How to Enable Flexible, Scalable AI with Private Cloud

Today's business leaders recognize the transformative potential of enterprise AI use cases, but capitalizing on these use cases isn't always easy. Enterprises are at varying stages in their AI journeys, each with unique challenges and infrastructure requirements based on their level of AI maturity.

Some businesses have just started to implement AI and are still determining what infrastructure they need. Others have progressed further-deploying accelerated computing infrastructure and clearly defining their data sources-and are now looking for ways to take their AI initiatives to the next level. With such diverse starting points, there's no one-size-fits-all approach for AI infrastructure. Businesses need an infrastructure solution to help them maximize flexibility as they work toward specific requirements.

Let's look at the challenges businesses face when they pursue traditional on-premises data centers and the public cloud-and how deploying a multicloud environment that incorporates private cloud can help.

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Why DIY isn't ideal for AI infrastructure

AI workloads have completely different infrastructure requirements than conventional IT. Legacy data centers often lack the capacity, power density, low-latency connectivity, and advanced cooling capabilities needed to support GPUs and other advanced AI hardware. Trying to update an existing data center to make it AI-ready is costly and complex, and most businesses don't have the in-house expertise needed to execute such an upgrade.

In addition, businesses must dedicate serious time and resources to maintain their AI environment, ensuring it continues to work effectively. As AI evolves, with datasets growing larger and models growing more sophisticated, businesses will likely struggle to keep up with these ongoing changes.

Finally, businesses also need to provide their own security-including both the physical security of the data center and the cybersecurity of the data that resides there. This can lead to serious risk, while also being complex and costly.

A do-it-yourself approach to AI infrastructure is not feasible for many enterprises.

Public cloud enables scalable AI infrastructure, but how you access it matters

For businesses looking to deploy AI infrastructure quickly, the public cloud can seem like the answer. Services are accessible on demand and businesses can scale up those services as needs change. It also means outsourcing many of the challenges involved with a DIY approach, including the technical complexity of securing and future-proofing a legacy data center environment.

That being said, there are some challenges to using the public cloud, including loss of control and unpredictable costs. Unless businesses are very careful about how they access cloud services, it's easy to fall into vendor lock-in, leaving them ill-prepared to operate in the current AI landscape. Using different applications distributed across different environments has become the norm.

While public cloud enables infrastructure scalability, it doesn't scale in a controlled manner. As businesses progress in their AI maturity, adding more users and bigger datasets, they'll likely need to consume more compute cycles and move more data into and out of their cloud environments. They won't have the visibility they need to track how their cloud environments are growing. They may soon find themselves paying high egress fees, and their cloud costs could easily get out of hand.

Businesses also need to navigate data privacy and sovereignty requirements. If they're moving data into a cloud native storage environment, they're giving up control over that data. There's a good chance that the cloud provider's shared responsibility model won't provide adequate protection to meet their compliance requirements.

What matters is how enterprises access the cloud. With the right approach, businesses can access specific services from multiple cloud providers on demand, while still maintaining datasets on private infrastructure. This allows them to ensure data privacy and more predictable costs.

Private cloud enables the right balance of flexibility and control

There is a third option that provides a happy medium between on-premises and public cloud. An AI-optimized private cloud environment allows businesses to maintain control and avoid vendor lock-in, while also eliminating the need to configure and maintain their AI infrastructure in house. By choosing Equinix and HPE Private Cloud AI, co-developed with NVIDIA, customers benefit from a leading end-to-end AI software platform running on the digital infrastructure designed to help this platform perform at its full potential.

HPE AI Essentials is a built-in data platform that allows customers to manage all their different data sources, users and applications from within the same private cloud environment. It also provides flexibility to incorporate tools from different partners, including open-source tools.

Whether delivered on-premises or at an Equinix data center, HPE Private Cloud AI also offers air-gapped management for organizations with strict data privacy requirements, multitenancy that enables businesses to collaborate and partition resources across teams, and governance capabilities to ensure data only goes where they need it to go. For businesses operating in heavily regulated industries like financial services and healthcare/life sciences, these features help unlock the full power of advanced AI use cases without undue risk.

To help keep AI costs under control, HPE Private Cloud AI, the signature offering in the NVIDIA AI Computing by HPE portfolio, allows customers to set limits on how many compute cycles they use for a particular project. This level of visibility and control allows them to scale their AI environment on their own terms and make smarter use of the resources available to them.

Equinix supports these capabilities by enabling fast, secure access to data in distributed locations. Customers can access HPE Private Cloud AI quickly in pre-provisioned environments inside select Equinix IBX® colocation data centers, choosing the location that offers the best proximity to their data sources. In addition, Equinix Fabric®, our Network as a Service solution, enables flexible, low-latency interconnection between distributed AI workloads and data sources, including those in public cloud environments.

Finally, Equinix and HPE's combined partner ecosystem offers a significant competitive advantage. The Equinix partner community is comprised of thousands of enterprises and services providers to support customers, while HPE delivers more than 75 AI use cases to customers through its Unleash AI partner ecosystem and professional services, as well as the latest NVIDIA AI Blueprints.

If there are business partners that customers need to incorporate into their AI strategies, there's a good chance they'll find those partners at Equinix and HPE, making it that much easier to jump-start their AI journeys.

To see HPE Private Cloud AI at Equinix in action, contact us to schedule a hosted trial in an Equinix data center near you. You'll be able to see for yourself just how quick and easy it is to start taking control of your AI infrastructure.

Also, view our on-demand Tech Talk Maximize your AI investment with Equinix and HPE. You'll hear from experts that will teach you how to unlock the full potential of your AI technologies and ensure optimal return on investment.