Our program will begin momentarily. Now, please take a look at this message from IUCN, a great NetApp customer and one of our 2026 customer social impact award winners.
Nature is under threat. Species are disappearing. Oceans are being emptied. Plant life is vanishing. And time is running out for life on Earth. Every moment matters. At IUCN, our mission is clear: to defend what remains and help secure a livable future for generations to come.
This mission runs on data. Data that reveals where nature is under pressure. Data that tracks decline, risk, and recovery. Data that helps governments, scientists, and communities act before it is too late. When the signals are clear, action is true, but only if the data is there when it's needed, where it's needed.
It must be secure, available, fast, resilient, because when critical data fails, decisions are delayed, and nature cannot wait. That's why we partnered with NetApp. Together, we built the infrastructure behind the mission, a foundation designed to keep data protected, accessible, and always ready.
We use NetApp ONTAP, NetApp Snapshot and SnapCenter for simplified data protection to secure our system to be able to do fast recovery and restore the data in case of an incident with zero impact, zero data lost. So our teams can move with confidence, respond without delay, and stay focused on conservation, not disruption.
NetApp will continue to be a strong partner in ensuring that our data is properly safeguarded and can be accessed by all systems at all times, enabling an intelligent data infrastructure. They are trusted allies in the work ahead. Together we protect nature and the data that helps save it, so we can help build a more sustainable future for all life on Earth.
Okay, NetApp Insight, one last bit of housekeeping. Please refer to the message on the screen for some important dos and don'ts for today's session. Thank you.
It's showtime. The final countdown to NetApp Insight starts now.
Please welcome George Kurian.
Welcome to Insight 2026. Thank you to our sponsors, our partners, and so many who have worked so hard to make this a fantastic event. And thank you for traveling and taking the time to be with us here today. We are so excited about what we'll cover over the next two days. Between our keynotes, our featured and breakout sessions, you'll leave having a deep understanding of our perspective on the evolution of the data center, data infrastructure, and the data stack over the next few years, the massive AI opportunity, and the specific challenges facing organizations who want to capture it, and what we are doing at NetApp to solve those challenges and to help you meet this moment.
While much of the AI conversation has evolved, shifting focus from discussions around models to GPUs and AI factories to inference performance and token economics, what has not changed is the critical importance of your data and your data infrastructure in driving success for your AI strategy.
Many organizations are still struggling to scale AI from pilot to production. And when we sit down and problem solve with you, it always comes down to two fundamental challenges: challenges with your infrastructure and challenges with harnessing your data. Everything we'll discuss today is tied to addressing those two problems. Let me begin with infrastructure.
With the advent of accelerated computing and AI applications, data center infrastructure is being transformed. The old design patterns are being swept aside, making way for new paradigms. To reduce the time it takes to train and fine-tune your models, you need to pull enormously large GPU clusters together. Typically, the larger the cluster, the faster you can train the model.
But GPU compute is unforgiving with high concurrency and parallelism: bulk data reads to load, bulk data writes to checkpoint, interleaved metadata and data operations, and you are measured unforgivingly on time to first token and cost per token, and it is extremely expensive to have any of it sit idle.
You also need to organize very large exabyte-scale or even zettabyte-scale data sets with different data types that span your hybrid cloud under one namespace. Because simply put, larger, higher-quality data sets generally improve AI inference quality by giving models more diverse examples to recognize patterns accurately for inference. And the scale of both the compute clusters and data sets is accelerating at an extraordinary pace.
As an example, large language model scale has grown 5,000-fold in four years. This can be particularly frustrating if you're a model builder or trying to fine-tune models, if you're operating AI hyperscale clouds, GPU clouds, or sovereign AI factories, but also if you're an end user of these factories or building your own AI platform.
The second challenge is your data. Data is becoming the constraint to productively scale AI. And you know, at NetApp, we know a few things about data. For over 30 years, data infrastructure has always been and remains our focus. And we have been building and innovating solutions with industry leaders like AWS to help businesses take advantage of their data.
And now it matters more than ever. You see, 93% of organizations report that at least one form of data challenge is blocking their AI ambitions. And the high-performing ones are already running into this. And the challenge becomes even more critical as you scale AI into more business-impacting opportunities which carry more risk. Because AI retrieves, recombines, and generates data faster than traditional governance models can keep up.
To compare against these needs, let's look at your data estate today. You see, before an AI system can generate a single useful token, it must discover, access, and understand the correct information required to answer the question it is being asked. In a modern enterprise, that information rarely lives in one place. A small part of it, less than 15% typically, resides in structured business systems such as your ERP system, your CRM system. But these provide just a clinical view of your business.
