AI Costs - What solutions are available for your budget?

From autonomous vehicles to personalized recommendations on streaming platforms: Artificial intelligence (AI) is permeating nearly every industry and influencing numerous areas of our lives. But while the benefits are obvious, there are also hidden costs behind AI applications.

In this blog post, we take a closer look at the costs of AI for you and provide valuable knowledge along the way. We take a closer look at different types of costs, compare providers and different solutions, and clarify the question of what factors make up the costs of AI.

The most important facts about AI costs in brief

  • Artificial intelligence is helpful in many areas of life, industries and departments. However, AI also incurs costs that vary greatly. Depending on the provider, project scope and specific requirements, there are major differences. Therefore, it is important to obtain individual price quotations.
  • AI costs arise in a number of areas, including data acquisition and cleansing, hardware and infrastructure, personnel costs, cloud services, research and development, employee training, and legal compliance.
  • There are methods to increase the cost-effectiveness of AI, including cost-benefit analysis, use of existing resources, use of open-source tools and frameworks, automation of tasks, and monitoring and optimization of AI systems.
  • Several vendors and solution models exist in the market for every budget, including Google Cloud AI, Microsoft Azure Machine Learning, Amazon Web Services (AWS), IBM Cloud, UiPath, Automation Anywhere, TensorFlow, PyTorch, Datadog, Prometheus, DataRobot, and Konfuzio.

What is Artificial Intelligence?

AI definition

Artificial Intelligence (AI), more commonly used in this country as Artificial Intelligence (AI), is the umbrella term for human-like intelligence capabilities provided by machines. This includes the ability to acquire and use knowledge, solve problems, make decisions, understand language and simulate human-like abilities such as seeing, hearing and learning.

In essence aims artificial intelligence aims to design machines to mimic human-like thought processes and behaviors. This is achieved through algorithms, machine learning, and neural networks that enable computers to learn from data and recognize patterns. This pattern recognition is critical to the ability of AI Systems, accomplish tasks and make human-like decisions.

Everything about the emergence of AI and detailed background information can be found in our continuing blog post: What is AI?

Subcategories of AI - areas of use and application examples

Artificial intelligence is a broad field and includes several subcategories. To develop an understanding of the scope, we present the most important ones:

Weak AI (Narrow AI)

Weak AI, also known as Narrow AI, focuses on the development of AI systems designed for specific tasks or limited task domains. These systems are specialized for their respective applications and offer impressive performance in these areas.

Fields of application and application examples

  • Public health: An AI system for the diagnosis of skin diseases. It analyzes photos of skin lesions and detects skin cancer or other skin diseases early. Read here more to AI solutions for the healthcare industry.
  • finance: An AI chatbot for customer support at Banks. He handles customer inquiries, reviews transactions, and answers frequently asked questions. Read more about how Banks Process Customer Data Efficiently.

Strong AI (General AI)

Strong AI, referred to in English as General AI, is the ultimate goal of AI research. This form of AI strives for a broad understanding of human intelligence and is predestined to handle a wide range of tasks that normally require human intelligence. Imagine a computer that not only writes text and solves math problems, but also creates creative works, makes moral decisions, and continuously improves itself. Although we have made progress towards strong AI, it remains an ambitious goal so far and has not yet been fully achieved.

Fields of application and application examples

  • Education: A General AI tutor who helps learners with a wide range of school subjects and exam preparation. He creates individual learning plans, evaluates exams and adapts to the needs of each student.
  • Research and development: A general AI researcher who generates new scientific knowledge and solves complex problems across multiple disciplines. He hypothesizes, conducts experiments, and writes research papers - all with such a low error rate that humans are highly unlikely to ever achieve.

Machine Learning (ML)

Machine learning is one of the most widely used subcategories of AI. This is a technique that can be Algorithms used to learn from data and make predictions or decisions. This technology has applications in many fields, from the medical diagnostics through financial forecasts to language and Image recognition. Machine learning enables computers to recognize patterns and relationships in data and use these insights to solve problems.

Fields of application and application examples

  • Medicine: An ML model for predicting disease outbreaks. It analyzes health data and environmental factors to predict the occurrence of diseases such as influenza or COVID-19.
  • Finance: An ML algorithm for credit risk assessment. It scores credit applications based on credit history and financial data to determine default risk. Read here more on the subject of KYC procedures.

Deep Learning,

Deep learning is a special machine learning technique that is based on neural networks based with many layers. These layers create the ability to recognize complex patterns and abstractions in data. Deep Learning has made breakthrough advances in tasks such as image recognition, Speech Processing and autonomous navigation achieved.

Fields of application and application examples

  • Autonomous vehicles: A Deep Learning model for road traffic detection. It analyzes real-time image and sensor information to recognize traffic signs, other road users, and pedestrians.
  • Speech Processing: A Deep Learning system for automatic translation of texts. It analyzes texts in one language and translates it another language, preserving the meaning and context.

