Rethink cloud-first: Building a hybrid cloud strategy

A few years ago, cloud-first infrastructure was the default choice for organizations looking to modernize and get value from tech investments. 

That’s no longer the case. 

As AI becomes part of every technology and workloads grow more intensive, AI demand is outstripping capacity—and a cloud-first approach may not always be the right solution. In fact, less than half (46%) of organizations are confident their use of cloud services can handle AI for the next year. 

In today’s tech landscape, leaders need to choose the right infrastructure for their workloads instead of assuming cloud-first for everything.

In this article, we cover when it makes sense to use cloud, on-premises data centers, and edge computing and how to get started with a hybrid cloud strategy.

With a hybrid cloud strategy, organizations use a variety of computing environments, like on-premises resources and cloud resources hosted by a public or private cloud provider. (They could also use a hybrid multicloud strategy, which combines on-premises resources with cloud resources from multiple cloud providers.)

Adopting a hybrid cloud approach can improve resilience, latency, and cost effectiveness, enabling organizations to drive value with cloud and AI.

One of cloud computing’s biggest benefits is its ability to scale up or down depending on demand. This makes it best for dynamic, inconsistent, or variable workloads that require flexibility and elasticity. 

This might include: 

• Training large language models (LLMs)
• Supporting real-time data and analytics
• Handling burst capacity and spikes in demand (for example, flash sales on eCommerce sites)
• Gaining access to managed AI services and other cutting-edge technologies
• Providing a base for experimentation

With on-premises (or on-prem) infrastructure, organizations manage their own hardware, software, and data in a physical location like a data center. This gives organizations full control over their data and IT infrastructure. It’s ideal for consistent, continuous workloads. 

This might include: 

• High-volume AI workloads and inference
• Anything that uses or relies on data from healthcare, finance, or other regulated industries
• Workloads dealing with intellectual property or confidential business information
• Applications that need ultra-low latency (for things like finance, trading, and robotics, where any lag can create risks or major disruptions)
• Business-critical systems that can’t rely on external cloud computing

Edge computing processes information at the edge of a network, close to the data source, instead of traveling to a cloud or data center. This greatly reduces latency and makes edge computing optimal for workloads that require almost-immediate response times and enhanced data security.

This might include: 

• Applications that rely on real-time data analytics (healthcare monitoring, autonomous vehicles, manufacturing, etc.) or otherwise require low latency (telecommunications, competitive gaming, streaming, etc.)
• Workloads with data that can’t cross geographic borders due to data localization laws or other regulations
• Workloads that require heightened security or compliance (defense contractors, financial organizations, etc.)
• Processing in remote locations where cloud or internet access is unavailable or unreliable
• Instances where it makes sense to reduce bandwidth costs

In addition to cloud, on-premises, and edge computing, a hybrid cloud approach may include other strategies.

When it comes to AI specifically, neoclouds have emerged as viable options. These GPU-as-a-service (GPUaaS) providers are built primarily for AI training and inference. Because they’re tailored to specific needs, they provide more cost-effective options for running AI workloads than cloud hyperscalers. 

Adopting neoclouds alone won’t deliver value, though. They need to be part of your cloud strategy and infrastructure, not standalone siloes.

Colocation, or colo, allows you to rent data centers, servers, space, and other facilities from another company. Compared to on-prem, this is often a more affordable option that can help you determine if creating your own data center is worth the investment. The tradeoff is that you don’t have full control over your data and infrastructure.

Even if you’ve already moved workloads to the cloud, you don’t have to keep them there. Cloud repatriation is the process of moving some or all of your workloads from a public cloud to a private cloud or on-premises data center. 

Organizations often make the switch if cloud costs have become unpredictable, they need greater data privacy, or they want more control over their AI workloads.

(Note: Only 8 - 9% of organizations fully repatriate their workloads. Most choose to optimize their workloads and move only select parts to private clouds or data centers.)

The Pluralsight 2023 State of Cloud Report found that 69% of leaders lack a cloud strategy to guide their implementation. It’s not surprising, then, that only 27% said their cloud initiatives drove more customer value. 

Don’t make the same mistake when it comes to hybrid cloud. Start with a strategy. 

Consider: 

• Workload needs. Different workloads require different infrastructure. Assess your workloads, goals, and use cases, then identify the infrastructure that makes sense for them.
• Ownership. Hybrid cloud brings various resources and systems into the mix. Determine who will own cloud, on-premises, and edge computing, respectively.
• Cost. On-premises data centers require large upfront investments, while cloud favors an ongoing, pay-as-you-go model based on usage. Evaluate your workloads to understand what is most cost-effective for your organization.
• Your people. Hybrid cloud requires tech professionals skilled in cloud, on-premises, and/or edge. Assess your team’s capabilities and ensure they have the skills they need.

Build your team’s hybrid cloud skills: 

• Hybrid and Multi-Cloud Architect Learning Path
• Designing a Hybrid Cloud in Azure
• Networking in Google Cloud: Hybrid and Multicloud

A hybrid cloud approach optimizes your IT infrastructure to provide greater value to your organization. However, simply adopting different types of infrastructure isn’t enough to ensure success. 

You need a strategy that aligns your on-premises, cloud, and edge computing resources. Without one, you create siloes, added complexity, and friction. But with one? You can truly harness hybrid infrastructure’s power and deliver cloud and AI value.

Help your teams build the cloud skills they need to manage hybrid infrastructure. Learn more about tech skill development with Pluralsight.