Much of the context, the meaning, the sentiment, the texture of your organization that makes AI valuable — about 85% of it — is somewhere else entirely. Engineering documentation, research data, operational files, media assets, simulation output, scientific data sets, and decades of institutional knowledge placed in content repositories, email systems, Slack channels, and Zoom calls.
All of it distributed as unstructured data across siloed storage and application environments, cloud environments, remote offices, and different data sovereignty jurisdictions. So what's the cost of all that fragmentation? First, foregone opportunity. Petabytes of your unstructured data are dark, with hidden knowledge ignored because it sits in isolated, undiscovered silos.
Second, operational complexity and fragile business continuity. You know, different customers' workloads and data sets have genuinely different protection requirements. However, having customized, tailor-made infrastructure silos requires separate maintenance schemes, massive operational complexity, and never-ending technical debt. I'm sure you recognize this challenge.
Third, ransomware and cybersecurity attacks are on the rise, and agentic AI-enabled ransomware attacks are already enabling bad actors to attack vulnerabilities faster than ever before. And finally, sovereignty restrictions are beginning to be a struggle to implement consistently across your data estate.
So to summarize: fragmented data, dark data, uneven protection, facing rising attacks and hardening sovereignty rules. This is the state of most customers' data estates. No wonder it's hard to use AI with your data and data infrastructure in this state. Let's talk about the business impact of doing so.
Businesses today are more global in scope than ever before. Customers, suppliers, and employees can be spread across the world, and they work closely together to share information, learn from each other's experience, and act on the organization's behalf.
With the advancement of AI, leaders across every industry now envision the agentic enterprise, where agents extend their employees' capabilities. These agents are autonomous software programs that use AI to perceive their environment, make decisions, and take multi-step actions to achieve a goal. To make them work for you, they need two capabilities that let humans and organizations operate effectively in the first place: memory and context.
Let's start with memory. As humans, our memories shape who we are. They make up our internal biographies. They tell us what we've done with our lives, who we are connected to, and inform what we should do next.
Now, on to context. Context is the background of an event, a statement, or a piece of data. It is what gives facts a clear meaning. Without context, a single word, number, or action can mean many different things. Context helps memory recover the right experience to make the right decision, and it updates that memory with recent experience.
For example, you might have a memory of yourself as a child on a boat with your parents. Without context, that's all it is. But with context, you'll remember that it was a special occasion because your dad took off work that day to be with you. Your mother packed your favorite lunch, and you were wearing a brand new shirt that day that you felt great in, and that you might still have hanging in your closet today.
Together, memory and context create feeling, impact. And when combined with actions that provide for continuous learning, memory and context are what create knowledge. For your enterprise to be truly AI-enabled, it needs context and memory for your agents. To give them that, you need a data platform that not only can store and protect your data, but can also unlock the insights that are embedded but hidden in your data.
One that has been trusted to store your organization's data, your digital memory, for decades, and that can now organize it and build in context right where the data is created and where it exists, to transform your data into knowledge. That's the NetApp platform, an intelligent, governed infrastructure that provides zero-copy access to all your data, for any data and any workload, anywhere you operate.
The NetApp platform is already part of your enterprise. You've trusted us with your data, your organization's memory, for more than three decades. And you can continue to trust us that we will provide you with the tools to turn your data into knowledge.
Today we will share two major advancements to address those challenges that I spoke about earlier — infrastructure and data — so that you can lead in the AI era, scale AI across your organization, and take advantage of all the investments you've already made with the NetApp platform.
The first extends the value of the trusted NetApp platform to meet and exceed the infrastructure demands of the largest computing infrastructure, the biggest frontier models, AI factories, and hyperscalers. This allows you to solve the infrastructure challenge we spoke of and use the world's most advanced capabilities to process your data, your memories.
The second, to build context, we are going to provide you with the capabilities to exploit the value of all of your data — NetApp data — where it is created and where it resides, by organizing it, providing context, coherence, and the data foundation you need for your agentic enterprise.
The NetApp platform lets your organization do four things. One, make your data AI-ready so that you can move quickly from idea to impact. Two, unify your storage, confidently handling the most demanding workloads wherever they live. Three, proactively protect your data with unquestionably the most secure storage on the planet. Period. And four, give you complete control over your entire data estate and data infrastructure. No silos, total visibility. The NetApp platform helps you turn your data into knowledge.