Types of AI costs

AI costs arise in many different areas. We present some of them below:

  • Data acquisition and cleansing: High-quality data is at the heart of any AI application. The acquisition and cleansing of this data are cost-intensive.
  • Hardware and infrastructure: High-performance hardware, including graphics processing units (GPUs) and tensor processing units (TPUs), is essential for training AI models. The maintenance of these Infrastructure and the Energy costs are also to be taken into account.
  • Personnel costs: The potential recruitment and retention of talented Data Scientists, machine learning engineers or AI researchers is expensive because the demand for these professionals is high.
  • Cloud services: Many companies use Cloud services for their AI models, which, with a few exceptions, generates regular fees.
  • Research and development costs: Ongoing research into new algorithms, techniques and applications requires investment in development teams and resources. These costs are added to the price of AI offerings. Do you want to increase your Present AI expertise? The Konfuzio marketplace offers the ideal springboard.
  • Employee training: After the successful introduction of AI in companies, it is important to train employees appropriately. This includes not only the technical understanding of AI applications, but also the development of skills to Collaboration with AI systems.
  • Legal and ethical compliance: The compliance with Privacy policy and ethical guidelines potentially causes additional costs, especially when violations occur.

Methods to increase the cost-effectiveness of AI.

It's important to understand, plan and manage the costs of AI to get the maximum benefit from AI projects. Investing in AI pays off in the long run, but smart budgeting and resource allocation are the foundation for business success. The following tips will help you increase the cost-effectiveness of your AI projects without sacrificing quality and performance.

Cost-benefit analysis

Conducting a cost-benefit analysis for AI projects is extremely useful up front. The investments in AI should pay off in the long term, be it through cost savings, increases in sales or improvements in efficiency within the company.

Use existing resources

Before you purchase new hardware or software, see if you can use existing resources more effectively or turn to AI solutions that integrate with your existing IT infrastructure. Konfuzio, for example, is known for seamless integration. You may already have powerful computers or servers that can be upgraded for AI tasks. Reusing or combining resources brings significant cost savings.

AI applications, solution models and providers in comparison

The following list will give you a rough overview of what is available on the AI market. Also interesting: Our blogpost about 10 AI startups you should know about in 2023

KI Cost Comparison

Transfer Learning

Transfer Learning is a method that uses pre-trained AI models and adapts them to the specific requirements of your project. This saves time and computational resources compared to the Training of a model from scratch.

Google Cloud AI: Provides comprehensive transfer learning and AI modeling services.


  • Comprehensive servicesGoogle Cloud AI offers a wide range of AI modeling services, including pre-trained models, data processing tools, and machine learning infrastructure. This enables companies to develop diverse AI applications.
  • Integration with Google ecosystem: Integration with other Google products and services such as BigQuery, TensorFlow, and Google Cloud Platform provides seamless capabilities for data processing and analysis, and for scaling AI applications.


  • Complexity: The variety of services and tools offered may be overwhelming for newcomers to AI development. The complexity of the platform requires time and resources to learn and implement.
  • Costs: Using Google Cloud AI becomes costly depending on the scope of the project, especially if AI models are trained or run on a large scale. Companies should carefully monitor and budget for costs.

Microsoft Azure Machine Learning: Provides powerful tools and resources for Transfer Learning in the cloud.


  • Powerful tools and resources: Microsoft Azure Machine Learning provides powerful tools for AI model development, including automated machine learning algorithms, data processing and integration with Azure cloud services.
  • ScalabilityAzure enables easy scaling of resources, which is ideal for organizations with variable AI workloads. Resources can be added or removed as needed to meet current demand.


  • Learning curve: Similar to Google Cloud AI, the platform has a steep learning curve due to its variety of services and tools. New users need time to learn the ropes.
  • CostsUsing Azure Machine Learning is expensive, especially when large amounts of data need to be processed. Transparency and monitoring of costs are important to prevent budget overruns.

Cloud computing and pay-as-you-go models

The cloud offers scalable resources and makes it possible to pay only for the computing power actually used. This is particularly useful for companies with variable AI workloads, as they do not need to purchase expensive hardware.

Amazon Web Services (AWS): Enables scalable cloud resources and flexible billing models.


  • Scalability: AWS has a wide range of cloud resources in its repertoire that can be easily scaled according to demand. This allows companies to remain flexible in their resource customization and save AI costs when demand decreases.
  • Extensive services: AWS offers a wealth of cloud services and tools for various use cases, including computing, storage, databases, AI and more. Customers have access to a wide range of solutions.


