Cloud AI vs. On-Premise AI: What You Need to Know

Summary:

• Adoption of AI is accelerating, with 72% of businesses using AI for at least one function.
• Businesses must decide between cloud AI and on-premises AI based on needs, goals, and resources.
• Cloud AI offers flexibility, scalability, and affordability, while on-premises AI provides control and security.
• Cloud AI is recommended for most organizations due to lower costs and less risk.

Adoption of artificial intelligence (AI) is accelerating, with Forbes reporting that “72% of businesses have adopted AI for at least one business function.” But as companies continue to tap into the power of AI to automate processes, enhance decision-making, and reveal new opportunities for growth, a question emerges: 

Should our business invest in cloud AI or on-premise AI?

It’s a good question, and one that often sparks a great deal of debate. Both offer distinct advantages and trade-offs, which is why businesses must evaluate their needs, goals, and resources before deciding which approach is best for their business. 

What is Cloud AI?

Cloud AI integrates AI technologies into cloud computing platforms, giving businesses on-demand access to AI-powered applications and capabilities without the need to build and maintain their own on-premises infrastructure. 

Cloud providers such as AWS, Google Cloud, and Microsoft Azure are investing in their public solutions to offer services that make it easy and affordable for companies to test AI applications in the cloud. This is important, because as tools like GenerativeAI (GenAI) gain traction, cloud AI provides the flexibility and scalability companies need to experiment with these emerging technologies and determine how to deploy GenAI capabilities in a way that plays to its strengths while also mitigating risks and avoiding significant, upfront financial investments. And if the initiatives fail to deliver value, companies can easily shut down the AI experiment without facing significant financial loss.

What is On-Premises AI? 

Unlike cloud AI which relies on a third-party cloud provider’s services, on-premises AI is deployed, hosted, and managed within an organization’s physical infrastructure, requiring the business to have data centers that house all the hardware, software, and data storage needed to support its AI applications. 

While many argue that on-prem AI offers organizations greater control over their data, this control comes at a significant cost. Investments in hardware, IT expertise, and ongoing maintenance and support are significant, and may limit an organization’s ability to scale their AI initiatives as they begin to demonstrate value. Further, because on-prem AI requires significant up-front infrastructure investments, failed AI experiments result in a much bigger financial impact to the business. 

What to Consider When Evaluating Cloud AI vs. On-Premises AI 

As organizations look to grow their use of AI applications, it’s important to consider the factors that will help them succeed. For some organizations, flexibility and scalability are a priority, while for others, enhanced security and data protection take precedence.
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Cloud AI: flexible, affordable, and scalable

When it comes to development and deployment of AI applications, cloud AI provides greater elasticity for organizations who are looking to test AI with the intent of finding the killer apps that work for their users  – and then scaling them as they take hold. Because of the nature of how it works, cloud AI enables organizations to react in the most optimal way, scaling up or down as demand for an app surges or users’ needs evolve. And because you only pay for what you use, companies can avoid making significant investments in hardware that they then need to manage until they get rid of it.

However, because cloud AI is an emerging technology, there are a few cautions organizations should consider as well. 

• Vendor lock-in: As with any move to the cloud, it’s important to consider the risks of vendor lock-in. Because cloud AI technologies are new, vendors are continually evolving their capabilities. That’s why it’s important for organizations to remain flexible so they can pivot their strategy to take advantage of new capabilities from various cloud providers as their offerings - and the organization’s needs - change over time.
• Cost fluctuations: Today, the cost to deploy AI in the cloud is relatively low, making it easy for organizations to use the cloud as a proving ground to pilot AI applications without huge, upfront investments. However, the future is unknown with regards to costs as demand continues to skyrocket. That’s why avoiding vendor lock-in and keeping an eye on cost fluctuations is key to managing cloud AI deployments in a cost-effective way.

On-Premises AI: secure and controlled

For organizations with strict regulatory requirements, highly-sensitive data, or specific performance needs, on-premise AI deployments offer greater control over their data, enhanced security, and greater ability to customize AI models to fit specific processes and workflows. And for those organizations with strong internal teams, on-prem AI can offer an easier way to work with sensitive data, especially if the company has concerns about data privacy, data protection policies, or putting their data into the cloud. 

However, while greater control over their AI applications is attractive for many organizations, especially during this experimental stage, there are downsides to on-premises AI as well. 

• Infrastructure investment: on-prem AI requires organizations to invest heavily in their on-premises infrastructure to ensure they have the hardware, software, data storage, and resources available to deploy AI applications.
• Limited ability to scale: on-prem AI lacks the flexibility and scalability that comes with cloud deployments. 
• Ongoing support: on-prem AI not only requires an up-front infrastructure investment, but it also requires human resources as well as ongoing management and maintenance until it reaches its end of life. 

Cloud AI vs. On-Premise AI At-a-Glance

The Verdict: Cloud AI is the Better Bet

AI technologies are evolving rapidly. And quite frankly, a lot of unknowns remain. But one thing is clear: unless your organization is a large compute provider, cloud AI is likely the better choice when it comes to the deployment of AI applications. 

Not only is cloud AI a more affordable option, but it's less risky, too. AI is evolving and companies are making substantial investments in AI to help it succeed. But it’s important to keep in mind that we’re still in the early stages of AI evolution.That’s why now is not the time to place a big bet and invest in a robust, on-premises infrastructure. Instead, companies should opt for a cloud environment that enables them to test AI apps, see what works, scale as the organization's needs evolve, and react in an optimal way as things change.