To provide you with a deeper understanding of the NetApp platform and some of the new innovations, I want to introduce our chief product officer, Sham Nyer, who will be on the stage shortly. But before we bring up Sham, it's always worth remembering that the secret to the NetApp platform is not only the awesome innovations that we build, but because we have as our strategy and our tradition the idea that when we innovate together, we innovate best.
And so we have done so with a broad ecosystem of technology leaders across the industry. And I'm excited to announce the newly expanded partnership with a leader in cloud AI, leading soon to an awesome new product announcement. Let's hear directly from them virtually. Welcoming Mahesh Thiagarajan, executive vice president of Oracle Cloud Infrastructure at Oracle.
George, thank you for the opportunity to join you and the NetApp community at Insight. Getting real value from AI depends on making enterprise data accessible, manageable, and ready to use. NetApp has earned customers' trust over the years by delivering powerful and consistent data management across on-premises and cloud environments.
For NetApp customers, moving critical applications and data to the cloud should not mean retraining teams, redesigning workflows, or starting their data protection strategy from scratch. And this is why Oracle is very excited to partner with NetApp to bring ONTAP on OCI. Very soon, OCI file storage and NetApp ONTAP will enable customers to run business-critical file storage workloads in OCI using the same set of ONTAP tools, APIs, and automation.
Together, OCI and NetApp will help customers extend applications and their data to the cloud with greater operational consistency, supporting large data sets, enterprise applications, development environments, and a very resilient hybrid cloud strategy. We're very excited about what our teams can help our customers achieve together. Super excited about the partnership.
Please welcome Sham Nyer.
Good morning, everyone. Good morning. Thank you, George, and thank you, Mahesh. We are thrilled to partner with you and the Oracle team to build the next generation of NetApp storage service on Oracle Cloud Infrastructure. We will talk more about this throughout the breakout sessions. Please visit the expo floor. There will be featured sessions where we'll talk about this new offering.
Now, for 30-plus years, our industry answered one question: where do I put my data? The answer was a scalable, durable storage layer. Store it safely. Store it securely. We became the most trusted place to hold your intellectual property, your most precious asset, data. Everybody talks about it, the real fuel of the enterprise. And with your trust and our continued innovation, we are still very good at it.
A few months ago, I sat down with the CIO of a large European financial institution. He said something that stayed with me since. Everyone sells me their new features, performance, durability, maybe a new AI operating system, but what's actually on my mind is driving value from the investments that I've already made. I need a partner who helps me get more from the data that we already have. And he said he felt for the first time someone really answered that call.
NetApp's vision is to supercharge what you all have already invested in. What the CIO was asking: what does my data know? What can it do for him? And he is focused on the agentic enterprise, where agents take on complex actions on your behalf, whether it's moving money or making trades. George talked about the importance of memory, context, and knowledge — what agents need to deliver business outcomes and to be trusted.
Let me show you where that memory and context live and how you can enable it on the infrastructure that you already have. Look, we need to solve for the infrastructure needs and the data needs together, not in silos. And that's the need for the agentic enterprise. And today we will talk about the only platform built to do exactly that: make your entire data infrastructure ready for the agentic enterprise.
One thing before we go further. Remember the CIO conversation? The CIO wanted more value from his infrastructure, and I'm holding myself to the bar right now with all of you. Everything today, literally everything, is either generally available this morning or in preview, and you can sign up in the expo hall this afternoon. All of you get to supercharge your investments right away. That's the promise from NetApp.
The NetApp platform has four capabilities: AI-ready data, proactive protection, unified storage, and complete control. But every one of them is anchored on the same thing: your data where it already is, made usable without any migration. That's the promise of true zero-copy activation with always-on protection with the native platform. It's trusted, intelligent data infrastructure for the agentic enterprise you all deserve.
Let's start with AI-ready data. Agents that act on your behalf are only as good as the data they can reach, trust, and act on safely. Data fragmentation is a reality across on-premises, cloud, and the edge. And when this happens, it's natural to have increased operational complexity and inconsistent governance. So the first thing that must be true is your data. It must be ready. That means an agent can find it, understand it, trust it, and act on it right where the data lives, without added complexity and no runaway cost.
Now, here's the part that changes everything. You all already own this data. You have this infrastructure. It's your intellectual property. Every copy somebody is asking you to make is a governance problem. Every move is an operational cost. So what do we do at NetApp? We help you activate your data right where it already sits, on the infrastructure you already trust. We are igniting what you already own, everywhere where your data is.
