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Efficiency by design. Predictability by default.
No more surprises in your infrastructure budget. Just more room to grow the business.
What's actually in the way right now?
74%
slowdown in AI infrastructure deployment due to supply chain shortage.
96%
consider or plan to buy storage as a service to solve their storage shortage.
98%
are either considering or consolidating their infrastructure overall into fewer platforms
It’s not a pricing problem. It’s an efficiency and predictability problem.
Solution Brief
Designing for Cost Economics
Keep costs predictable, govern services through contractual SLAs, and stay flexible as conditions change.
Analyst Report
Navigating the Great Storage Squeeze
Learn how storage and memory supply challenges are affecting IT decision-making and budget planning.
Video
Built for Volatility: How Everpure Changes the Equation
When NAND swings and quotes expire, shifting infrastructure risk off your team becomes clearer.
Solution Brief
Storage as a Service with Evergreen//One™
Storage as a service, not a purchase. Consumption-aligned spend, with built-in financial flexibility.
Make efficiency your competitive advantage
Efficiency
Maximum efficiency per terabyte
Cut overprovisioning. Get more from the infrastructure you already have before buying any more of it.
Predictability
Keep costs predictable and controlled
Spend tied to actual usage rather than forecasts, absorbing market volatility instead of passing it through. A cost structure your board can rely on.
Flexibility
Scale with demand
Pay for what you use and scale capacity up or down easily.
Manage your data, not your storage
Unified Data Plane
A single cloud of data that spans on-prem, hybrid, and public cloud locations, giving IT a consistent data environment.
Intelligent Control Plane
Global, intent-driven policies automatically control placement, protection, and governance across your estate.
Universal Data Intelligence
Discover, classify, and map every data relationship, so every decision comes from accurate data.
Three ways to outrun volatility
Requirement One
Maximise what you have
Use existing capacity fully so you don’t need to overprovision.
Requirement Two
Run cloud and on-prem as one environment
Place workloads where they make sense—no duplication, no cost drift.
Requirement Three
Align costs with demand, not volatility
Match spend to usage and reduce exposure when pricing shifts.
How do you keep storage costs predictable?
Five ways to handle demand spikes, reduce sprawl, and avoid cost drift.
Don’t just take our word for it
Ford Motor Company
“At the end of the day, what we are talking about is enablement of developers and velocity of delivering services at a particular price point and at a particular quality… There's hardly anything we can do in Kubernetes without having persistent storage and that is what has brought us together with Portworx.”
What is AI data readiness?
AI data readiness is the state where an organisation’s data can be used by AI applications without manual cleanup. It requires three conditions across all environments: Data is stored consistently, governed by policy that applies wherever the data moves, and understood by the systems that use it.
Is AI data readiness the same as AI-ready infrastructure?
AI-ready infrastructure refers to compute, networking, and storage with enough performance and capacity to run AI workloads. AI data readiness refers to the data itself: whether it’s stored consistently, governed, and understood. Organisations need both. Fast infrastructure doesn’t fix fragmented or poorly governed data.
What is an Enterprise Data Cloud?
The Enterprise Data Cloud (EDC) is the Everpure architecture for managing storage across on-premises data centers and public clouds as one environment. Administrators set policies for performance, protection, and access once, and those policies apply across every location. It reduces data silos and manual work per system.
How do I find out whether my data is AI-ready?
Check three things across every environment where your data lives. Is storage managed the same way in each location? Are access and retention policies enforced without manual steps? Is the data labeled so applications know what it is? A no on any of these is the gap to fix first.
Why do AI projects fail at the data layer rather than the model layer?
Check three things across every environment where your data lives. Is storage managed the same way in each location? Are access and retention policies enforced without manual steps? Is the data labeled so applications know what it is? A no to any of these is the gap to fix first.
See how this connects to other solutions
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