  • Cost complexity: Due to the multitude of services and options, cost planning proves to be a complex undertaking. Companies should pay careful attention to how they use their resources to avoid unexpected cost increases.
  • LicensingSome AWS services require separate licenses or fees for certain features. This consequently increases the cost of using these services.

IBM Cloud: Offers Cloud services and pay-as-you-go options for businesses.


  • Pay-as-you-go model: IBM Cloud offers pay-as-you-go options that allow companies to pay only for the computing power they actually use. This helps control costs and prevents excessive spending.
  • Hybrid Cloud Solutions: IBM Cloud offers hybrid cloud options that enable seamless integration of on-premises systems and cloud resources. This is particularly useful for companies with complex IT infrastructures.


  • Limited range of functionsCompared to AWS, IBM Cloud offers a more limited choice of AI services and tools. This limits the flexibility in choosing solutions.
  • Market shareAWS has a larger market share and user base than IBM Cloud. This has implications for community support and expertise availability.

Task automation

AI is particularly widely used to perform many repetitive tasks, including automate marketing processes and thus optimize processes. As a result, employees are assigned to more demanding activities, which in the long term leads to increased efficiency and cost savings in the company.

UiPath (for Robotic Process Automation): Specialized in the Automation of business processes and repeated tasks.


  • Ease of use: UiPath is characterized by a user-friendly interface that makes it possible even for non-technical people, Process automations to create and manage.
  • Robust community and ecosystem: UiPath has a strong community of developers and a wide range of pre-built automation modules that accelerate development.


  • Costs: The cost of UiPath turns out to be high depending on the size of the company and the scope of automation, especially if additional modules or support are required.
  • Complexity in complex scenarios: In very complex automation projects, the complexity of the workflows and the maintenance effort is likely to increase.

Automation Anywhere (for RPA solutions): Provides RPA-platforms for the Task automation available in companies.


  • Scalability: Automation Anywhere offers scalable solutions used by small to large companies to automate work steps and processes.
  • Wide range of integration options: The platform enables integration with a wide range of applications and systems, facilitating the automation of complex workflows.


  • Complexity: In complex automation projects, the development and maintenance of botworkflows are complex and time-consuming.
  • Costs: The cost of Automation Anywhere is potentially prohibitive for smaller businesses or individuals, especially if they need additional features or support.

Open source tools and frameworks

Many powerful AI Tools and libraries are Open source and available free of charge. These resources are optimal for reducing development and licensing costs.

TensorFlow (from Google): A leading open sourceFramework for machine learning and deep learning.


  • Wide distribution: TensorFlow is one of the most widely used deep learning frameworks and has a large and active user and developer community. This means that there is a wealth of resources, documentation and support.
  • TensorBoard: TensorFlow includes a powerful tool called TensorBoard, which is used to visualize models and training results. This facilitates the monitoring and optimization of models.


  • Steep learning curve: A frequently mentioned disadvantage of TensorFlow is that it has a steep learning curve for beginners. The API is complex, which makes it difficult to learn.
  • Improved abstraction: Earlier versions of TensorFlow had implied a cumbersome API. Although this has been improved in TensorFlow 2.x, some old habits are hard to discard.

PyTorch (from Facebook): A popular open source framework for deep learning and neural networks.


  • Dynamic calculation graphsPyTorch uses dynamic computation graphs, which makes model development and debugging more flexible and intuitive. It is especially useful for researchers and AI developers who change models frequently.
  • Community and researchPyTorch has a strong presence in academia and is often favored by AI enthusiasts in the community. This indicates that it responds quickly to new technologies and research results.


  • Poorer production efficiency: In the past, PyTorch had disadvantages over TensorFlow in terms of production efficiency and model deployment. Although this has improved, it still lags behind TensorFlow in this area.
  • Smaller community: Although the PyTorch community is growing, it is still smaller than that of TensorFlow. As a result, there are fewer resources and support, especially for less common use cases.

Monitoring and optimization

Regular monitoring of your AI systems allows you to identify and fix resource waste. By optimizing the algorithms and models, you increase efficiency and reduce operating costs.

Datadog: Offers comprehensive monitoring and alerting services for IT infrastructures.


  • Comprehensive monitoringDatadog offers a wide range of monitoring capabilities that enable organizations to control their entire IT infrastructure in real time. This includes application performance, servers, cloud services, networks and more.
  • Powerful alerting: The platform has powerful alerting capabilities that allow users to set custom alerts based on various metrics and events. This enables proactive troubleshooting.


  • CostsDatadog requires a hefty wallet, especially for organizations with extensive monitoring needs. The base cost of the AI tool increases with the number of resources monitored.
  • Complexity: Due to the large number of functions and options, Datadog is rather unsuitable for monitoring beginners and also involves a steep learning curve.

Prometheus: A powerful open source tool for system monitoring and alerting.


