The FOMO is real. When it seems like everyone and their dog is implementing AI in their business processes, jumping on the bandwagon in a hurry is very tempting.
Here’s why it’s not a good idea to rush. There are many varieties of AI, and AI development services require significant investmentі in technology, talent, data, and infrastructure. Companies often underestimate these variables, leading to insufficient resources and allocating an inadequate budget.
Knowing what types of AI solutions are available will help you choose the appropriate ones for your needs. Then, you must understand the factors influencing AI development costs to determine a realistic budget and avoid unexpected fees.
This article will teach you exactly that. We’ll start by explaining the main types of AI solutions to give you a sense of the complexity of the AI market.
Types of AI solutions
With so many options for AI solutions on the market, it’s easy to get confused. But not if you remember this: first comes the problem, then comes a tool to solve it — not the other way around. After you define the central problem AI has to solve and where it will make the most impact in your business, you can decide which AI type will work best for your purpose.
Before we begin, note that AI is a broad term involving various computer models that simulate human intelligence to perform various tasks. Depending on whether you want to integrate AI to analyze information, make predictions, or communicate with humans, you’ll need a different AI model.
Here’s a brief overview of different types of AI solutions and their uses.
Machine learning solutions
Machine learning (ML) is a branch of AI that identifies patterns in historical training data and then makes decisions or predictions based on those patterns. The more data you use to train the system, the better the results. Also, machine learning models can improve with feedback to become more accurate over time.
There are many types of machine learning, and here are three of the most common:
Supervised learning
Supervised learning models are trained on labeled datasets containing input and examples of correct output. Then, algorithms learn relationships between these datasets to classify data into categories or predict outcomes.
Supervised learning is often used for fraud detection. Systems are trained on datasets that contain both fraudulent and nonfraudulent activity and learn to differentiate them. Another use case is email filtering. In this case, the model trains on emails labeled as spam or not spam and learns to sort new mail accurately.
Unsupervised learning
Unsupervised machine learning models find hidden patterns and insights in a large amount of unlabeled data. An excellent example of unsupervised learning is customer segmentation: the algorithm defines similar characteristics of customers and groups them accordingly (e.g., by age or revenue).
Reinforcement learning
Reinforcement learning is about learning by doing. It involves an intelligent software agent that interacts with the environment, learning from experience rather than a training dataset. The agent uses trial and error to learn which actions lead to positive feedback: it gets rewarded for each right step toward the goal, while incorrect steps are ignored or punished. The goal is to reach the best possible result.
Reinforcement learning is used to train self-driving cars, where they learn different scenarios in a simulated environment.
Natural language processing solutions
Natural language processing (NLP) is a subfield of machine learning that teaches models to understand human language and communicate in a human-like manner. For example, NLP allows chatbots to answer user questions like a real-life support agent.
However, not all chatbots use AI. There are also rule-based chatbots that can respond to pre-defined questions by choosing from a bank of answers, but these can’t process complex queries. AI-based chatbots, on the other hand, understand the meaning of sentences even if they’re phrased in different ways and can ask clarifying questions.
Computer Vision Solutions
Сomputer vision is a branch of AI that allows algorithms to recognize objects in images and videos. Computer scientists train these models using a vast number of labeled examples and algorithms to learn to recognize novel objects and pictures. The ML algorithms identify common patterns and apply this knowledge to identify and categorize new images.
Computer vision has many uses.
Image recognition
Image recognition algorithms identify objects, people, or scenes in images. After training on a vast number of labeled images, the algorithm can classify images and their elements. For example, it can classify medical images with abnormal elements (such as cancerous tumors or skin cancer signs) to aid clinicians with diagnosis.
Object detection
While image recognition only identifies the objects in an image, object detection is a more complex process that also localizes objects. After an object detection algorithm detects the presence of an object, it places a bounding box around the object, defines its exact location, and labels it.
For example, object detection helps self-driving cars understand the environment; it detects obstacles and prevents accidents.
Image segmentation
Image segmentation also localizes and recognizes objects, but it adds yet another layer of complexity. It divides an image into segments, detects the shapes and dimensions of the segments, and compares them to the whole image.
