New storage infrastructure and automated data pipelines target the bottleneck that kills most enterprise AI projects before they ship.
Most enterprise AI projects never reach production. The reasons are rarely about the model. They are about the infrastructure underneath it — storage that cannot keep pace with training at scale, data pipelines that require constant manual intervention, and capacity planning that breaks down the moment workloads grow.
Everpure is announcing two products designed to address exactly that. Evergreen//One for FlashBlade//EXA extends the company’s consumption-based storage model to its highest-performance flash platform, built for large-scale AI training and inference. Everpure Data Stream, entering beta later in 2026, automates the movement of data from ingestion to inference — eliminating the manual pipeline management that slows most AI deployments before they ever reach production.
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The Performance Case
The central promise of FlashBlade//EXA is that storage performance should not collapse as workloads scale. In practice, that is exactly what happens on conventional infrastructure. Training a model on four nodes produces acceptable results; scaling to 192 nodes exposes every bottleneck in the stack.
STN, an Everpure customer, has pushed FlashBlade//EXA to 192 nodes and has yet to find the performance ceiling. “In a typical storage infrastructure, researchers might start training a model on four nodes and get good performance — but as soon as they start scaling up, that performance collapses,” said Sabur Mian, CEO and Founder of STN. “With FlashBlade//EXA, we’ve yet to find the limit.”
Recent benchmark results support that characterization. FlashBlade//EXA achieved the highest score ever recorded on the SPECstorage Solution 2020 AI_Image benchmark, sustaining 6,300 simultaneous AI jobs — more concurrent training tasks at full speed than any other solution currently on the market. Separately, MLPerf-driven workload testing validated sustained GPU utilization above 90 percent across large NVIDIA Hopper clusters. The system moves data twice as fast as its closest competitor while occupying less than half a rack of physical storage space.
Everpure is also aligning FlashBlade//EXA with NVIDIA’s modular STX reference architecture, supporting the Vera Rubin platform and extending NVIDIA-Certified Storage validation to the system. The integration creates a path toward NVIDIA Cloud Partner certification-level compliance — relevant for enterprises building on NVIDIA reference architectures at scale.
Solving the Data Pipeline Problem
Raw performance means little if the data feeding it is stale, incomplete, or manually managed. Everpure Data Stream addresses the orchestration layer — automating curation and delivery of AI-ready data directly into the infrastructure, without the administrative overhead that typically accumulates between data ingestion and model training.
The product is co-engineered with Supermicro and built on the NVIDIA AI Data Platform reference design. For enterprises that have treated AI readiness as a one-time infrastructure milestone, Everpure’s framing is deliberate: continuous data optimization, not a single deployment event.
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The Consumption Model
Evergreen//One extends across FlashBlade//EXA as a pay-as-you-go storage model, allowing organizations to deploy capacity globally and scale on demand without upfront provisioning commitments.
For Options Technology, that flexibility has been operationally significant. “We can now deploy storage anywhere in the world, consume it on a pay-as-you-go basis, and scale on demand — bringing down the barriers to global growth and flexing to meet the demands of rapidly evolving AI workloads,” said Andrea Moccia, VP of AI and Data at Options Technology.
The model reflects a broader argument Everpure is making: that the reason most AI projects stall between pilot and production is not ambition or talent, but infrastructure that was never designed for the volume, velocity, and variability of enterprise AI at scale.
“Most AI projects fail to reach production because many treat AI as just another workload,” said Kaycee Lai, Vice President of AI at Everpure. “We are helping customers break down siloed data and move AI initiatives from pilot to production with infrastructure that delivers guaranteed performance, flexibility, and growth.”
