Sponsor Content Created With Dell

How to choose between Dell PowerScale and cloud-native storage for AI workloads

Stylized graphic representing a storage platform
(Image credit: Getty Images)

AI workloads create a challenge when it comes to storage. Businesses need to manage large quantities of unstructured data and maintain high throughput while ensuring compliance with data protection and governance requirements. It’s a lot to deal with.

Cloud-native resources can be attractive thanks to their elastic capacity and close integration with cloud AI services, while on-premises storage allows for closer control of data and lower-latency local access. Dell PowerScale helps reduce the need to compromise, combining local control with scale-out expansion, unified management, and support for hybrid infrastructure – so teams don’t have to choose between control and flexibility.


TL;DR

  • Dell PowerScale is a unified file and object scale-out storage platform that delivers high-performance, scalable management of unstructured data for AI and enterprise workloads
  • Cloud-native storage is often the best fit for AI workflows already running in the cloud, but Dell PowerScale is designed for large-scale file and object storage across environments where governance and control are priorities
  • AI workloads usually benefit from data being architecturally close to compute resources, whether those are in the cloud or on premises
  • Cloud storage can scale up and down to follow demand, while Dell PowerScale is better suited to datasets and requirements that are expected to grow over time. It’s AI-ready and suitable for terabyte to multi-petabyte environments
  • A hybrid approach allows each AI workload to be placed in the optimal location for performance, cost, and governance requirements
  • Dell PowerScale lets businesses stay in control of their data without losing the potential benefits of cloud storage and services

Dell PowerScale

(Image credit: Dell)

Dell PowerScale versus cloud-native storage: what's the difference?

Dell PowerScale is a scale-out storage platform built for unstructured data across on-premises, edge and hybrid environments, whereas cloud-native storage is designed to operate primarily within hyperscale cloud environments and integrate with cloud services.

PowerScale allows organizations to keep infrastructure and data under their direct control, with unified access to file and object workloads, support for NFS, SMB, HDFS, and S3, and integrated data protection. This makes it a good solution when data locality, operational consistent management, and governance are priorities.

For AI performance, data locality often matters as much or more than capacity

AI performance can depend heavily on speed of access to storage and compute resources. Accessing data from afar causes latency, and moving large datasets between locations can waste time and resources.

Cloud-native storage is a natural fit when AI workflows are already running in the cloud, allowing easy integration with other hosted services. For local AI, PowerScale can keep data closer to users, applications and compute resources, supporting more responsive access for operations such as high-throughput analytics workflows.

Dell PowerScale

(Image credit: Dell)

Which approach scales more effectively for growing AI datasets?

The right choice for scaling depends on whether the priority is matching variable demand or supporting long-term growth.

Both cloud-native and Dell PowerScale storage can expand to very large capacities: in supported configurations, a PowerScale cluster can consist of up to 252 nodes with up to 186PB of raw capacity.

In addition, PowerScale’s storage performance can scale with capacity, as each node adds CPU, cache, network and I/O resources to the cluster. This supports growth in throughput as the cluster expands, making PowerScale a strong choice when the priority is sustained local access to large AI datasets.

However, while PowerScale is well aligned with datasets and processing demands that are expected to grow over time, cloud-native storage can more efficiently accommodate workloads that scale both up and down.

Stylized graphic representing a storage platform

(Image credit: Getty Images)

When does a hybrid approach make more sense?

A hybrid approach makes sense when businesses need to run multiple AI workloads with different requirements. Cloud storage offers flexibility for short-term or cloud-based AI services, while PowerScale may offer stronger local performance and stricter governance controls or proximity to on-premises data sources.

And there’s no need to choose one or the other: IDC predicts that by 2028, 75% of enterprise AI workloads will be deployed on fit-for-purpose hybrid infrastructure.

Dell PowerScale’s OneFS operating system supports hybrid AI storage strategies through cloud integration and deployment flexibility, including on-premises, edge and software-defined options for AWS and Microsoft Azure. Policy-based tiering can help place data according to performance, cost, and governance requirements, while data management tools help managers maintain visibility and control as datasets grow.

Dell PowerScale

(Image credit: Dell)

Dell PowerScale versus cloud-native storage: which is the better choice for AI workloads?

There’s no single solution for all AI workloads. The decision depends on whether the priority is elastic cloud experimentation, high-throughput local access, governance control, or hybrid flexibility. Cloud-native storage may be suitable for elastic scaling or workloads already built on AI platforms, while Dell PowerScale is a better fit for local workloads with large volumes of unstructured or governance-sensitive data – and it can work alongside cloud storage to give businesses the best of both worlds.

Swipe to scroll horizontally

Scenario

Why it matters

Best-fit storage solution

Cloud-based AI workloads

Keeps storage close to cloud compute and managed AI services

Cloud-native storage

Highly variable AI workloads

Elastic scaling avoids overprovisioning

Cloud-native storage

High-throughput AI with large datasets

Benefits from local access, scale-out performance, and operational control

Dell PowerScale

AI workloads requiring data control and governance

Supports tighter control over data placement, access, and protection

Dell PowerScale

Hybrid AI deployments spanning on-premises, edge and cloud

Different workloads can be placed where they best meet cost, performance, and compliance needs

Hybrid (Dell PowerScale plus cloud)

Dell PowerScale

(Image credit: Dell)

Keep data scalable, protected, and close to the workload

As AI storage becomes increasingly data-intensive, storage choices will affect how quickly and effectively businesses can operate. A strong architecture doesn’t just provide the necessary capacity, it keeps data scalable, protected, and close to where workloads run. The right storage architecture helps organizations keep unstructured data both protected and available to AI pipelines, all while supporting future growth.

It’s not a question of whether storage infrastructure should be entirely cloud-based or entirely on-premises: the right strategy is one with the flexibility to support the needs of the organization as its requirements evolve.

If you think PowerScale is the right storage solution for your business, find out more on the Dell website: US readers click here.