Image segmentation is often used in medical diagnostics. While image recognition helps spot anomalies in medical images, image segmentation does more granular analysis. For example, in tumor detection, it shows doctors the size of the tumor and its exact location.
Predictive analytics solutions
Predictive analytics models forecast events by identifying patterns in historical data and searching for similar ones in new data. For businesses, this means adapting to customer shopping behavior patterns, forecasting retail demand to manage product inventory, or creating pricing strategies based on market fluctuations.
Robotic process automation solutions
Robotic process automation (RPA) uses bots to automate workflows and optimize repetitive tasks (copying data, filling in forms, etc.). It streamlines routine tasks, increases productivity, and reduces labor costs. For example, automated invoice processing can calculate, send, and track invoices, and update information in an Enterprise Resource Planning system (ERP).
Recommendation systems
Recommendation systems collect and analyze user behavioral data to identify patterns, learn about user preferences, and generate recommendations. For example, a predictive model can recommend articles, books, movies, and videos similar or related to what a user watched in the past.
Generative AI solutions
GenAI algorithms train extensively on a vast dataset and then generate new content in response to user prompts. The amount of data used to train the algorithm is enormous; for example, GPT 3.5. was trained on 45 TB of text data. Also, generative AI models use feedback to learn and thus refine and improve its output.
Digital assistants
An AI-powered digital assistant uses NLP to understand and process human requests. It interprets queries in natural language and generates human-like responses (e.g., Apple Siri, Alexa). Digital assistants help people with everyday tasks like directing support inquiries or suggesting additional content to customers from the knowledge base.
The number and variety of AI solutions continue to grow, with new models constantly entering the market and competing for market share. Therefore, AI development costs can seem like a rollercoaster, with prices fluctuating up or down depending on different factors.
Factors that influence AI development cost
How much does AI cost, development-wise? There are various sources where you can find ballpark cost estimates for developing artificial intelligence models. But the truth is that you’ll never find hard numbers or a fixed price on the web because it’s nearly impossible to forecast all the factors that influence artificial intelligence costs.
As an evolving technology, AI model development requires extensive hypothesis testing. However, if you know the main pillars of AI cost, you can plan your budget more effectively. Artificial intelligence cost estimation starts with evaluating the factors affecting the cost of AI.
1. Project complexity
When talking about the complexity of AI products, there are several considerations.
Type of AI solution
How much does artificial intelligence development cost? Depending on the complexity of your project, you will need a specific type of AI solution. For example, integrating supervised machine learning requires less computational power than complex advanced analytics, so it will cost less to develop. Also, depending on the task you need to accomplish, you may need several types of AI models developed, which will increase the AI software price.
Dataset structure and size
AI algorithms identify patterns and relationships more effectively when they train on cleaned and organized data. Data cleaning comes at a cost and requires time investment, especially if your organization's data is unstructured.
Also, the more complex an algorithm, the more data it requires for training. Machine learning solutions that use structured learning require less data than models that learn from data without a predefined structure (e.g., deep learning).
Model training
There are two main options for model training: you can use pre-trained foundation models, such as ChatGPT from OpenAI, or choose a free, open-source model and train it.
If you work in a niche industry, we recommend training a model from scratch with your own data; fine-tuning pre-trained models will not produce accurate output for a niche market. We also recommend using your own database to train a model if you want to provide customers with personalized solutions or improve your internal business processes.
2. AI software
Open-source AI models are free to use, which may initially seem tempting. They’re customizable, transparent (everyone can access their code), and using them locally means you don’t have to pass your data to an external server.
However, when it comes to deployment and operating costs of artificial intelligence development, there’s a catch: open-source models are resource-intensive, which makes the infrastructure to host and deploy them more expensive.
In contrast, if you purchase a proprietary model, such as those developed by the AI research company OpenAI, infrastructure costs less as you don't have to set it up. However, the ongoing cost can be high because you pay for requests you send, which may turn out to be quite expensive, especially if you send more than 1,000 requests per day.
Long story short, open-source AI models are cost-effective in the long run if you have a large volume of requests. However, they have higher setup costs. An out-of-the-box model is a better choice if you want to minimize model training costs and save on maintenance. But get ready for higher AI software development costs if you need to process more requests as you scale your project.