When I met with the head of a research computing company, they have decades of trial data, lab results, research notes sitting across systems going back years, some of it older than the researchers now trying to use it. This is digitized memory and context hidden in there for them. But to unlock it, six-plus months of data engineering before a single model says a single thing. They needed the data they already owned to speak to the AI they are trying to use.
This is not very different from the CIO conversation. Just a different application of the same ask: what does my data know? Activate the data in place, safely and securely. We can do this today. Today we can activate with context exactly where decades of trust already put the data.
Let me show you how it fits together. The foundation of it is about discovering and understanding what you have, what data is there, without moving it. Then comes insights: surface what's actually in the data, the patterns, the context that was sitting there unused. Built-in intelligence: governing, securing all of the data automatically with the same policies protecting your data today. And finally, context. Now your agents have something to reason over, built entirely from data that never ever left your infrastructure.
We bring it to life using the NetApp platform. How? By tracking your data and metadata without constant discovery, without scanning your file systems again and again, with no migration. George mentioned this. Let's go back to the need for memory and context. Combine that to create knowledge. We talked about how your data is everywhere, but what has been missing is the layer that knows what is in the data. Where does the data live? What does it mean?
The layer that turns a data estate into memory and context — and the layer is right there, lives on the same trusted unified storage you already use. You already use this for your most critical data and your business-critical applications. The most secure storage on the planet. Oh, sorry, the only trusted data infrastructure you will ever need, and that is from NetApp.
Look, this is not about just activating data. You have invested in the infrastructure. We need to activate it with efficiency, with accelerated computing and GPU acceleration. Better economics on the infrastructure you already trust. That's the outcome you need from an intelligent data infrastructure.
Last year, we introduced the NetApp AI Data Engine. And as customers used it, we learned what the biggest job really was: safely activate data without copying it, without losing the governance. This AI Data Engine is part of the platform, purpose-built to turn all of your dark data into trusted, governed context. Your data made AI-ready right there. And while it's activating it, it's also standing guard over it. I'll come back to exactly what this means in a little bit.
For now, just remember this: readiness and protection aren't two separate things. They have to happen together. And now the part I'm most excited about. We talked about the researcher. Their problem was cost and complexity. Six-plus months of engineering. Their CIO told me that not all of the data was on the same storage platform. So they had to discover, ETL, harmonize, model, and make them available to the researchers. Well, we have a solution for that.
Everything I just showed you up until today worked on NetApp data. And I'm excited to announce that the metadata capability of the NetApp AI Data Engine supports data from anywhere. Heterogeneous data sources, all of it. It's not a new system you need to buy. It is the fastest way to find out what's actually in the data you already own.
The AI Data Engine is now fully software-defined, discovers, understands, and activates data we have never touched. Point it to your entire data estate, every system you have inherited along the way, and start building context across data without moving them into another system. This is the magic. I have a call to action. Thank you.
I have a call to action about the metadata engine. Everyone, every enterprise in this room should be running it. Not eventually, not on a roadmap — as soon as you are all back in your offices. And to help you all do that, I'm happy to announce we are making NetApp AI, the AI Data Engine, and this new metadata capability I talked about available to all of you here today and every other enterprise out there, free for the first six months.
Take out your phones, scan the QR code, and they will be available on the expo floor if you don't get a chance. Look, our promise is very simple: your data, activate it wherever it already lives. And we mean that literally.
The data is ready, but every training run, every large query still ties up compute resources. Everyone you talk to these days talks about accelerated computing. Let's talk about the researchers. They don't want a new tool. They live in Spark. They live in PyTorch. They've got decades of Python libraries that they trust. These are engineers. They were talking about true accelerated computing that they need, that we need to build into the data infrastructure, with no tool change, no code change.
That's exactly why I acquired the company DataPalago. DataPalago accelerates the tools the researchers already use — Spark, Trino, Python libraries built over years — right in the data infrastructure. We talked about zero-copy activation. This is zero-code-change acceleration.
In the agentic era, readiness and protection are not two separate conversations. If your data is not protected, your data is never ready. An attack isn't a matter of if. It's a matter of when. That was already true. But now your agents are acting on your data autonomously, making decisions, moving money, triggering transactions, all at machine speed.
If that data is compromised and nobody catches it in time, the agent doesn't slow down to ask if we should trust it. It just acts. So your data infrastructure is no longer the last line of defense. It's the thing your agents' judgment depends on each and every time they act.
A colleague of mine, a cybersecurity leader, would often say, 'If you're reachable, you're breachable.' I think the same is true for every piece of data now, maybe a little bit more urgently. If it's valuable, it's a target. And if it's a target, it needs to protect itself.
