The early private cloud was completely different from the cloud. It couldn’t scale on demand or automatically, and it couldn’t self-provision. Clearly this was a ‘marketing architecture’ and most companies avoided it. Of course, there were other private clouds, such as the open source OpenStack, that are still in use today. OpenStack has developed a lot over the years, but at the time it was more of an engineering project than an installation project.
New Opportunities for Private Cloud
Private cloud is transforming from a general-purpose solution to a specialized implementation solution, especially for AI. This evolution occurred as investments in AI surged and companies sought dedicated infrastructure. Dedicated infrastructure runs in data centers and provides a pre-configured AI ecosystem.
Specialized private clouds have evolved much further, going beyond AI-focused implementations and addressing a variety of enterprise needs across multiple sectors:
- High-performance computing (HPC) cloud supports intensive computational tasks.
- Developer Cloud simplifies software development with integrated CI/CD tools.
- Database cloud optimizes data management workloads.
- Disaster recovery cloud ensures business continuity.
- Edge cloud handles IoT and real-time processing requirements.
- Compliance and Security Cloud addresses specific regulatory requirements.
Private clouds focus on specific industries. The financial services sector benefits from a cloud designed for high-speed transaction and regulatory compliance, while the multimedia cloud optimizes content delivery and streaming services. These specialized clouds provide purpose-built infrastructure, optimized performance, and industry-specific features to deliver unique benefits to targeted applications. However, as with AI private clouds, they often face similar challenges around flexibility, cost, and the risk of technology stagnation, so it is important for companies to carefully evaluate their specific needs before adopting a specialized private cloud solution.
When looking at AI private clouds, most companies don’t know how to piece together their bundle of technologies to create AI/ML solutions. AI Private Cloud comes pre-configured with all the necessary development tools. It is designed to optimize GPU clusters and also features an MLOps pipeline to streamline the process. But instead of consuming it as a series of public cloud services, a bunch of boxes show up in a loading dock and companies install them in data center racks. At first glance, it offers the perfect solution for companies looking to dive deep into AI initiatives. However, this promising framework has several challenges.
Look carefully at the pros and cons
On the one hand, these specialized clouds are excellent for enhancing data sovereignty and security by providing capabilities specifically designed for AI and machine learning. Additionally, lower latency can be a significant advantage for certain applications, allowing businesses to take advantage of real-time data processing.
However, there are also disadvantages to the static nature of this configuration. Many AI private clouds have limited technical flexibility and may require significant investments while failing to adapt to changing business needs. Companies can become locked into vendor solutions that may not support the latest AI frameworks or tools, which can hinder innovation and growth.
The cost issue that arises when switching to a private AI cloud is also an important consideration. Public cloud service providers generally operate on a pay-as-you-go model, but AI-specialized private clouds require massive initial investments reaching millions of dollars. Hardware infrastructure can range from $2 million to $10 million, and software licenses often cost $500,000 to $2 million per year. Additionally, operational burdens such as manpower and maintenance arise.
On the other hand, using public cloud services does not require initial infrastructure investment and resources can be flexibly expanded according to demand. The ability to quickly adapt to new technologies and pricing structures in a public cloud environment provides significant advantages to many companies.
This becomes an even more complex decision considering that private clouds often have operating cost advantages over public clouds over a five-year period. However, the overall cost, including manpower and power costs to maintain these systems, must be considered. These costs are often overlooked when comparing TCO between public and private cloud options.
Five Year Plan for Professional Cloud
Let’s ask essential questions about strategic planning. If companies are attracted to the promise of a specialized private cloud, it is important to carefully evaluate the performance requirements, data governance requirements, and long-term trajectory of their AI project. Although many companies are tempted by the allure of improved control capabilities, the rapid advancement of AI technology cannot rule out the risk of investing in static technologies that could quickly become outdated.
A hybrid approach is often the most practical solution. Enterprises can use specialized private clouds for consistent workloads that require strong data governance, and public clouds for experimentation and capacity overruns. However, this is more difficult than you think.
Ultimately, specialized private clouds, especially those focused on AI, are becoming increasingly indispensable in certain situations. It’s much better than the private clouds of the past, which were more of a scam than a valid solution. However, companies must weigh the pros and cons, especially the potential constraints and costs associated with static technology infrastructure.
Here’s some general advice: If you’re planning a lot of change over the next five years and have no existing needs addressed, public cloud providers are likely the best solution for tasks like AI development, deployment, and operations. If you don’t think much will change in the next five years, an AI-specific private cloud option could be really cost-effective, depending on your needs. This is also one of those “it depends” situations.
The conclusion is clear. The role of AI-specialized clouds is important, but companies must respond flexibly. Risks can be mitigated by starting small in a public cloud environment and gradually expanding after a stable understanding of workload patterns. The rapidly changing nature of AI means that today’s perfect cloud solution may be inadequate tomorrow, so it’s important to remain adaptable. In a digital environment, you must remember that constant change is the only constant and choose wisely.
editor@itworld.co.kr
Source: www.itworld.co.kr
