These days everyone seems to want to jump on the AI development bandwagon, especially generative AI. And for a good reason. Powered by artificial intelligence, software can help businesses cut costs, increase customer satisfaction, boost revenue, and save time on completing routine tasks, among many other useful things.
So it’s only a matter of time before you as a business owner or a company’s decision maker start asking yourself, “How much does AI cost and how can I integrate it into my operations?”
As Uptech’s co-founder, I know that this question will definitely arise. And as an AI expert, I know the answer to it.
First things first, you don’t have to be the size of Google to be able to build an AI solution as many people are used to thinking. The average cost to develop AI software can range from $10,000 for a simple solution or feature to $200,000 and more for the complex tech part alone. So AI can be affordable. However, there are a lot of factors that impact the pricing tag of AI development.
This article will shed some light on them and not only. Read our guide so that the question, “How much does it cost to build an AI system?” could finally be answered.
Main Factors That Make Up the Artificial Intelligence Cost
The cost of AI can range widely. For a simple project, expenses start at around $10,000, but for more complex, technology-intensive projects, costs can exceed $200,000.
When we look at AI development projects reviewed on Clutch, the costs typically fall between $10,000 and $49,000. Consulting services are often less expensive, varying according to the consultant's hourly rate, which usually ranges from $200 to $350. Manpower cost ranges between $25-$49 per hour.
Meanwhile, third-party AI software, such as virtual assistants, can incur annual costs of up to $40,000, although some may be available for free.
Read our dedicated article if you are eager to find out more about how to create an AI assistant and how much it costs.
So, why is the range so broad?
Because the cost of AI depends on a variety of factors, such as
- the development type (in-house or outsourced)
- the AI project complexity
- additional resources needed for complex systems
- the type of AI applications being developed, to name a few
Let's explore the key factors in more detail to understand how they influence costs.
In-house or outsourced AI development
So the first factor that can either make AI development more expensive or cheaper is your decision to build the product in-house or outsource its development.
Here’s an overview of the expected AI prototype in hours:
- Use case development – approx 60 hours
- Data definition and extraction – approx 80 hours
- Algorithm deployment and iteration – approx 120 hours
- Success evaluation – approx 20 hours
The total project involves 284 hours of work.
Cost of in-house development
One approach is to utilize in-house subject matter experts to build an AI system. While highly sustainable, this method is time-consuming and expensive.
If we take a developed European country or the US, an average hourly rate between $50 and $99, results in costs will range from $14,200 to $28,100 for 284 hours of work on the AI prototype.
Cost of outsourced AI development
Outsourcing software development to AI development companies is a cheaper option. We’ll use Ukraine as an example, where hourly rates range from $25 to $49. Based on these rates, the total cost of developing the AI prototype would range from $7,100 to $13,900, which is twice as cheap.
The complexity of the AI solution
Different AI systems involve different levels of complexity and, therefore, resources. This includes the scope of the project, the number of specialists needed, the difficulty of the tech stack, etc.
For example, developing an entire deep-learning model from scratch will logically be a more time- and resource-consuming endeavor than integrating an available base model, say, GPT-3.5 Turbo by Open AI.
Low-level complexity AI solution. For simpler AI applications, such as adding a specific feature to an existing software system, developers often leverage existing platforms and pre-trained models.
These solutions generally do not require extensive customization, detailed data training, or complex infrastructural setups. This accessibility results in relatively lower development costs.
Cost estimate: $5,000 to $20,000+
Medium-level complexity AI solution. At this level, custom model development might be necessary, where specialists utilize machine learning frameworks to process and analyze large datasets. This price range considers the need for data preprocessing, algorithm selection, model training, and integration into existing platforms.
Cost estimate: $30,000 to $100,000+
Complex AI solution. The development of highly complex AI systems, such as those used in autonomous vehicles or advanced robotics, requires specialized knowledge and technologies. Highly complex AI models can beat the cost of a whole app.
These systems may need deep learning techniques and substantial hardware involvement, which are resource-intensive and need rigorous testing and compliance with strict regulatory standards.
Cost estimate: $200,000 to $1,000,000+
Additional resources needed for complex AI systems
The complexity of an AI model also impacts other associated costs, which can include:
- Training and data costs. More complex models, like those used in natural language processing or image recognition, require vast amounts of data that must be accurately labeled and managed.
