Computer Hardware

On-Premise vs Cloud AI Solutions

June 9, 2021 • 4 min read

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3 Reasons to Choose On-Premise AI Solutions Instead of the Cloud

Cloud-based artificial intelligence (AI) solutions are ideal for many organizations. It saves them from the hassle of building their own data center and dealing with all the complications of hardware configuration and software stacks. Even a startup can easily use infrastructure that someone already built if they want to test the waters before development.

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On-premise infrastructure solutions; Credit: Unsplash

There are limitations to cloud-based AI, however: the app you develop in a cloud solution will have to run there for as long as the contract dictates. It’s like renting a home, rather than owning one. 

On-premise AI solutions, on the other hand, allows your applications and data to stay on the hardware as long as the business needs it. Here are three reasons why an on-premise solution may be the better option for you:

1) Flexible Scalability

In our article on hardware requirements for AI, we discussed how it’s not feasible to run any type of AI-driven project on an off-the-rack computer because of how demanding the computational loads are. The same is true for the software; you need scalable software that can be upgraded and managed to keep up with changing demands.

Although cloud resources can be adjusted to accommodate specific requirements, there is often too much software clutter within hardware stacks that limit scalability. An on-premise solution, on the other hand, gives you complete control over the infrastructure so you can update it whenever you need to. It's also much more efficient to store and manipulate data locally, rather than transferring them back and forth on a cloud solution.

2) Reduced Long-Term Costs

Cloud hosting may seem to be the more cost-effective choice in the short term because it's pay-as-you-go. AI projects, however, require massive computation power and regular network updates so the cost of cloud services will increase with time, especially since many AI projects require you to run them 24/7 as you train and analyze data. This is why in the long run, an on-premise solution would cost less; it eliminates the need for renewing contract terms and you can deploy as many applications as you need.

The one-time payment for on-premise AI may seem intimidating, along with the responsibility of storing, maintaining, and securing the hardware. However, if you hire consultants to build your organization’s on-premise AI solution, they can solve circuit board layout challenges and create versatile systems for uninterrupted service. For as little as $3,700, workstations can be tailored to your specific needs and you would have additional tech support for managing your systems.

3) Stronger Protection

Data privacy is a major concern nowadays; losing the data you’ve gathered about your customers or other businesses can harm your organization’s reputation and credibility. Choosing a cloud solution means leaving the responsibility of handling data with your provider. You won’t know where the data is stored or how often it’s backed up.

On-premise hosting would give you full control over the data stored locally and eliminate third-party handlers. While there is always a risk of getting hacked, it would be much easier to prevent and handle security breaches within your own business.

Sometimes, choosing between cloud or on-premise hosting is not a one-time decision. You’re free to change your mind later on. Still, it’s usually best to begin with on-premise solutions if you’re still growing your data, so you won’t need to transfer hundreds of terabytes later on.

Have any questions? Feel free to contact us at any time.

Exclusively written for SabrePC by Alexis Stanley


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on-premise

cloud

aws

deep learning

workstation

server

ai

data security