Artificial Intelligence Cost Estimation

Key Factors and Examples

Artificial Intelligence (AI) is a transformative technology reshaping many industries and organizations. The integration of AI into business processes can unlock new efficiencies, enhance customer experiences, and drive innovation. However, the journey to leveraging AI's full potential comes with its own set of challenges, most notably its cost.

Understanding the financial implications of AI implementation is crucial for any organization planning to harness its capabilities. This article explores the factors influencing AI costs, provides real-life examples of custom AI development costs, and offers insights on reducing the cost of AI initiatives.

This is part of a series of articles about machine learning engineering.

In this article:

How Much Do Artificial Intelligence Projects Cost? The Key Factors

The cost of building a custom AI project in an organization can vary widely depending on various factors. Here are the key factors that contribute to the overall cost of AI:

Development Costs

The development costs for AI are often the most significant part of the overall budget. This includes the cost of designing and building the AI system. The development costs depend on the complexity of the AI application, the technology used, and the time taken to develop the system.

For instance, a simple AI chatbot can be developed in a few weeks and may cost a few thousand dollars. On the other hand, a complex AI system, such as a self-driving car, can take years to develop and cost millions of dollars. In addition to the development costs, there may also be costs associated with testing and validating the AI system.

Data-Related Costs

Data is the lifeblood of any AI system. The quality and quantity of data can significantly impact the performance of the AI system. Therefore, the cost of acquiring, cleaning, and managing data can be substantial.

Data-related costs can include the cost of acquiring data, the cost of cleaning and preparing the data for use in the AI system, and the cost of storing and managing the data. Additionally, organizations may also need to invest in data security measures to protect the data from theft or misuse.

Infrastructure Costs

Another significant cost associated with implementing AI is the infrastructure cost. This includes the cost of hardware and software, as well as the cost of setting up and maintaining the infrastructure. Many AI systems need specialized hardware like graphics processing units (GPUs), which represent a significant added cost.

The infrastructure cost can vary widely depending on the size and complexity of the AI system. For instance, a simple AI chatbot can run on a single server, while a complex AI system may require a large data center with hundreds of servers, each with multiple GPUs. Organizations have the option of purchasing the required infrastructure or acquiring it from a cloud service provider and paying according to actual resources used.

Operational Costs

Operational costs are the costs associated with running the AI system once it is operational. This includes the cost of electricity, cooling, and other overheads. If cloud computing resources are used, the cost of those resources are usually treated as operational costs.

Ongoing operational costs can be significant. For large AI systems, they might require an entire machine learning operations (MLOps) team to manage ongoing development, deployment, monitoring and troubleshooting of production models. Therefore, organizations need to factor in these costs when estimating the overall cost of implementing AI.

Human Resource Costs

Last but not least, human resource costs can also contribute significantly to the overall cost of AI. This includes the cost of hiring and training staff to develop, manage, and maintain the AI system. AI is a complex field that requires specialized skills, and AI professionals are in high demand. Therefore, the cost of hiring and training staff can be substantial.

Related content: Read our guide to enterprise AI

Build vs. Buy: Should You Build Your Own AI Solution Or Use Existing Tools?

A critical decision that organizations face is whether to build their own AI solution from scratch or to utilize existing tools and platforms. This choice hinges on several factors, including the specific needs of the business, available resources, and the desired level of customization.

Building Your Own AI Solution

Creating a custom AI solution offers the advantage of tailoring the technology precisely to the unique requirements of the business. This approach can lead to a more effective system that aligns closely with specific business objectives.

However, the downside is higher costs and longer development time. Building a bespoke AI solution requires significant investment in human resources, technology, and time. Organizations must have the capability to manage the complexities of AI development, which includes not only the initial creation but also ongoing maintenance and updates.

Using Existing AI Tools

Opting for pre-built AI tools and platforms can be a more cost-effective and time-efficient route for many businesses. This approach allows organizations to leverage the expertise and advancements already available in the market. Existing AI solutions often come with support, documentation, and a user community, which can be valuable resources, especially for businesses without deep technical expertise in AI.

However, the trade-off can be a lack of customization. Pre-built solutions may not fit perfectly with every business's unique needs and may require some adjustments or compromises. Additionally, dependence on external vendors for critical technology can pose risks related to control, security, and future scalability.

Making the Right Choice

The decision between building an AI solution and buying existing tools depends on a careful assessment of the organization's needs, capabilities, and resources. Factors such as the level of required customization, available technical expertise, budget constraints, and time to market should guide this decision. Often, a hybrid approach—combining elements of both strategies—can be the most effective way to leverage AI technologies.

Methods for Estimating AI Costs

Top-Down vs. Bottom-Up Estimation Approaches

There are two primary methods of estimating the costs of any project, AI projects included:

  • The top-down approach begins with a broad view of the project and breaks it down into more detailed components. It is a macro-level method that takes into account the overall project goals, budget, and timeline. This approach is often used in the early stages of a project when detailed information may not be available. However, the top-down approach can lead to inaccurate estimates if not properly done, as it relies heavily on assumptions and historical data.
  • The bottom-up approach begins with the most granular level of detail and works its way up to create a comprehensive cost estimate. This method is more accurate as it considers each component of the project, but it can be time-consuming and requires a deep understanding of the project's requirements and tasks. Therefore, it is often used in the later stages of a project when more detailed information is available.