Here is why I believe this even more strongly. Very recently, some of the best AI models in the world couldn't pass a hard security test. You probably heard about it. They were working in a sandbox, but they cheated and finally broke into a production infrastructure at a real company. These agents were not malicious, but their actions were risky.
The guardrails — yes, they had them — but you cannot hand an agent a policy and expect it to just care. I'm talking about the most secure agents operating on your data, and where we need a new meaning of zero trust, which is not a network posture. It's not an identity check at the door. It is protection built into the data itself. Every single piece of it.
NetApp is on a path as part of our platform to define and deliver zero-trust data security built into the platform. We will work with our partners, the best cybersecurity systems, as well as other providers, and make sure that there is ability for every piece of data to protect itself. Real AI readiness and always-on data security were never two separate guarantees. Data that is unprotected was never actually AI-ready. They have to be part of the same platform promise, and we will deliver that with the NetApp platform.
Now, detection without recovery is just an expensive alarm. It tells you something went wrong. It doesn't help you get back to work. Zero trust means every access point gets verified before it happens. But verification doesn't help if someone still gets through. So your recovery point has to be immutable. Nobody can alter it or delete it, even with the right credentials.
And it has to stay safe even if your network is the thing that got hit, because it has to run air-gapped, cut off from whatever failed. So it's not about detecting ransomware. It's about closing the loop when something goes wrong and getting you back in business. The real recovery protecting your business, always. And it has to run itself and be part of the platform DNA.
With the NetApp platform, it catches problems before they spread and fixes them automatically, so that no one is waiting on an approval at 2 a.m. Our value is now going to be built deeper into the Commvault product. So your team knows from the console that they already use. With this new integration, potential ransomware attacks light up within your Commvault interface, and you can see all the threats collected together.
We have now also integrated the complete curated clean recovery in with Commvault, so you can leverage tools like SnapRestore and recover as easily as you want. No new tool, and fewer tools also mean fewer doors for the attacker to find.
The real cost of a breach is never just downtime. It's time lost getting back to business. It's time lost rebuilding trust with regulators and customers who are suddenly asking us a lot of questions. The real value here is the day something goes wrong, you can depend on the platform for business continuity, and you are already planned for that and accounted for.
I want to thank the Commvault team for the partnership, and we're not stopping here. We continue to enable our partners to always leverage the best of the NetApp platform. As another example, let's hear from Thomas Kurian of Google Cloud, one of our longtime partners, discussing how they are leveraging our latest innovations.
Thank you and hello everyone. It's great to be back at NetApp Insight with you. Google Cloud and NetApp are continuing their long partnership to help customers solve critical data challenges in this AI era. With Google Cloud NetApp Volumes, we power the largest data center exits across all enterprise workloads. And we're doing that without compromising on performance, scale, or security.
By leveraging ONTAP's latest innovation alongside Google Cloud's compute and Hyperdisk, we deliver massive-scale performance with up to 200 gigabytes per second of throughput to support demanding EDA workloads. All of this is backed by best-in-class security, including NetApp ransomware resilience.
You know, NetApp Volumes is also enabling customers to deploy advanced agentic workloads and RAG inferencing by bringing unstructured data into Google Cloud. Customers can now transform their data from on-premise systems and GCNV into a hybrid grounding knowledge base. And by combining Gemini Enterprise with NetApp Volumes, we deliver trusted AI intelligence without any infrastructure friction.
Customers including PayPal, Merck, and HCL Technologies are already leveraging Google Cloud NetApp Volumes to power their enterprise workloads. Additionally, NetApp storage solutions are now embedded natively into Google Distributed Cloud. This is empowering regulated sectors to run our AI model Gemini and Gemini Enterprise in air-gapped sovereign environments.
Alongside NetApp, we enable you to unlock your data's potential and to drive faster AI innovation. Thank you for your continued partnership.
Thank you again to Thomas and the Google team for the partnership. Now a different focus area for us. If you're running a hybrid estate — and almost all of you are, all of us are — this one is for us. Let me tell you about the manageability challenge that I often hear about. The problem is often simpler but somehow harder. Infrastructure in four different places, four different teams, four different consoles.
Admins can't tell with confidence whether the same policy is enforced everywhere, all at once. We need to allow you to manage it any way you want it, anywhere. And that's what complete control means: one place to see, manage, and govern everything on-premises, every cloud, everywhere in between, as one data estate. And our answer to that is the NetApp console.
