Why Getting Cloud Infrastructure Right Matters in the AI EraWhy Getting Cloud Infrastructure Right Matters in the AI Era

By Lenley Hensarling

AI workloads are upending cloud strategies, forcing organizations to revisit their infrastructure decisions to stay agile and efficient. But as AI-driven workloads demand more storage and processing power, cloud strategies can't remain the same. Over the next two years, 90% of organizations expect to revise their cloud strategies to better support AI workloads. Organizations have to balance , multi-cloud, and inter-cloud models to meet increasing business demands.

Adapting Infrastructure Strategies for AI

While AI increasingly drives business innovation, it's also making infrastructure decisions more complex. AI models need to efficiently access large amounts of data quickly, which is triggering organizations to re-evaluate where their workloads live according to cost and performance requirements. And it can be a challenge to balance these factors.

While the cloud offers elasticity to meet variable demand, some organizations keep training data on-premises to bypass egress fees, improve predictability, and minimize latency. Although cloud storage, such as AWS S3, is an economical option for long-term data storage, on-premises data storage helps to reduce data transfer costs and enhance processing speeds — especially in sectors such as manufacturing, where the data is generated from in-house instrumentation. Organizations continue to combine their on-premises and cloud resources and optimize each for the needs of their particular workloads.

Balancing Cost and Performance

Cost is a major factor in these decisions. While the cloud excels at handling fluctuating workloads, stable, always-on applications are often more cost-effective on-premises or in co-location facilities. Elastic pricing, egress fees, and hidden charges for moving data between cloud regions or zones can create spikes on the bottom line if workloads aren't carefully architected.

AI workloads need to be fast, reliable, and always have access to data. But in the cloud, things like network congestion, provider throttling, and data transfer limits can get in the way — especially when data has to move between regions, availability zones, or storage layers.

Tailoring Solutions for Performance and Flexibility

With these drawbacks, more organizations are rethinking their approach to infrastructure. They're using hybrid, multi-cloud, and inter-cloud solutions to help balance cost, control, and flexibility.

Hybrid clouds allow businesses to create a customized infrastructure for their workloads by mixing private data centers with public cloud resources, providing a mix of control and elasticity. For example, a retail business can use the cloud to gain extra capacity during seasonal spikes while keeping core operations on-premises during off-peak periods.

Multi-cloud strategies take this a step further. Using multiple cloud providers to run different workloads prevents vendor lock-in and allows businesses to leverage each provider's strengths. This can be advantageous when disparate workloads have different features that are best handled by different services.

Inter-cloud strategies add a layer of resilience by allowing applications to failover from one cloud vendor's space to another's, ensuring continuity during outages. In financial services, having a plan to failover or run across multiple clouds is becoming a regulatory mandate, particularly in the European Union.

Organizations need to design these architectures thoroughly. Unfavorable workload balancing or improper management between cloud environments can result in excessive egress charges, performance lags, and increased complexity. Combining these approaches provides enhanced infrastructure, allowing for flexibility, optimization, and resiliency through hybrid, multi-cloud, and inter-cloud design.

Embracing Flexibility for a Resilient Future

There is no one answer. As American poets Charles Olson and Robert Creeley observed, "Form is never more than an extension of content." The same holds true for infrastructure. The decision of where to compute and store your data is never more than an extension of each organization's environment and situation. Organizations increasingly realize that no single model fits every workload.

As AI workloads continue to grow, organizations need to balance elasticity and cost predictability with greater control over the infrastructure. By knowing how those decisions on infrastructure impact workloads, organizations can build the resilient, scalable, and efficient foundation necessary to thrive in a competitive landscape.

About the author:

Lenley Hensarling is a technical advisor at Aerospike. He has over 30 years of experience in engineering, product, and operational management at startups and large, successful software companies. Lenley previously held executive positions at Novell, Enterworks, JD Edwards, EnterpriseDB, and Oracle. He has extensive experience delivering value to customers and shareholders in enterprise applications and infrastructure software. Lenley believes that business is now happening in real-time and that the right infrastructure for serving data to new real-time applications is a rapidly accelerating requirement for businesses to succeed.