- Infrastructure costs. Advanced AI models need robust computational infrastructure for training and deployment, which can be particularly expensive.
- Maintenance costs. Maintaining and updating complex AI systems to adapt to new data or to enhance functionality can be a continuous cost.
To illustrate the scale of expenses involved in high-end AI development, consider OpenAI’s GPT-4 model. OpenAI CEO Sam Altman revealed that training this model alone cost them over $100 million. This example underscores how investments in AI can escalate, particularly as the ambitions and capabilities of AI grow.
Types of AI solutions and their prices
AI chatbots capable of handling your customer queries and a recommender engine like the one Amazon has are both types of AI, yet their development prices will differ drastically.
Let’s make a cost breakdown of a few popular types of AI or the showcase.
Keep in mind that these are rough estimates and just a few factors mentioned. Among other things, there is the size of the dataset you need to train a model, the complexity of the algorithms, the pricing tags of the AI development company you want to hire, etc.
As for the industries, we have a few dedicated blog posts that explain the use of generative AI in various domains, namely:
- Benefits and use case of generative AI in banking
- Different ways generative AI is used in healthcare
- How the insurance industry utilizes GenAI.
Each case is unique so to find out how much it costs to implement AI for your particular needs, contact us to schedule an appointment.
How Much Does AI Cost: 3 Key Factors
The development of AI software involves multiple steps, each with specific challenges and associated costs. To plan and budget effectively, it’s important for you to understand these stages and their expenses.
Step 1. Assessment and planning
This initial phase covers goal definition, feasibility assessment, and strategy planning for AI integration.
Costs may include:
- Consultation fees for AI experts to define and evaluate the AI strategy
- Assessments of technical and organizational readiness
- Allocation of resources for planning and documentation
- Complex projects might require workshops and detailed presentations
The estimated cost range for the assessment and planning of an AI project is $5,000 to $25,000 and higher.
Step 2. Model design and development
This is the heart and soul of the AI development process where teams convert concepts into actionable technology. There are a few variants of this step based on the type of development you want to go with.
Namely, you can choose between:
- custom AI model development,
- pre-trained AI models,
- third-party AI providers.
Custom AI model training and deployment
This stage is resource-intensive, like really resource-intensive. It involves data preparation, model development, and deployment activities.
The whole process encompasses the following sub-steps, each influencing the overall cost of AI:
- Data acquisition, cleaning, and preparation. Unless you have ready-made datasets that would be enough for model training, additional resources will be required to perform data labeling, get rid of the information garbage, and create a high-quality training dataset.
- Development of model architecture and training of the model. Specialists, commonly data scientists or in some cases ML engineers, build the model architecture and conduct training, which demands extensive computational resources.
- Validation and testing. The team puts the model on the tests and fine-tunes it to meet performance criteria.
- Deployment and maintenance. The team brings the model into a production environment and takes care of its maintenance. Here, the significant costs associated with infrastructure must be considered.
The estimated cost range for custom model training and deployment is $50,000 to $300,000 and higher.
Use of pre-trained models
Pre-trained models can be a cost-effective way of deploying AI capabilities, especially if your operations don’t require complex tailored models in place.
This scenario involves:
- Selection and configuration. The team identifies and configures the right pre-trained model to suit the system’s requirements.
- System integration. The model is being integrated into existing systems, which often requires a certain level of customization. More on how to integrate AI into an app, read in our separate blog post.
- Testing and monitoring. The team ensures the model performs as it is supposed to under various conditions.
While pre-trained models greatly accelerate the development process, they still must be post-trained and adapted to the client’s data. For this, developers need to gather this data, process it, and use it for model fine-tuning.
If we talk about pre-tained models like large language models (LLMs), they don’t require post-training. However, their deployment might be costly since they require a lot of resources. As a result, the price may go up.
The estimated cost range for the pre-trained AI models is $35,000 to $150,000 and higher.
Third-party AI provider
This approach means that you opt for a third-party AI provider to enhance your software with AI capabilities. The prices for these systems vary.
Let's take GPT-4 Turbo from OpenAI as an example. This language model is priced per usage, with costs denoted either per 1 million (1M) tokens or 1 thousand (1K) tokens. To give you an idea, 1,000 tokens are roughly equivalent to 750 words.
