As organizations continue to evolve in the era of AI and advanced analytics, relying solely on a single-cloud strategy is no longer enough to meet the demands of data-intensive workloads.

The need for more flexible, secure, and high-performance cloud architectures has led to the adoption of hybrid cloud and multi-cloud environments. However, simply deploying these environments isn't enough: enterprises must also ensure secure, reliable interconnectivity and data governance across different cloud platforms and regions.

Beyond cloud infrastructure: the limitations of legacy cloud models in AI deployments

How do legacy cloud strategies increasingly fall short in addressing the unique requirements of AI-driven workloads? The primary challenges that can be seen here include:

Latency: AI applications require real-time responses. For instance, in applications like real-time fraud detection or autonomous vehicles, latency can be a critical factor. Organizations need to consider edge computing solutions - processing data closer to where it's generated – or optimize cloud infrastructure to reduce latency. Cloud on ramps in colocation data centers also offer dedicated, low-latency connections to the cloud, which is important for applications requiring high data volumes.
Data Gravity: AI relies on vast datasets. Moving this data to the cloud can be slow, costly, and inefficient. Data Gravity, coined by Digital Realty, refers to the fact that as data accumulates, it becomes increasingly difficult and expensive to move. Organizations must consider strategies for managing Data Gravity, such as processing data closer to its source or using high-speed data transfer services.
Security, privacy, and compliance: Regulations often restrict where and how sensitive data must be processed. Public cloud alone is often not enough to meet these needs. Enterprises must implement robust security measures, including data encryption, access controls, and compliance monitoring, to ensure their hybrid cloud environments meet regulatory requirements.
Specialized hardware: Training and deploying AI models requires high-performance hardware, such as GPUs optimized for parallel processing, that may not be adequately supported in general-purpose cloud environments. Businesses may need to invest in specialized hardware or partner with cloud providers that offer AI-optimized infrastructure (GPU-as-a-Service).
Cost: Running AI workloads continuously in the cloud can become costly and unpredictable, as workloads and datasets need to scale quickly. Enterprises need advanced solutions to optimize costs while maintaining performance and control. Optimizing efficiency with the right infrastructure partner can make it easier to manage costs.

How to address these challenges: Digital Realty’s Pervasive Datacenter Architecture (PDx)® methodology provides a structured framework for assessing and mitigating Hybrid IT and AI challenges. By using the PDx® use cases, enterprises can model where infrastructure should reside, how it should connect, and which deployment strategies optimize performance across hybrid environments.

The rise of hybrid cloud: flexibility and resilience

Enterprises are increasingly adopting multi-cloud strategies to gain flexibility and resilience. Hybrid cloud computing allows for workload placement based on specific security, latency, and compliance requirements. By leveraging multiple cloud services, companies can choose the best services for their needs, avoiding vendor lock-in and improving overall resilience.

The benefits of hybrid cloud include:

Optimized workload distribution: Transferring workloads between private and public clouds to optimize performance and cost.
Redundancy: Using multiple cloud services and regions to ensure high availability and minimize downtime.
Best-of-breed approach: Selecting the most suitable cloud services for specific business needs, rather than relying on a single vendor.

Securing and orchestrating data across hybrid environments

Protecting data across hybrid environments is critical. Secure interconnectivity and unified control planes are necessary to ensure data flows correctly between applications and clouds without compromising compliance or performance. It’s essential to put in place robust security measures and data governance policies to manage hybrid cloud environments effectively.

Some key considerations for securing hybrid cloud environments include:

Data encryption: Encrypting data both in transit and at rest to prevent unauthorized access.
Access controls: Implementing strict access controls to ensure that only authorized personnel can access sensitive data.
Compliance monitoring: Continuously monitoring compliance with relevant regulations and standards.

Data centers as the backbone: enabling seamless global operations

Hybrid cloud orchestration must originate at the data center level, with inter-regional connectivity supporting seamless global operations. Colocation providers play a crucial role in enhancing cloud performance and reliability by positioning data closer to cloud gateways, reducing network latency. Solutions like Digital Realty's ServiceFabric® on PlatformDIGITAL® provide secure, direct connections to major cloud service providers, ensuring optimal performance for demanding applications.