  • Open source: Prometheus is an open source tool, which means that it is available for free and supported by an active developer community.
  • ScalabilityPrometheus is scalable and can be used in environments with a large number of systems and resources to be monitored. It can be easily adapted to different requirements.


  • Configuration complexity: The configuration of Prometheus is complex. Setting up alerts and customizing queries requires technical know-how.
  • Limited functions: Although Prometheus is a powerful monitoring tool, it lacks some advanced features available in paid AI solutions. This may be a hurdle for more complex requirements.

All-in-one solution with expert support

Consultation with AI experts increases the likelihood of entrepreneurial success enormously and leads to the development of effective strategies for cost savings in the long term. External experts often open up new perspectives and draw on best practices.

DataRobot: An all-in-one AI tool that combines automation and machine learning models for predictive analytics and Data science offers.


  • AutomationDataRobot creates a high level of automation for model development. Users thus create predictive models quickly and efficiently, even without extensive data science knowledge.
  • Versatility: It can be used for a wide range of applications in various industries. From marketing analysis to financial forecasting, DataRobot offers versatile applications.


  • Costs: DataRobot is expensive to use, which is especially challenging for small businesses with limited budgets.
  • Limited customization options: While DataRobot enables rapid model development, customization for specific business needs is limited.

Konfuzio: A leading provider of AI technology and consulting services that helps companies implement AI and offers customizable solutions for businesses of all sizes.


  • Customized solutions: Konfuzio specializes in customizable AI solutions tailored to individual business needs. This enables precise adaptation to complex and fast-moving business requirements, growing with them as needed.
  • Expertise and advice: In addition to advanced technologies, Konfuzio also offers comprehensive consulting and implementation support for all AI projects. Companies benefit from the expertise of the established provider, which numerous Success Stories underpin.


  • Costs: Customized solutions incur higher costs than solutions with fewer functions and application areas, depending on their scale. The exact cost depends on the specific requirements of the project. Consult an expert about the KI costs advise!
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AI costs at a glance

Please note that the table compares general information and actual costs vary significantly depending on the project and specific requirements. It is advisable to contact vendors directly to obtain accurate pricing information for your specific requirements.

Transfer LearningGoogle Cloud AIThe cost varies depending on the project and the amount of usage, starting from 0.10 USD per hour for instances. With the help of the Price calculator track costs based on location, workload, and other variables.
Microsoft Azure MachineCosts vary depending on the project and scope of use. Calculate You can enter your estimated hourly or monthly
Cost of using Azure.
Cloud computing and pay-as-you-goAmazon Web Services (AWS)Prices depend on the infrastructure and services used. Billing is per usage. Use of an initially free quota is available. Depending on the product follow three different types of offers.
IBM CloudCosts are project and use specific.
Task automationUiPathThe costs depend on the number of robotic process automations and the selected license models.
Automation AnywhereThe costs vary depending on the scope and the selected license model.
Open source tools and frameworksTensorFlow (from Google)Free of charge (Open Source)
PyTorch (from Facebook)Free of charge (Open Source)
Monitoring and optimizationDatadogCosts vary depending on the number of users and functions. You can find an overview with starting prices here.
PrometheusFree of charge (Open Source)
All-in-one solution with expert supportDataRobotCosts vary depending on usage and services
KonfuzioCosts vary depending on the project and requirements. Take Contact the team of experts and receive a non-binding consultation and offer.
Status 09/2023

The AI of Konfuzio

Konfuzio specializes in tools for the development of AI-based software for automated Document processing specialized, simple and secure in the cloud or for installation on your own servers. With data protection within the EU and reliable Text recognition in more than 100 languages, Konfuzio's AI technology is therefore a true all-rounder and supports you in your digital transformation.

Data is efficiently extracted from documents extracts and validated. Thanks to machine learning and deep learning, the AI software goes beyond partial process automation and offers end-to-end automation. Users can train the AI so that it learns with each document and generates real added value from various data.

Why is Konfuzio the right choice from the AI provider pool?

Konfuzio not only stands out with transparent pricing, but also offers customized solutions that are tailored exactly to your company's specific needs. The team at Konfuzio not only recognizes that every business has unique requirements and an individual budget, but has been actively practicing this philosophy in their daily work and consulting for years.


Taken as a whole, it is indisputable that the implementation of AI brings significant benefits. When used responsibly, AI helps solve complex problems, drive efficiencies and develop innovative solutions. However, the expenses, both financial and social, must be properly considered. Careful consideration of the costs and benefits of AI is critical to ensure that the technology delivers both economic and societal benefits over the long term.

Are you planning to introduce AI in your company or would you like to know more about the costs of AI? The professionals at Konfuzio will advise you in detail. Contact us at any time via the Contact form.

Charlotte Goetz Avatar

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