On-premises, Keystone, all major public clouds, all on one canvas. AI-assisted autonomous operations so that there's one control plane for the entire platform. Predicting issues, resolving incidents faster, optimizing capacity and cost with human oversight, always in the loop.
And for the most sensitive environments, the ones that can't depend on a network connection to stay secure, or where regulatory or enterprise policy needs it to be local and air-gapped, the console runs within your environment completely air-gapped, with the same visibility, same control, completely isolated when isolation is the requirement. With this, four consoles and several other tools all now become one. Built for every environment, every policy, one place, and built into the same platform you trust your mission-critical businesses with.
I talked about this local air-gap control. Let me show you exactly what that looks like in practice. This is fleet management capability of the console local. It goes beyond what any other tool ever tried to do: organizing and operating an entire data estate as one, from inside your data center. Intelligent fleet visibility, built-in health monitoring, predictive insight — again, all running on your local infrastructure.
For existing AIQ unified management customers, this isn't a rip and replace. It's an upgrade path. The same investment now delivering complete unified control. No more manual operations stitched together across tools. One control plane doing what it used to take five.
Let's take it a step further. What if you want your entire environment built out as a service, but you need the same service provided in a sovereign manner? George talked about this: the sovereignty needs, data residency, a growing list of regional requirements.
Today we are unveiling Keystone Sovereign. This is Keystone subscriptions with data sovereignty controls to meet the strict regional compliance and data residency requirements of European Economic Area countries. This is a sovereign-focused Keystone offering, an add-on entitlement that enables EEA customers to purchase Keystone with additional regional operational support and data handling controls specific to EEA.
For customers, it ensures data as well as support remain within the same specific geographic boundaries and meet the regulatory requirements. This is available early November for France and Germany, with general availability for everyone else later on.
In both of these previous demos, I showed us leveraging the console as we always traditionally have. But George also told you that enterprise leaders everywhere are envisioning an agentic enterprise where agents extend what people can do. That's just not true for your business applications. It's true for your infrastructure too.
Now the console isn't something to look at anymore. It's something you talk to. The console now understands intent, not just commands. Ask it to provision a volume, set up a replication, investigate why a workload slowed down, in plain language. And it acts inside the guardrails you've already defined across ONTAP, StorageGRID, and E-Series.
And because it works with the LLM you already run, your own governance travels with it. The same agents helping your businesses move faster now help your infrastructure move just as fast. And again, one team, human and agents working together, running the whole estate.
Every day, we are working with the largest industry movers to help our customers most simply manage their entire hybrid multicloud environment. To learn more, let's hear from Scott Guthrie, executive vice president of Microsoft Cloud.
Hi, I'm Scott Guthrie, executive vice president of the Cloud and AI group at Microsoft. It's great to be with you today. NetApp Insight brings together the people helping organizations navigate some of the biggest technology shifts we're seeing today. When you look at cloud, AI, cybersecurity, and digital transformation, so much of it comes back to one thing, which is data.
Customers across every industry are looking to move faster with cloud and AI while continuing to leverage the applications, infrastructure, and data that already run their businesses. They don't want to start over. They want to build on what they have while taking advantage of what's possible with new technology. That's where strong partnerships become especially important.
Together, Microsoft and NetApp are helping customers bring ONTAP workloads to Azure and simplify hybrid environments. That gives customers greater flexibility as they evolve their IT strategies. We're also continuing to innovate through Azure NetApp Files, helping customers run some of their most demanding workloads in Azure, including semiconductor design, chip verification, and large-scale engineering simulations where performance and reliability matters.
As organizations scale AI, the ability to access the right data across environments becomes increasingly important. By combining NetApp's intelligent data infrastructure with Microsoft's cloud and AI platforms, we're helping customers put their data to work in new ways while maintaining the governance and control they require.
And that's what makes this partnership so valuable. Customers can move forward with confidence, knowing they can build on the foundation that they have today while creating a stronger one for what's next. I'm excited about what we're continuing to build together and the opportunities ahead for our shared customers.
Thank you, Scott. We are excited to continue our partnership with Microsoft, and all these partnerships tell us that the NetApp platform doesn't have borders. It works on every hyperscaler, on your premises, as well as on the neoclouds.
So we have talked about how we protect your data, how we make your data AI-ready, and now we help you manage all of that. Now let's talk about where all that data is stored. Let's talk about the story of a global retailer. Every November, the traffic multiplies overnight. For years, that meant scrambling to provision extra capacity, then tearing it back down, helping two environments stay in sync the whole time. A big challenge.
