For years, private clouds pushed traditional on-prem hardware. Will the recent move towards specialized private clouds, especially for AI, be different?
I have always been suspicious of the “private cloud.” I get why the National Institute of Standards and Technology (NIST) included the term in the description of cloud computing almost 17 years ago. However, the term was quickly interpreted as a way to bundle aging on-prem server offerings to be sold as a “cloud.”
The early private clouds were nothing like a cloud. They could not scale on demand or automatically, and self-provisioning was impossible. Clearly, this was “marketecture” and most enterprises avoided it. Of course, there were other private clouds to be had, such as open source OpenStack, which is still around today. OpenStack is much better than when I first experienced it, when it was more like an engineering project than an installation.
New opportunity for private clouds
Private clouds are transforming significantly from general-purpose solutions to specialized implementations, particularly for AI. This evolution is driven by soaring investments in artificial intelligence, prompting organizations to seek dedicated infrastructures that provide a prepackaged AI ecosystem and run in their data center.
Specialized private clouds have evolved far beyond AI-focused implementations, addressing diverse enterprise needs across multiple sectors:
- High-performance computing (HPC) clouds support intensive computational tasks.
- Developer clouds streamline software development with integrated CI/CD tools.
- Database clouds optimize data management workloads.
- Disaster recovery clouds ensure business continuity.
- Edge clouds handle IoT and real-time processing needs.
- Compliance and security clouds address specific regulatory requirements.
Private clouds also focus on specific industries. The financial services sector benefits from clouds designed for high-speed transactions and regulatory compliance, while multimedia clouds optimize content delivery and streaming services. These specialized environments offer unique advantages for their target applications, providing purpose-built infrastructure, optimized performance, and industry-specific features. However, like AI private clouds, they often face similar challenges regarding flexibility, cost, and the risk of technology stagnation, making it crucial for organizations to carefully evaluate their specific needs before committing to any specialized private cloud solution.
Back to AI private clouds. Most enterprises do not know how to knit together their own technology bundle to make an AI or machine learning solution. An AI private cloud offers everything prepacked and preconfigured with the necessary development tools, designed to optimize GPU clusters and equipped with MLOps pipelines that streamline processes. However, instead of consuming this as a set of public cloud services, a bunch of boxes shows up on your loading dock that you install in your data center racks. At first glance, they offer a perfect solution for enterprises eager to dive deep into AI initiatives. However, this promising framework comes with its own set of challenges.
A careful look at the trade-offs
On one hand, these specialized clouds excel in providing purpose-built capabilities for AI and machine learning, enhancing data sovereignty and security. Reduced latency can also be a significant advantage for specific applications, allowing organizations to capitalize on real-time data processing.
Yet, the static nature of these setups presents a considerable drawback. Many private AI clouds limit technological flexibility and may require substantial investments with little room for adaptation as enterprise needs evolve. Organizations could find themselves locked into vendor solutions that might not support newer AI frameworks or tools, stifling innovation and growth.
The cost implications of moving to a private AI cloud represent another critical consideration. Public cloud providers typically operate on a pay-as-you-go model, but private AI clouds necessitate hefty up-front investments that can escalate into the millions. Hardware infrastructure can range from two to ten million dollars, and software licenses often require an annual expenditure of $500,000 to two million. Additionally, there’s operational overhead—staffing, utilities, and maintenance.
In contrast, public cloud providers eliminate the substantial upfront infrastructure investments and provide flexibility in scaling resources according to demand. The quick adaptability of public cloud environments to new technologies and pricing structures represents a significant advantage for many organizations.
This becomes an even more complex decision when you consider that over a five-year horizon, private clouds often offer an operational cost advantage over public clouds. However, you need to consider the all-in costs, including the people who maintain these systems, the cost of power, etc. These are often overlooked when making a TCO comparison between public and private cloud options.
What’s your five-year plan?
Let’s raise an essential question regarding strategic planning. As organizations are drawn to the promise of specialized private clouds, it’s vital to carefully assess performance needs, data governance requirements, and the long-term trajectory of their AI projects. The allure of enhanced control entices many organizations, yet they risk investing in static technologies that may become obsolete in the face of rapid AI advancements.
A hybrid approach is often the most practical solution. Companies may benefit from specialized private clouds for consistent workloads that demand strong data governance while also using public clouds for experimentation and overflow capacity. By the way, that is more challenging than it sounds.
Ultimately, specialized private clouds, especially those focused on AI, are increasingly indispensable in certain contexts. They are better than the private clouds of the past, which were more like scams than legit solutions. However, organizations must weigh the advantages against the drawbacks, particularly the potential limitations and costs associated with static technology infrastructures.
Here’s some general advice. If you plan on changing a lot during the next five years and your existing requirements are not at all settled, public cloud providers are likely the best solution for things like AI development, deployment, and operations. If you’re unlikely to have a lot of change within the next five years, private cloud options, such as for AI, are genuinely cost-effective, assuming that your requirements lead you there. This is another one of those “it depends” situations.
The bottom line is clear: Although specialized AI clouds have a significant role, organizations must be flexible. Starting small in public cloud environments and gradually scaling up only when there is a stable understanding of workload patterns, can mitigate risks. It’s crucial to maintain adaptability since the fast-paced nature of AI means that today’s perfect cloud solution could become inadequate tomorrow. Choose wisely and remember that ongoing change is the only constant in the digital landscape.