PlatformDIGITAL® provides the foundation of our PDx® framework, enabling enterprises to deploy standardized architectures validated through PDx use cases. Each use case defines a repeatable pattern—such as hybrid cloud enablement, AI model training, or data compliance—that accelerates time to deployment and ensures consistency across global sites.

Building a business-ready hybrid cloud: key considerations

If you’re planning to build a robust hybrid cloud environment, you should carefully consider the following key steps:

Find the right cloud for the right workload: Allocate workloads to the right environments based on their specific needs. This involves evaluating cloud providers' capabilities, including infrastructure, security measures, and support services.
Use resilient architecture for direct cloud access: Implement direct connectivity services to mitigate performance risks associated with sub-standard connections. This can include services that provide secure, direct connections to cloud service providers.
Reduce geographical reach to cloud servers: Position cloud servers closer to cloud on ramps to minimize latency. Colocation providers can play a vital role in this process by placing data near cloud gateways.
Scale efficiently: Work closely with cloud providers to ensure that the bandwidth and redundancy options needed are in place. This may involve adopting autoscaling capabilities to handle spikes in demand.
Monitor performance continuously: Establish clear performance benchmarks and metrics to identify and address issues proactively. Monitoring tools can help highlight deviations from expected performance, allowing for any remedial action to be taken at the right moment.

By adopting hybrid cloud and multi-cloud strategies, it’s possible to improve flexibility, security, and performance. Additionally, leveraging PDx® use cases can help organizations identify optimal workload placement patterns—balancing performance, compliance, and proximity to data sources—based on proven hybrid deployment models validated by global enterprises.

As the cloud landscape continues to evolve, it's essential for organizations to develop a dynamic framework that links core infrastructure and cloud architecture needs with business requirements. This approach enables you to optimize your cloud environments, reduce costs, and drive success in an increasingly complex and competitive market.

Best practices for hybrid cloud adoption

To maximize the huge potential of hybrid cloud adoption, we recommend a number of best practices that most organizations will benefit from:

Developing a clear cloud strategy: Aligning cloud adoption with business objectives and IT strategies.
Implementing robust security measures: Ensuring the security and integrity of data across hybrid environments.
Optimizing workload placement: Allocating workloads to the most suitable environments based on performance, security, and compliance requirements.
Monitoring and optimizing performance: Continuously monitoring performance and making adjustments as needed to ensure optimal outcomes.
Applying the PDx® methodology: Align infrastructure design, interconnection, and data governance with business objectives.

The future of the cloud

As we continue to navigate the complexities of hybrid and multi-cloud environments, several key trends will shape the future of cloud computing. These include the increasing adoption of edge computing, the exponentially growing importance of AI and machine learning, and the need for more sophisticated security and compliance measures.

By staying ahead of these trends and continually optimizing their cloud environments, enterprises can future-proof themselves in a rapidly changing technological landscape.

Re-orienting towards greater opportunity

The shift towards hybrid and multi-cloud represents a significant opportunity in the face of increasingly demanding AI-driven workloads. By understanding the limitations of legacy cloud models and leveraging the benefits of hybrid cloud, it’s possible to create a more agile, responsive, and cost-effective cloud infrastructure.

As the cloud computing landscape continues to evolve, it’s essential to be adaptable and proactive in your approach to cloud adoption. But in the end, the key to success lies in understanding the unique needs of your organization and developing a cloud strategy that aligns with those needs. By doing so, you can create a robust, scalable, and secure foundation that supports your business objectives and drives long-term success.

By adopting hybrid and multi-cloud strategies guided by PDx® and delivered through PlatformDIGITAL®, enterprises can standardize their architecture, simplify deployment, and future-proof operations against the growing demands of data-intensive, AI-driven workloads.

To learn more, download the PDx® Hybrid Cloud Solution Guide, which provides proven strategies for seamless cloud integration, designed to manage the complexities of AI and Hybrid IT.

Digital Realty Advances AI Infrastructure Innovation Supporting NVIDIA AI Factory Research Center and NVIDIA DSX Blueprint