Is generative AI leading to a private cloud renaissance?

Compiled by Noah

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is again As a technological revolution approaches, many companies are facing a strategic choice: Should they continue to rely on the convenience of public clouds, or return to the embrace of private clouds? With the rapid development of AI technology, this decision has become more urgent.

According to Forrester’s 2023 Infrastructure Cloud Survey, approximately 79% of approximately 1,300 enterprise cloud decision-makers surveyed stated that their organizations are implementing private clouds. In addition, IDC predicts that global spending on dedicated private cloud services, including managed private clouds, will reach $20.4 billion in 2024 and will double by at least 2027.

Before 2024, IDC data shows that global enterprise private cloud infrastructure spending, including hardware, software and support services, will reach US$51.8 billion and grow to US$51.8 billion by 2027. $66.4 billion. However, public cloud providers are still a presence that cannot be underestimated. Public cloud, including the three giants AWS, Microsoft and Google, is expected to absorb US$815.7 billion in 2024.

We believe that this is not only a return of technology, but also a profound reflection on cost control, data security and corporate autonomy. Let us explore together what unknown truths are hidden behind this trend of private cloud renaissance.

1. The driving role of AI

It is undeniable that AI has driven the re-emergence of private clouds, making its value far beyond simply purchasing hardware and placing it on the data center behavior. In fact, private clouds have previously declined in popularity as public cloud providers offer capabilities that far exceed what open source private cloud systems or current enterprise hardware vendors can offer.

As AI workloads become more prevalent and complex, many organizations are re-evaluating their cloud strategies. Today, the consensus among enterprise architects is leaning toward hybrid cloud architecture.

A major factor driving this trend is the increasing need to control the rising costs associated with cloud and AI technologies. Public cloud providers proved more expensive than on-premises deployments, and this was eventually realized by CIOs who found their CFOs knocking on the door asking for explanations for cloud resource costs that were about 2.5 times higher than originally planned.

Key to this shift are private cloud platforms such as Dell APEX and HPE GreenLake (now equipped with generative AI support). In fact, most on-premises systems can enter the private cloud market with just a declaration. This "private cloud bleaching" phenomenon was a large part of the early stages of cloud computing, but has largely disappeared as the market has matured. However, it may appear again.

2. A misplaced sense of security

These platforms provide the computing power, even GPUs, needed to handle AI workloads, as well as the necessary flexibility. They also tightly control data privacy and security, although this security is often more perceived than real. In many cases, public cloud providers are able to provide higher levels of security because they have invested more in their own solutions.

The rise of AI has heightened concerns about data security, particularly around the risk that private corporate data could accidentally be incorporated into public AI models. Again, this concern stems more from perception than reality, but it’s a concern I hear often enough that it’s worth exploring. I can't imagine a scenario where a public cloud provider not only accidentally accesses enterprise data, but also uses that data to train their AI models. This would be a scandal of epic proportions. Still, many businesses find private cloud an attractive option because it allows them to keep so-called "sensitive data" in a controlled environment.

3. Challenges Emerge

Despite its advantages, private cloud is not without its challenges. For example, large-scale AI operations require specialized hardware such as GPU-powered servers. This can be costly and require extensive power and cooling systems, and businesses do not yet fully understand the new costs this will bring. In many cases, this is more complex than running these AI workloads on public cloud providers.

However, solutions are emerging, such as building private clouds inside co-located data centers offered by companies like Equinix. These data centers are specifically equipped to handle these infrastructure needs, and I think they are a better option than building your own. After all, there will come a time when we need to get out of the data center business, leaving it to public cloud providers, co-location providers, and managed service providers to provide better solutions.

So, is a private cloud a good choice for enterprises? Of course, they are always one of the options architects consider. They have their uses, and if they are more cost-effective or bring more value to the business, they should be used—with or without the involvement of AI.

My guess is that as AI technology and applications continue to evolve, shifts in cloud strategy are expected to reflect a growing preference for some private cloud alternative. This trend bodes well for a bright future for private cloud solutions. Thanks to AI, I suspect enterprise technology vendors that have seen waning interest in their private cloud offerings now have new energy.

Reference link: //m.sbmmt.com/link/5b61616b138596dfa7c219db523f73a6

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