How Much Does Artificial Intelligence Cost? The Full Breakdown

Artificial Intelligence is one of today’s most transformative inventions. Online business platforms are eagerly adopting and integrating AI technology with enthusiasm and foresight.

AI is indispensable if you’re a business owner or planning to launch an online enterprise. Whether targeting local or global markets, AI technology is crucial for paving your path to success.

When considering AI development, the next question often is: How much does artificial intelligence cost? This article will guide you through the answers and explore the various types of AI for business.

I. Understanding AI Applications You Can Develop Right Now

Firstly, determine what you aim to create. A basic AI chatbot might be free or low-cost, whereas a comprehensive, finely-tuned AI model could demand millions.

AI software development cost

Here are some common AI use cases:

• Conversational Interfaces: Chatbots and Virtual Assistants
  These systems mimic human dialogue, offering customer support and automating routine tasks. Chatbots are the most affordable AI solutions to develop and deploy.
• Predictive Analytics
  Forecasting future trends by analyzing historical data is ideal for finance, marketing, and operations. Predictive analytics and no-code machine learning are increasingly accessible.
• Image Recognition
  AI algorithms identify and classify objects in images, and they are widely applied in security, healthcare, and retail. This technology enables customers to photograph items and locate purchasing options.
• Natural Language Processing (NLP)
  This field involves processing and understanding human language, powering applications like sentiment analysis and language translation—crucial for topic modeling and text analysis.
• Recommendation Systems
  These AI systems analyze user behavior to suggest products or content, greatly enhancing user experience in e-commerce and streaming services. Social networks use recommendation systems to present engaging content.
• Autonomous Vehicles
  AI systems empower self-driving cars, using sensor data for navigation and decision-making. Companies are extending AI capabilities to various autonomous applications.
• Fraud Detection
  AI algorithms identify unusual patterns and potential fraud, which is crucial for finance and cybersecurity. Advanced AI models are increasingly used to detect fraudulent activities.
• Robotics
  AI-powered robots perform manufacturing, logistics, and surgery tasks, enhancing efficiency and precision.
• Personalized Marketing
  AI customizes marketing content to individual preferences, optimizing engagement and conversion rates. Generative AI is now enhancing targeted marketing campaigns.
• Speech Recognition
  Converts spoken language into text, supporting voice-activated systems and transcription services. Your company might require AI for one or several of these applications or even have a unique, enterprise-grade use case.

II. Key Factors Influencing AI Costs

The Type of Software You Plan to Develop

Artificial intelligence refers to any application or device that makes decisions based on the information it processes, imitating human intelligence. This includes voice assistants that comprehend natural language, security cameras that identify individuals in real-time video, and expert systems that detect tumors in CT scans. However, these AI systems’ complexity, performance requirements, and costs can vary significantly.

The Level of Intelligence You’re Aiming For

When thinking about AI, people often imagine robots or holographic avatars. In reality, most business AI solutions are narrow AI, designed to perform specific tasks such as converting text from PDFs into editable documents. Truly intelligent AI systems should uncover patterns in data with minimal human intervention, assess event probabilities, justify assumptions, continuously process new data, and learn from it.

How much does it cost to develop AI

The Amount and Quality of Data You’ll Provide

Artificial intelligence is only as effective as the data it is trained on, and its effectiveness improves as algorithms consume more data. Pre-trained AI tools, like large language models, simplify the training process and can sometimes be used as-is. However, the best results come from fine-tuning algorithms with data unique to your organization. This data may be structured, stored in relational databases, or unstructured, such as emails and videos, typically stored in data lakes.

Working with structured data is generally cheaper, especially with large amounts of information, to enhance accuracy. Unstructured data requires extra effort for organization and labeling, increasing costs. Data can be scarce in some fields, like medical imaging, due to privacy concerns. AI engineers might expand datasets, reuse algorithms, or generate synthetic data, which can raise the cost of development.

The Algorithm Accuracy You’re Hoping to Achieve

The precision of your AI solution depends on its application type and specified requirements. For example, a customer support chatbot might handle 60% of routine queries, while a delivery drone transporting medical supplies needs precise navigation using advanced computer vision. Higher accuracy and reliability directly impact your project’s lifespan and increase development costs. Additionally, AI systems will continue learning from new data alongside human experts, which may involve further training and maintenance expenses.

The Complexity of the AI Solution

Artificial intelligence is the core of a technology system that manages data flow in and out of your business application, delivering insights to users, including those without technical expertise. When considering AI costs, it’s essential to account for software development, including cloud-based backends, ETL/streaming tools, APIs for integration, and user interfaces like dashboards, mobile apps, or voice assistants. Lightweight AI, such as customer support chatbots, may require minimal infrastructure, while comprehensive AI ecosystems offering a full view of operations demand much more.

