Deploy AI on-premises or in the Cloud? 10 reasons not to make a mistake!

These days, many businesses are moving their systems to the cloud from on-premise solutions. The reason for this is because of the many benefits cloud computing has to offer. Key offerings of being on the cloud include databases, infrastructure, platforms, software and storage capabilities that scale to seamlessly meet different operational requirements. One such benefit for being on the cloud is the utilization of artificial intelligence (AI) and business process automation. Will Quinn , Director of Global WMS Strategy at Infor, shares an article on Informatique News explaining the circumstances of deploying AI on the cloud versus on-premises.  Quinn notes that AI deployment methods brings to light multiple issues and makes it possible to evaluate the advantages and disadvantages while highlighting the central role of security in defining the optimal approach for companies. whose activity relies on AI. Quinn state that the Cloud represents the ideal solution in most cases for 10 reasons below, as well as the disadvantages of AI being deployed on-premises:

Benefits of AI and Cloud 

1. Cost-Effective Scaling: Cloud services facilitate cost-effective scalability of machine learning models without significant upfront investment, promoting flexibility.
2. Reduced initial investment: AI deployed in the Cloud frees companies, particularly those with limited resources, from investing in expensive hardware.
3. Great ease of deployment: the speed of deployment offered by the Cloud streamlines configuration processes, which promotes innovation and accelerates the launch of new projects.
4. Improved security: Cloud service providers are investing in rigorous security protocols to offer their customers cutting-edge encryption and authentication mechanisms.
5. Accessibility and collaboration: AI deployed in the Cloud facilitates access and encourages seamless collaboration between multiple users, thereby increasing project efficiency.
6. Compliance: adopting Cloud services that comply with current standards ensures compliance with data protection regulations.
7. Always up-to-date information: Regular updates and patches from cloud service providers reduce vulnerabilities and minimize the risk of data breaches.
8. Distributed Backup: Because data is stored in the Cloud across multiple locations, the risk of data loss due to physical disasters or hardware failures is minimized.
9. Expertise and monitoring: Cloud service providers employ security experts specializing in cyber monitoring and continuous threat response.
10. Scalability and interoperability: Integrating AI in the Cloud into existing systems seamlessly ensures seamless operation and scalability.

Disadvantages of AI deployed on-premises

• Higher initial investment  : implementing AI on-site requires significant investments in hardware, software and qualified personnel.
• Limited scalability  : Scaling on-premises infrastructure poses some challenges, especially if there is a sudden increase in computing needs.
• Maintenance and upkeep  : the responsibility for maintaining and upgrading equipment induces additional operational costs.
• Technological obsolescence  : Rapid advances in AI equipment risk making on-premises configurations obsolete more quickly than solutions deployed in the Cloud.
• Resource dependence  : Robust security requires the use of skilled operators, which puts a strain on company resources.
• Physical security concerns  : On-premises setups are vulnerable to physical threats such as theft attempts or natural disasters.

 

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