Here are the specific pricing details for different configurations of GPT-4 Turbo:
Apart from the subscription or service fees for using the AI service, there are other aspects affecting the overall pricing of this option, like:
- integration and customization jobs to align with specific system functionalities
- testing and deploying the AI-enhanced features, unless you do that yourself
Third-party AI provider estimated cost range: $15,000 to $100,000 and higher.
Step 3. Ongoing maintenance and optimization
The final phase ensures AI systems continue to perform effectively and adapt to new requirements or data.
- Regular updates and tuning. AI specialists update and tune AI models for better accuracy and performance.
- Monitoring system performance. Teams monitor the system to address performance issues timely or to update it based on new operational data.
- Stakeholder training. All the responsible keep all stakeholders informed about the latest AI developments and system changes.
This stage can be outsourced to a country with lower hourly rates, and in this way, you can cut down the costs a bit. By the way, we have a post explaining why the West is outsourcing to Eastern Europe, make sure to check it out.
The estimated cost range for the post-deployment stage is $20,000 to $80,000 or higher.
What Does Your Business Win by Implementing AI?
The popularity of AI speaks for itself. But let’s discuss the benefits of implementing artificial intelligence into your business in more detail.
Data-driven decision enhancement
AI-powered systems can uncover patterns and trends in business data that are not as easily, if not impossible, visible to humans. These insights can greatly improve both strategic and operational decisions. Finally, you won’t have to rely on management's gut feeling solely.
Process automation
The improved speed of automation is one of the core reasons for investing in AI app development. AI applications help improve tasks and make routine work faster, allowing businesses to use their resources better. Automation reduces the need for manual labor, helps operations run more smoothly, and leads to greater productivity overall.
Personalization
If your goal is to create personalized experiences for your customers, AI is a real game-changer here. It’s capable of analyzing user behavior and preferences, and, in this way, tailoring content and recommendations for each individual. This leads to higher customer satisfaction, stronger brand loyalty, and a better overall experience for users.
Competitive edge
Better decisions and process automation make businesses more efficient and develop new products and/or services. Put simply, you receive a huge competitive advantage over competitors that passed by AI. At the same time, delaying AI implementation could allow competitors to move ahead, making it difficult to catch up.
Cost-effectiveness
AI-automated and improved business processes add to the efficiency throughout an organization. This applies to both direct and indirect sales processes. For example, AI-powered chatbots can handle customer service inquiries, freeing up human resources, and AI can also optimize sales routes for field staff, leading to more profits.
Round-the-clock operation
AI systems can process data and operate at a speed and scale beyond human capabilities. They can analyze millions of data points in seconds and work 24/7 without breaks or vacations. While competitors' systems might only operate for 8 hours a day, AI systems can run non-stop. As a result, service availability is better, and customers are happier.
From theory to practice, let’s take a closer look at how our delivered AI projects helped our clients and how much it cost to build an AI system for each case.
AI Cost: Uptech Cases
At Uptech, we have firsthand experience in building AI-powered solutions of various levels of complexity – from simpler chatbots to more complex generative AI software systems. While we can’t uncover the exact cost of some projects due to signed NDAs, we can provide an approximate cost range for products if you decide to work with us.
Dyvo.ai for business
Dyvo.ai for business is an AI app for creating studio-quality product photos. This product emerged from the need to simplify the process of creating high-quality, brand-aligned product photos. Transitioning from generative AI technology used for creating fancy AI avatars, we tailored Dyvo.ai to meet the specific needs of eCommerce businesses.
AI-powered features include:
- Quick generation of high-quality, brand-aligned product photos
- One-click background removal
- A selection of designed backgrounds or the option to create personalized backgrounds
- Photo resizing and upscaling
- Capability to add products, text, and elements to align images with brand aesthetics
As a result, Dyvo.ai for business significantly reduces the time and effort required to create engaging content for online stores.
Costs: The investment for developing Dyvo.ai for business was $25,000 to $50,000, falling within a typical range for advanced AI solutions.
Medical record summarizer
In another internal project, we developed a Proof of Concept (PoC) for a medical records summarization chatbot. This AI-driven tool is fueled by the power of artificial intelligence and natural language processing (NLP) to automate the extraction and summarization of relevant information from medical records.