III. How Much Does Artificial Intelligence Cost in 2024

Here’s a breakdown detail of AI software development cost

Category	Details	Estimated Cost (USD)
Personnel Costs	Data Scientists: Algorithm development, data analysis. Typically 1-2 people.	$15,000 – $25,000/year per person
	ML Engineers: Model training, deployment. Usually 1-2 engineers.	$12,000 – $22,000/year per person
	Software Developers: Integration, backend and frontend development. 2-3 needed.	$10,000 – $20,000/year per person
	Project Managers: Coordination, timeline management. Usually 1 manager.	$9,000 – $18,000/year
	UX/UI Designers: User interface and experience design. Typically 1 designer.	$8,000 – $15,000/year
Data Acquisition	Data Collection: Gathering data from various sources, may include purchases.	$500 – $2,000
	Data Cleaning: Preprocessing and cleaning data for accuracy.	$1,000 – $3,000
	Data Annotation: Manual labeling of datasets for training.	$1,500 – $4,000
	Data Storage: Cloud or on-premise solutions. Monthly costs.	$100 – $500/month
Infrastructure Costs	Computational Resources: Cloud services like AWS, Google Cloud, or local servers.	$200 – $1,000/month
	Licensing Fees: Software and tool licenses (e.g., MATLAB, TensorFlow).	$500 – $5,000/year
Development Tools	IDEs and Software: Integrated Development Environments like PyCharm, VS.	$100 – $500/year
	Version Control Systems: Services like GitHub or GitLab.	$0 – $50/year
Testing and Validation	Testing Tools: Automated testing tools and environments.	$500 – $2,000/year
	Quality Assurance: Manual testing and QA processes, ensuring quality.	$3,000 – $10,000/year
Deployment Costs	Deployment Platforms: Deployment on cloud services or on-premise servers.	$200 – $1,000/month
	Maintenance and Support: Ongoing support, updates, bug fixes, improvements.	$2,000 – $10,000/year
Miscellaneous Costs	Training and Workshops: Training staff on new tools and technologies.	$500 – $2,000/year
	Legal and Compliance: Ensuring data privacy, security compliance, legal consults.	$1,000

Outsourcing in Vietnam: Outsourcing AI software development to Vietnam offers cost efficiency due to lower salaries and affordable infrastructure. The country has a skilled workforce of data scientists and engineers familiar with international standards. Its time zone allows for continuous project progress, and Vietnamese teams are agile and responsive. Government support and a stable economy further enhance its appeal. Additionally, Vietnam’s strategic location in Southeast Asia facilitates regional collaborations.

IV. How to Cut AI Expenses and Quickly Benefit from Artificial Intelligence

A recent Forbes Technology Council article points out that developing and deploying an AI solution can cost your company 15 times more than you anticipated unless you already have an efficient data ecosystem in place.

High AI development costs often arise from necessary infrastructure optimization, data integration, security, and AI management efforts. However, you can minimize these expenses by planning your project thoroughly and starting small, while keeping the bigger picture in mind. Utilizing pre-trained AI models can also help accelerate your project and allow for experimentation with artificial intelligence.

To assist you in building a cost-effective AI system and starting to see benefits immediately, the ITRex team has prepared a comprehensive guide for AI development and implementation. The main idea is to adopt an agile approach, as it can be difficult to gather all the requirements for a custom AI solution or accurately estimate costs at the beginning of your journey. This method also allows you to see a significant ROI early on, which in turn helps gain support from your company’s leadership and secure further funding.

How much does AI cost

Here’s how to manage your pilot project:

Collect Stakeholder Feedback

Before creating an AI system, engage with both internal and external stakeholders to pinpoint crucial processes and decisions that AI could enhance or automate.

In the next step, utilize frameworks like MoSCoW, RICE, or Kano to prioritize business cases that initially offer the highest value and lay the groundwork for future AI projects.

To create a flexible and cost-effective AI solution, integrate custom, open-source, and pre-built components, such as facial recognition engines, API-driven voice assistants, and cloud services for AI algorithm development.

Select the Optimum Technology Stack

To build a vendor-agnostic solution and reduce overall AI development costs, use a combination of custom-made, open-source, and off-the-shelf components, such as plug-and-play facial recognition engines, API-driven voice assistants, and cloud-based services that support the creation and training of AI algorithms. Pay special attention to UI/UX design: your future AI system should have a user-friendly interface that enables stakeholders to ask questions, get instant insights, or automate tasks without needing assistance from your IT department.

Prepare Data for AI Analysis

To help algorithms make sense of your business data, gather information, assess its quantity and quality, and bring it into a unified format. Apply various data collection, preparation, and normalization techniques. You can learn more about this in our blog post on data preparation for machine learning. Remember that identifying the right data and thoroughly preparing it for model training is critical to reducing the cost of artificial intelligence while developing a system that produces consistent results.

Create an MVP Version of Your AI System

Building a minimum viable product that supports the essential use cases is a best practice in AI development. With an MVP, you’ll be able to check the feasibility of your concept, pinpoint areas for algorithm improvement, and start scaling the system across different use cases and departments. Do not confuse an MVP with an AI proof of concept (PoC); the latter validates your idea and is meant for internal use only. However, beginning your AI journey with a proof of concept is often advisable to test your idea’s feasibility and eliminate technology barriers early on.

Treat AI Implementation as a Work in Progress

Once you start using artificial intelligence, you might not get perfect results immediately. As your AI system processes new information under human supervision, it will make more accurate predictions and become more autonomous. It’s important to continue gathering feedback from your company’s stakeholders, making necessary changes to the system, and repeating the steps mentioned above when introducing new features and use cases. This will help optimize AI development costs and address the scalability challenges of artificial intelligence.

Conclusion

Developing AI software in Vietnam offers a cost-effective solution without compromising quality. By leveraging skilled professionals and competitive pricing, you can achieve your AI goals efficiently. If you’re ready to take the next step, contact TECHVIFY for a free consultation and discover how we can provide the best AI software development services tailored to your needs.