Custom AI Development vs. Off-the-Shelf AI Solutions

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When it comes to implementing artificial intelligence (AI) in business processes, many organizations struggle with whether they should develop a custom AI solution or buy an AI application off the shelf. Both options have their pros and cons, and it can be challenging to decide which is the best route for your organization.

This article will break down AI development and off-the-shelf solutions and help you decide which option is right for your business. 

Also see: Edge AI vs. Cloud AI: Which is Best for Your Business? 

What is AI, and How Can Your Organization Benefit From It?

AI is an advanced algorithm that completes tasks that ordinarily require human intelligence, such as visual perception, natural language processing, and decision making. AI solutions begin with developing a machine learning model and then training it on data. The AI system can then make predictions or recommendations based on the data input.

It may also come as a surprise that the concept of AI has been around since the 1950s. Artificial intelligence has gone through its share of booms and busts, but it is currently experiencing hyper-growth due to advances in computing power and data storage, in addition to becoming affordable enough for widespread adoption.

AI development has led to many business benefits, such as:

  • AI can help automate repetitive tasks, freeing employees to focus on more strategic work.
  • It can also help companies make better decisions by providing employees with data-driven insights.
  • AI can also improve customer service by providing more personalized and responsive interactions.
  • Conversational AI, in particular, is becoming more popular as it can help companies automate customer service tasks such as answering FAQs, scheduling appointments, and providing support.
  • Predictive analytics is another area where AI can be beneficial. It can help organizations make better decisions by analyzing data and predicting future trends.

Custom AI Pros and Cons

Developing a bespoke AI solution means building machine learning models from scratch and training them. You can do this in-house or outsource the task to a third-party AI development company. The end result is custom algorithms and a proprietary API.

Advantages of Custom AI 

Complete AI Development Control

When you create a bespoke AI solution, you have control over the entire process. You can decide how your data is used and what happens to it.

Ownership of Data

With a custom AI solution, you also own the data used to train the machine learning models. This aspect is important as it gives you control over how the data is used and who has access to it.


An in-house AI solution can be more flexible than an off-the-shelf solution, as you can tailor it to your specific needs.

AI Solutions for Specialized Data

If you need your program to deal with a highly specialized dataset, a custom solution is crucial since there’s less of a chance that there exists a ready-to-use solution for it. Instead, an off-the-shelf solution would generate poor results not suited to your specific problem.

Greater Security

There’s a saying often credited to Napoleon Bonaparte, “If you want something done right, do it yourself.” The same is true when it comes to AI and security. Off-the-shelf solutions may be convenient, but they’re also one-size-fits-all. That means they’re not always tailored to your specific needs and they may have gaps that savvy attackers can exploit. On the other hand, AI solutions built in-house are designed specifically for your organization and your unique security challenges. That means they can be more effective at identifying and responding to threats.

Disadvantages of Custom AI


One of the biggest disadvantages of developing a bespoke AI solution is that it can be very expensive. Not only do you need to pay the AI development company, but you also need to pay for the data and computing resources required to train the machine learning models.

Development Time

Developing an AI solution in-house is can take a long time – months, possibly years for full maturity. This is especially true if you don’t have AI expertise on staff.

Need for AI Expertise on Staff

If you want to develop a custom AI solution, you need to have AI experts on your team. This can be difficult and expensive, as AI experts are in high demand and command high salaries. Artificial intelligence experts in the US earn an average of $110,971 per year or $53 per hour, usually more. The top 10 percent earn more than $234,000 annually – and often quite a bit more. 

Off-the-Shelf AI Pros and Cons

The alternative to developing a custom AI solution is to buy a ready-made AI solution. This is a software application that has been developed by another company and is then sold to customers. There are many different AI software categories that serve a particular purpose. Below are some of the most common off-the-shelf solutions:

  • AI and Machine Learning Operations (MLOps) Software. Examples include IBM Watson Studio, Databricks Lakehouse, and InRule.
  • Conversational Intelligence Software such as Zendesk Support Suite, Intercom, and Birdeye.
  • Data Labeling Software such as Super Annotate, V7, and Amazon Sagemaker.
  • Data Science and Machine Learning Platforms. Examples include MatLab, RStudio and RapidMiner.

Advantages of Off-the-Shelf AI

Low Cost of Development

One of the main advantages of an off-the-shelf AI solution is that it is much cheaper to develop than a custom solution. You don’t need to make a large initial investment for AI development or data resources.

Quick Deployment

A ready-made solution can be deployed quickly. Onboarding and configuration are typically simpler and faster than developing a bespoke platform.

High Prediction Quality for Generic Data Cases

Off-the-shelf AI solutions are often very accurate for standard, well-understood data sets. This is because they have been trained on large, high-quality datasets. Therefore, if your data is similar to these datasets, an off-the-shelf AI platform is likely to be very accurate.

Low Maintenance

Once a ready-made product is up and running, it requires relatively little maintenance. This contrasts with custom AI solutions, which often require constant tuning and adjustment.

Disadvantage of Off-the-Shelf AI Solutions

Lack of Flexibility

Off-the-shelf AI solutions can be quite inflexible. This is because you are limited to the features and functionality that the AI software vendor provides. If you need something outside of what is offered, you will likely need to develop a custom solution.

Poor Performance on Edge Cases

Ready-made AI applications can perform poorly on data sets different from the ones it was trained on. This is because AI models are only as good as the data they are trained on. So if your data is different from the data used to train the AI model, then the AI model is likely to perform poorly.

Security Risks

An off-the-shelf AI solution can pose security risks. This is because you are trusting the AI software vendor with your data. If the vendor’s systems are breached, your data could be compromised.

Rising Costs as the System Grows

As your system develops over time, the costs of an off-the-shelf AI platform can start to increase. This is because you will need to purchase more licenses or subscriptions. Additionally, you may need to pay for more support or training as your system grows.

Also see: Data Science Certifications 2022

Custom AI vs. Off-the-Shelf: Which to Choose?

There are many factors to consider when deciding whether to develop a custom AI solution or buy an off-the-shelf AI solution. Some of the most critical factors include cost, flexibility, performance, and security. Ultimately, the best decision for your organization may depend on budget; generally, large enterprise uses custom-made AI, while the small and mid-market sectors use off-the-shelf solutions. 

Kihara Kimachia
Kihara Kimachia
Kihara Kimachia is a writer and digital marketing consultant with over a decade of experience covering issues in emerging technology and innovation. In addition to appearing regularly in Enterprise Networking Planet, his work has been published in many leading technology publications, including TechRepublic, eSecurity Planet, Server Watch, Channel Insider, IT Business Edge, and Enterprise Storage Forum.

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