By reducing the time healthcare professionals need to spend reviewing extensive medical documentation, this tool can improve efficiency in medical settings and potentially increase the quality of patient care. The chatbot is designed to simplify data handling and support medical decisions.
Costs: The initial development of the PoC for this technology was around $25,000 with further investment potentially reaching up to $100,000.
Among other AI projects that we worked on, there are:
- Hamlet – an AI text summarizing tool that works with copied text and uploaded PDFs;
- Tired Banker – an AI-driven tool for making more informed investment decisions;
- Angler AI – an AI-powered platform that helps brands radically improve customer acquisition and customer lifetime value.
You can also check out other cases on our website page.
How to Reduce the Cost of AI: 7 Main Approaches
What if we told you that there are ways to cut down the AI costs a bit? While building an AI-based system isn’t the cheapest, here are some effective strategies to make it more affordable.
1. Adopt a lean AI development approach
Start with a lean approach to minimize costs. Focus on high-value features that solve core business problems and begin with a Minimum Viable Product (MVP) that includes only these essential features. This allows you to build on a solid foundation without overspending early on.
2. Use pre-trained AI models
Leverage pre-trained models from libraries like TensorFlow Hub or OpenAI’s GPT to save money. These models provide a starting point that you can fine-tune for specific needs, reducing both the time and cost of training from scratch.
3. Consider hiring remote developers
Hiring remote developers can reduce costs by tapping into a global talent pool where the cost of living may be lower. Platforms like Upwork make it easier to find and manage remote AI developers.
4. Implement Agile methodologies
Using agile methodologies can enhance cost management and improve product quality. Practices like iterative development and continuous testing help catch issues early, reducing expensive rework later.
Handling an AI project in smaller segments can improve cost estimation accuracy and allow for adjustments as the project progresses, which is especially useful in AI development where challenges can be unpredictable.
5. Outsource non-core AI development tasks
Consider outsourcing routine tasks such as data labeling or maintenance to specialized providers. This approach can be more cost-effective than using your in-house team for these non-core functions. Find out more about outsourcing app development costs in a separate blog post.
6. Leverage microservices and reusable components
Utilize modular, reusable components and microservices architecture to save on development time and costs. This approach allows each part of your project to be developed, tested, and deployed independently, enhancing overall efficiency.
7. Set up a robust CI/CD pipeline
You can also opt for a Continuous Integration/Continuous Deployment (CI/CD) pipeline and significantly reduce costs by automating testing and deployment. This not only helps find bugs earlier but also speeds up the release process, improving the agility and cost-effectiveness of your development efforts.
By following these strategies, you can manage and even reduce the costs associated with AI development, making it a more viable option for your business.
Tips for Successful AI Development from Uptech
Last but not least, since the costs of AI development also depend on an AI agency you will choose for cooperation, It’s important to look for one with a proven track record in AI development. As a company that has delivered many AI projects, we’d like to share three key tips for successful cooperation.
Communication and requirements
Clear and direct communication is the key. Make sure to articulate your requirements, expectations, and objectives thoroughly. Describe the AI’s intended use, functionalities, and the outcomes you aim to achieve in detail.
The clearer your communication, the more likely the agency will deliver a solution that meets your needs precisely.
Collaboration and regular exchange
Maintain close collaboration with your AI development partner and ensure regular updates. Hold frequent meetings to discuss the project's progress, celebrate successes, and address any issues. This ongoing dialogue keeps both parties informed and aligned, facilitating a more effective partnership.
Clear agreements and project management
Define your project's timelines, budget, and deliverables upfront. Effective project management is key to a successful AI development process. Proper planning and clear agreements help ensure that you meet your desired milestones and achieve your goals.
Curious to know how much it will cost to create an AI solution for your particular case? Contact us.
FAQs
How much does AI cost?
The costs for simple AI systems start at around $10,000, but for more complex, technology-intensive projects, costs can exceed $200,000.
Why is AI so expensive?
AI systems are expensive due to the high costs of specialized hardware, advanced technologies, and the expertise required. Additionally, significant investments in data collection and processing also contribute to the high costs.
How much does it cost to train an AI model?
Training costs for AI models can vary widely. For example, the training of OpenAI's GPT-4 model, a high-end AI development project, cost over $100 million, according to OpenAI CEO Sam Altman. This highlights the potential scale of expenses involved in training sophisticated AI models.