Object storage isn’t new, but the way Everpure™ (formerly Pure Storage) is delivering unified object storage across FlashBlade and FlashArray —and tying it together with Everpure Fusion™ — is what made this Ask Us Everything session so relevant for infrastructure teams. Hosted by Don Poorman, with Karthik Srinivasan (Director of Product Management) and Justin Emerson (Principal Field Solutions Architect), the discussion cut straight to what practitioners care about: scale, performance, migration realities, and how object fits into environments that still rely heavily on block.
Why Object Exists (and Why It’s Different)
Justin opened with a reset that resonated: file and object may both store unstructured data, but they are built on different assumptions.
File storage evolved from human workflows — folders, directories, locking semantics, POSIX guarantees. That model works well for users and shared drives. But those same assumptions become friction at cloud scale.
Object storage was built for machines. It uses a flat namespace, atomic operations, embedded metadata, and native versioning. That’s why modern applications — backup platforms, analytics engines, AI frameworks — increasingly request S3 buckets instead of file shares. It’s not that file storage is going away; it’s that machines prefer object.
Scale: 3.8 Trillion Objects and Counting
One of the standout moments was a validation that Everpure ran for a customer, which tested 3.8 trillion objects in a single bucket on FlashBlade. They didn’t stop because they hit a ceiling — they stopped because they ran out of time.
That matters because unlimited scaling isn’t guaranteed in most on-prem object systems. Many legacy solutions quietly impose metadata or bucket limits that don’t surface until you’re deep into production. If your roadmap includes AI datasets, large backup repositories, analytics pipelines, or content delivery use cases, scale limits quickly become real-world constraints.
Object for AI: Performance Has Changed the Conversation
Using object for AI dominated the Q&A — and for good reason.
Training workloads demand enormous throughput, especially for checkpointing bursts across large GPU clusters. Inference workloads are more latency-sensitive and read-heavy. FlashBlade’s architecture, including S3 over RDMA, separates metadata authentication from the data path and enables direct, high-throughput access to data nodes. The team referenced performance in the hundreds of GB/sec range on multi-chassis systems.
Justin made an important observation: AI initially landed on file systems simply because object storage wasn’t considered performant enough. That assumption is changing rapidly.
Object on FlashArray: The “Alongside Block” Story
A lot of questions focused on object running on FlashArray — resiliency, performance expectations, and which workloads are a fit.
Writes are acknowledged only after safe persistence, and standard object retry logic handles failure scenarios cleanly. So, you can be sure of data integrity, even if a controller fails.
FlashArray Object is designed for smaller-scale S3 use cases: artifact repositories, container workloads, image stores, edge environments, and test/dev scenarios. FlashBlade remains the scale-out platform for massive object footprints. Over time, Everpure Fusion will increasingly abstract placement decisions so workloads land on the right platform without adding operational complexity.
Data Reduction and Garbage Collection: The Hidden Advantages
One of the more practical differentiators discussed was garbage collection. Many legacy object systems struggle with delete churn because of layered indirection — objects are marked, then nodes are marked, then underlying file systems are marked, then media eventually reclaims space.
Because Everpure controls the stack end-to-end — logical object through physical media — reclamation is cohesive and efficient. Combined with always-on compression and similarity-based DeepReduce techniques, customers see meaningful space savings without sacrificing performance.
Migration: It’s an Application Decision
Perhaps the most important takeaway: moving from file to object isn’t a storage copy exercise. It’s an application transition. Backup software, artifact repositories, and analytics platforms increasingly support object natively. Let the application drive the migration instead of trying to brute-force a file-to-object copy.
Object is growing quickly, but the shift doesn’t require abandoning everything at once. With FlashArray for edge and unified workloads, FlashBlade for scale-out performance, and Everpure Fusion tying it together, we are building a platform where object can grow naturally alongside block — not replace it overnight.
If you have follow-up questions, bring them into the Pure Community. The conversation around object is only getting bigger.
