as a few dollars per month. 

  • Platform as a Service (PaaS)

Imagine having a dedicated space with all the tools and resources you need to build and deploy your own AI models. That's essentially what Platform as a Service (PaaS) offers in the realm of cloud-based AI. Unlike SaaS (Software as a Service), which provides pre-built functionalities, PaaS empowers you to construct your own custom AI solutions. The cost of this solution starts from a few hundred dollars per month. 


Hybrid AI

While both on-premises and cloud-based AI offer distinct advantages, there's a growing trend: Hybrid AI. This approach bridges the gap between these two worlds, combining the strengths of each to create a powerful and versatile AI solution. 

While the upfront costs of hybrid AI can be higher than cloud-based AI, it can be more cost-effective than on-premises AI in the long run, especially for organizations that deal with sensitive and non-sensitive data, have fluctuating workloads, or require access to pre-built AI expertise. 

Additional Cost Considerations

There are ongoing costs to consider beyond the initial implementation:

1. Ongoing maintenance and updates: AI models require regular maintenance to ensure accuracy and security. This includes data refresh, retraining, and potential software updates.

How much?

Ongoing maintenance and updates may cost $10,000 - $50,000 per year (depending on model complexity). This range reflects the cost of data scientists or engineers who manage the maintenance process. 

2. Ethical and regulatory compliance: Ensuring your AI adheres to data privacy regulations (like GDPR) and ethical guidelines can incur legal consultations and compliance audit costs.

How much?

Legal consultations for ethical compliance cost $5,000 - $20,000 per project. This range reflects the cost of consulting with legal specialists who can advise on potential biases or ethical considerations in your AI development process. 

At the same time, the cost of compliance audits is $10,000 - $30,000 per year (depending on industry regulations). The frequency and cost of audits will depend on the specific regulations your industry adheres to. For example, industries like healthcare might face more stringent audits than marketing companies.

3. Return on investment (ROI): While AI can offer significant benefits, it's crucial to consider the potential ROI. Evaluate how AI will improve efficiency, revenue, or other metrics to justify the investment. 

How much?

Hiring data scientists for ROI evaluation costs $80,000 - $150,000 annually. Data scientists with expertise in the specific domain of your AI project can provide valuable insights into the potential return on investment. Their analysis can help you determine if the benefits outweigh the costs of AI implementation.

Final words…

As you see, costs differ. It all depends on your needs and budget. Only some companies demand a full-blown AI system like ChatGPT, as there are plenty of cheaper and scalable solutions.

So, are you ready to unlock the full potential of AI for your business? 
If yes, contact Devler.io; we’ll help you find specialists to implement your project!

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