FlashBlade

FlashBlade is best suited to scale-out file and object workloads requiring high concurrency, throughput, capacity or rapid access to large unstructured datasets.

Typical workloads include AI and machine learning, analytics, high-performance computing, media, backup and recovery, and large data repositories. If you are evaluating FlashBlade for a particular workload, book a free storage assessment and our specialists can assess the performance and capacity profile.

FlashBlade focuses on scale-out file and object workloads, while FlashArray is generally better suited to latency-sensitive databases, virtualisation and enterprise applications.

FlashBlade is designed for highly parallel workloads such as AI, analytics and large unstructured datasets. If your requirement is centred on transactional applications, block storage or predictable low latency instead, compare the Pure FlashArray range before selecting a platform.

FlashBlade//S prioritises performance for demanding active file and object workloads, while FlashBlade//E emphasises economical capacity for large-scale unstructured data repositories.

FlashBlade//S suits AI, analytics and other high-throughput workloads, while FlashBlade//E is better aligned to backup, active archive and retained datasets. The right choice depends on throughput, concurrency, capacity requirements, growth and how frequently the stored data is accessed.

Yes, FlashBlade provides native NFS, SMB and S3 services on the same scale-out platform for file and object storage workloads.

This allows organisations to support multiple access methods without maintaining separate storage systems or protocol gateways. It can be particularly useful where applications have different protocol requirements but the business wants to consolidate unstructured data onto a common storage platform.

FlashBlade should be sized using capacity, throughput, concurrency, metadata activity, network connectivity and expected growth rather than stored data volume alone.

AI and analytics can place far greater performance demands on storage than backup or archive workloads of a similar size. Our specialists can assess your current data and projected growth to recommend the appropriate FlashBlade platform and configuration.

Yes, FlashBlade is designed for highly parallel file and object workloads and can provide shared storage for demanding AI and machine learning pipelines.

It can support data-intensive workflows where multiple GPU or compute nodes require concurrent access to shared datasets. The appropriate design should consider throughput, metadata activity, network architecture, dataset growth and whether the workload is centred on training, inference or analytics.

FlashBlade uses capabilities including SafeMode snapshots and replication to help preserve recoverable data following ransomware, accidental deletion or other disruptive events.

The appropriate protection design depends on recovery objectives, retention requirements and whether FlashBlade supports production data or backup repositories. If cyber-resilience is part of the project, our storage specialists can assess your recovery requirements and how protection should be incorporated into the architecture.

Yes, FlashBlade can be incorporated into Pure Evergreen commercial and lifecycle models, including consumption-based services where appropriate to the storage requirement.

This provides alternatives where capacity growth is difficult to forecast, traditional upfront procurement is not preferred or ongoing platform modernisation is important. Explore Pure Evergreen to understand the available models, or speak to our storage specialists about matching them to projected FlashBlade usage.