Role of Pilot Projects in Cost Estimation

One of the most effective ways to estimate the cost of an AI project is to run a pilot project. A pilot project is a small-scale, preliminary project that is used to test the feasibility and cost of a larger project.

Pilot projects help to identify potential challenges and costs that may not have been considered in the initial cost estimation. They also provide valuable insights into the project's feasibility and the potential ROI.

Perhaps the most important aspect of a pilot project is that it can help the organization get a taste of the planned AI system, how it works and the value it could provide. By allowing real users and stakeholders to work with the pilot system, the organization can understand if the full project is worth the investment, and even if so, make adjustments to ensure it is effective.

3 Examples of AI Project Costs

This section presents an overview of the financial outlay involved in various AI projects. These examples were shared by specialists in the AI development field, and are anonymized for privacy.

AI Telehealth Solution

A health technology firm started a project to enhance their telehealth network, utilized in multiple American hospitals, with video analysis features.

The AI system would allow healthcare providers to deploy facial recognition and natural language processing (NLP) techniques to assess consultation video recordings. The main goal is to refine doctor-patient interactions. The solution performs linguistic analysis of video sessions to detect changes in communication patterns, thereby providing insights into patient health and assisting physicians in devising more effective treatment strategies.

The basic version of a video or speech analysis AI platform cost around $50,000 to develop.

Intelligent Recommendation Engine

A technology startup sought to integrate AI features into a B2C platform that connects users to local service providers. The idea was to substitute search filters with machine learning (ML) algorithms that can analyze input text and generate a list of relevant service providers.

The solution was based on Amazon Web Services (AWS) analytics services. The project included not only the recommendation engine but also a managed cloud infrastructure for training, deploying, and hosting the relevant ML models.

The cost of developing, testing, and launching an initial version of the system was $35,000.

Virtual Assistant

A mid-size organization wanted to develop a custom virtual assistant. A virtual assistant can help carry out routine, day-to-day tasks. Popular virtual assistants include Cortana, Siri, and Google Assistant. However, some businesses need a custom virtual assistant that leverages company data to attend to customer queries or employee requests.

The cost of developing, testing and launching the custom chatbot was $35,000.

How to Reduce Your AI Development Costs

Identify Priority Use Cases

AI can be applied in many areas of a business, but not all applications deliver the same value. Therefore, it's important to identify the priority use cases for AI in your business.

Priority use cases are those that align with your business strategy and have the potential to deliver significant ROI. By focusing on these use cases, you can optimize your AI investments and ensure that you are getting the most value for your money.

Carefully Select Your Technology Stack

The technology stack you choose for your AI project can have a significant impact on its cost. Therefore, it's important to select the optimum technology stack that meets your project's requirements and budget.

There are many AI platforms and tools available in the market, each with its own strengths and weaknesses. Some of the most popular AI platforms include TensorFlow, PyTorch, Azure Machine Learning, OpenAI, IBM Watson, and Databricks. Each of these platforms offers a range of features and capabilities, and their costs can vary significantly—from open source solutions to low-cost API-based solutions to platforms with high license or subscription costs.

Prepare Data for AI-Driven Analysis

Data preparation is a critical step in any AI project, and it can also be a significant cost driver. It involves collecting, cleaning, and transforming data to be used in AI-driven analysis.

To reduce the cost of data preparation, it's important to have a clear understanding of the data requirements for your AI project. This includes knowing the type of data you need, the quality of the data, and how the data will be used.

Furthermore, using automated data preparation tools can help to reduce the time and effort required for data preparation. These tools use machine learning algorithms to automate the process of data cleaning and transformation, reducing the need for manual intervention.

Create an MVP Version of Your AI System

Creating a Minimum Viable Product (MVP) version of your AI system is another effective way to reduce costs. An MVP is a simplified version of a product that has enough features to satisfy early users and provide feedback for future product development.

By creating an MVP, you can test the feasibility of your AI project and gather valuable user feedback before investing in a full-scale project. This can help to identify any potential issues or challenges early on, reducing the risk of costly mistakes later.

Treat AI Implementation as a Work in Progress

Finally, it's important to treat AI implementation as a work in progress. AI is a rapidly evolving field, and it's likely that your AI project will require ongoing adjustments and improvements

By adopting an iterative approach to AI implementation, you can continuously improve your AI system and adapt to changes in the market or technology. This can help to optimize your AI investments and ensure that you are getting the most value from your AI projects.

Optimizing Your AI Infrastructure with Run:ai

Run:ai automates resource management and orchestration and reduces cost for artificial intelligence infrastructure. With Run:ai, you can automatically run as many compute intensive experiments as needed.

Here are some of the capabilities you gain when using Run:ai:

  • Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
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Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.

Learn more about the Run:ai GPU virtualization platform.