3. Development team
The cost of artificial intelligence software development also includes salaries. An AI development team usually comprises data scientists, software developers, quality assurance engineers, architects, and more. The number of specialists depends on the complexity of your project. You have two options: hire in-house professionals or work with an external company.
Hiring an in-house team is the more expensive option because you have to pay for administrative costs, such as recruitment, onboarding, salaries, benefits, and special equipment (computers and other office equipment if they work on-site).
Outsourcing a development team is often more budget-friendly, especially if you outsource to a country with a lower cost of living than yours. Plus, when working with an external company, you pay only for the work done and avoid all the administrative costs. If your project grows, an outsourced team can quickly expand the team with the specialists you need.
4. Technology stack
Your AI tech stack includes technologies, frameworks, and tools used for data acquisition, training, deployment, and maintenance. An AI stack can have the following components:
- Deep learning frameworks (TensorFlow, PyTorch, Sonnet, Keras)
- Data collection and processing tools (RapidMiner, Databricks, Alteryx)
- Programming languages (Python, Julia, C++, Java)
- Model training platforms (Amazon Sagemaker, Microsoft Azure, IBM Watson)
- Deployment tools (Seldon, Bento ML, Cortex)
- Monitoring and optimization tools (Arise AI, Qwak, Evidently AI)
- Security tools (Crowdstrike, Darktrace)
- Hardware (GPUs, TPUs, and NPUs)
The specifics of your technology stack and its price depend on your AI solution type, project scope, computational complexity, data volume, and scaling needs. For example, if your product uses real-time data processing, it requires a more advanced tech stack to operate seamlessly in peak hours.
5. Deployment and maintenance
“How much does it cost to build an AI app?” is not the only question you should be asking. How much will it cost to maintain it? The price of deploying and maintaining AI applications consists of the following elements:
- Infrastructure and hardware expenses, including servers, GPUs, and cloud services
- Operational and support costs related to licensing fees, monitoring performance, and troubleshooting errors
- Data management costs like data storage and updating not relevant training data
You also need to account for increases in deployment and maintenance costs as your project grows.
How to keep a lid on the cost of an AI project
Often, when business owners get quotes from software development companies, all their hopes of disrupting the market with AI are dashed. Given the size of the investment, it’s important to carefully evaluate your idea before developing an AI product. You’ll also want to find ways to keep the cost in check. Let’s review some strategies to help you plan your budget and reduce the cost of AI.
Calculate your ROI
After getting an estimate from a development company, run a cost-benefit analysis to understand how the investment required for AI implementation compares to the expected benefits. Consider factors such as development cost, time to deploy, and ongoing maintenance costs and compare them with the revenue you will likely get when calculating your potential return on investment (ROI).
Start small and allocate resources wisely
If possible, start with pre-trained models and fine-tune them to adapt them to your specific needs. This way, you can quickly create a prototype for your project and validate your ideas. Use an agile project management approach and focus on a specific use case. For example, you may want an AI model to handle customer onboarding. Start there, and expand your project with more features after gaining traction with AI.
Analyze your current infrastructure and technology
If you’re looking to integrate AI into an existing project, you must evaluate your current hardware, software, and network capabilities and compare them with what’s needed to implement AI. You should also audit your data storage capacity, processing power, and network speed. This will help you avoid extra maintenance costs and troubleshooting issues in the future.
Understand long-term and short-term costs
Consider both short-term and long-term costs and plan your budget and resource allocation for the entire product lifecycle. Short-term costs include development, software and tools, infrastructure, testing, and deployment. Long-term costs include continuous expenses for running and training AI models in production, server fees, cloud services and databases, maintenance, and support.
Conclusion
AI software development cost depends on many factors, including the type of solution, model complexity, data preparation and processing, the cost of model training, hardware and software costs, and operational and maintenance costs.
With so many components, it’s hard to calculate how much it costs to develop artificial intelligence apps. So, when contacting an AI development services company, ask them about the short-term and long-term costs of AI development, including potential expenses for upgrading your tech stack and scaling. This will help you plan your budget, calculate the ROI, and decide if the project warrants the investment.