They did not need a big data center. They needed one platform that had the elasticity to stretch before November to meet the scale needs. And that's the true meaning of unified storage and a unified platform. One platform, one global namespace across every environment your data lives in: on-premises, every major hyperscaler, or anywhere in between.
It expands when you need it, contracts when you don't, without you managing three different infrastructures to make that happen. And underneath that, it's ONTAP. ONTAP does all the work. The same foundation that's run your most demanding workloads for three decades. And the reason we call this unified in the first place: AFF is a flagship for on-premises performance, and because it's built on the same foundation, not just for high-performance files or high-performance object.
It's truly high-performance NFS, SAN, and S3, all natively, all at once. And that means unified: one system speaking every protocol, every application that you have, it speaks to it. AFX extends the same foundation where you need the scale, performance, and capacity separately at massive scale for files and objects, and then the cloud.
This is where this really opens up. The same unified storage ships first-party natively inside every major hyperscaler. This is not a virtual appliance bolted on. It's an integrated service powered by the same foundation, which means you get the same AI readiness, the same protection we just talked about, wherever it is running. Same platform, same data services, wherever you need it.
And for workloads that need something even more specialized, the platform brings that too. StorageGRID gives you best-in-class object storage at massive scale, powering enterprise data lakes. And where a workload genuinely needs Lustre, that extreme purpose-built for HPC environment, that's in the platform too. You're not going to find another vendor for that. It's already there in the platform.
Today, AFF and AFX are running enterprise high-performance workloads and powering the AI and enterprise agentic transformation. They are extending the infrastructure customers like you already trust instead of replacing it. These are the leading systems for larger enterprises pursuing agentic AI today.
Remember the retailer? They wanted infrastructure that could keep up with extreme scale when needed. That's the answer. The same platform you already trust, fast enough to keep up with whatever you're asking of it.
Think about everyone I talked to you about today. A CIO who got the best out of everything they already invested in. A researcher who finally got the data working for them. A retailer whose infrastructure now stretches with them instead of against them. Teams who finally have one console, one view instead of four. That's the platform. That's what you can turn on this week. Now, as workloads move from enterprise scale to hyperscale into AI factories, neoclouds pushing GPU demand further than anything we have ever built for before, this same unified layer must go further. And we have got such a big announcement to make here. I think there's only one person who can do it justice. So, let me welcome back George Kurian. Let's do this.
Thank you, Sham. Today, we want to share with you the most important announcement we've made in a long time. Certainly the most important announcement of Insight 2026. The most important announcement since we announced the Data Fabric in 2013 and our first hyperscaler-native storage service in 2017.
Like Sham mentioned, unlike others who ask you to rip apart your prior investments each time the world changes, NetApp extends the NetApp platform and its technology capabilities to meet the moment. Today we are in the accelerated computing era, and as we discussed earlier, this era brings new demands and new opportunities for infrastructure and system design, and new technologies that are available today. So we are introducing a new architecture: one that is disaggregated intelligently so that every single element can be optimized for performance and scaled independently. One that is architected for the performance needs of 100,000-plus GPU clusters, the unpredictable performance and concurrency requirements of the most demanding agentic workloads, and which can assimilate data across tens of thousands of systems into a single namespace to help you turn all of that data into knowledge. The possibilities that that would create are endless.
Scientific research could accelerate as researchers run complex simulations and models in real time. Biologists could harness tens of thousands of computers to watch every protein molecule in a cell. And you could have breakthroughs that are not possible today in climate science, in genomics, in healthcare, and astrophysics. And so now I ask you to imagine what could be the possibilities for your organization.
If you could have a single file system that doesn't just match the highest performance numbers on the IO500 — the benchmark for supercomputing performance in the world, which is 10 terabytes per second — but one that is 10 times larger; one that is able to deliver the first file system on the planet to be able to deliver 100 terabytes per second of performance. That's right. 100 terabytes per second of performance. The fastest storage on the planet. Period.
And in addition to performance, if we could unify all of the world's data — not just a few exabytes or a few tens of exabytes that some other architectures might be able to deliver, but imagine if you could unify all of the world's data center capacity that shipped last year, 2,000 exabytes of data, into a single namespace. The first of its kind. That's right. The first zetabyte-scale file system on the planet. Let that sink in.
And if all of this could be accomplished with no custom clients or hacked kernel loadable modules, but rather uses industry-standard Linux so that you can rapidly adopt the latest CUDA libraries, radically simplify your operations, and stay current with the latest security updates. And imagine if we told you that all of your existing NetApp investments — the reliability, the trust, the security and protection of data ONTAP — are being extended into this architecture, and so are the AFF A90s. Today, you no longer have to imagine. Welcome to NetApp Novas.
Just like the Data Fabric extended the value of the NetApp platform and its technological capabilities from the enterprise data center to the world's biggest public clouds, Novas is designed to provide the architecture for the accelerated computing and AI era to help humans transform data into knowledge. You'll hear about NetApp Novas in depth through this conference and back on this stage tomorrow during the innovation keynote. But to give you a sneak peek, please welcome one of the key architects of Novas and NetApp's chief platform and technology officer, Arindam Banerjee.
Thank you, George. Every shift in compute rewrites storage, and NetApp has led every turn. With NFS, we disaggregated storage from the server. Virtualization disaggregated data services from the protocol. And 18 months ago, we disaggregated performance from capacity. Now, as compute makes its biggest shift ever, it is time storage makes a decisive leap from a CPU-led gigabyte-per-second architecture to a GPU-led terabyte-per-second architecture.
This shift is permanent, carrying with it every property that made persistence worth having in the first place: data integrity, resilience, security. We do not get to trade those away for speed. A data outage could stall your most expensive asset. The most powerful GPU infrastructure on traditional storage is as useful as your favorite sports car on a dirt road. This is not a cycle you can wait out. AI is not a workload. AI will become a property of every workload. Training, inference, and agents do not run in isolation. They run side by side on the same infrastructure simultaneously. What we are seeing is a structural change in how the world procures, deploys, and fuels compute. GPU-first gigawatt-scale AI factories with agents driving concurrency to a level this industry has never had to serve. Massive scale, extreme performance, relentless concurrency. That's Novas for you.
Why do we need this shift? Now, let me talk about some of the operational challenges at this scale. What breaks first? When you put a thousand GPUs and agents against a single namespace, the first thing that breaks is not throughput. It is when you combine millions of metadata operations on a namespace holding trillions of files along with throughput — it's metadata. And here is the part that costs you money: your data operations get queued behind the metadata operations. The pipe is not full. Your GPUs are still idle.
Two architectures served us very well up till now. Traditional NAS scales the enterprise. It gives us shared access, data services, protection, and governance. It was designed for human operations-based concurrency and continues to serve that well. Parallel file systems scaled HPC environments. They give us bandwidth for very large sequential jobs but require the operator to tune the layout and accept a proprietary client on every node. An AI factory is both of these workloads at the same time, plus agents, and neither architecture was designed for this combination. Billions of tiny random reads for data loading while terabyte-scale checkpoint writes land across the whole infrastructure all at once. This is bimodal I/O on the same namespace at the very same second, and this is not something traditional disaggregated architectures can solve. This requires a new architecture.
And to talk about new architectures, let me call upon stage someone who knows a thing or two about parallel file systems and what it takes to operate at this scale. He has spent much of his career helping define how those systems work. HPCwire named him a legend and a godfather of parallel file systems. He has helped shape technologies such as PLFS, the burst buffer, Lustre, and pNFS. He's been a trusted adviser to our R&D team, reviewing everything from napkin sketches to architecture papers. It's an honor and a privilege for me to welcome on stage Gary Grider.
Oh, Gary, thank you for speaking to us today.
It's an honor and a privilege having you here. HPCwire named you a 2025 legend and a godfather of parallel file systems. PLFS, the burst buffer, Los Alamos National Labs, fingerprints on both Lustre and pNFS, and now GPFS. You run an environment that has roughly six million cores and a trillion files. So when you operate at this scale, what breaks first?
Uh, well, imagine a workload like you've been talking about. A million cores all opening a single file at the same time, or all trying to create a file into the same directory at the same time. Difficult things like that. Imagine 100,000 cores trying to walk your file system to find something all at the same time. And worse yet, imagine all of that all at the same time, right? Very concurrent workloads. So what breaks? First, probably metadata. You can probably scale the data pretty well, but the metadata is very difficult to scale. What would happen? Well, the metadata server would get behind. Everything would start timing out and the file system would fall over, which happens. And so it's pretty much a metadata problem to scale like that.
Well said, Gary. For 30 years, we have been designing file systems for the median use case and we always bought hardware for the peak. Now for AI factory scale and workloads, every job could be a peak, right? And all of those peaks could come at the same time. So when we started Novas our design principle was not how fast can it go, but how can it handle all these jobs arriving at the same time and overlapping with each other. So Gary, as we gear up for AI factory scale, where exactly do current architectures hit the wall?
Well, that mix of workloads that we just talked about requires you to think about parallelism along a lot of different dimensions to handle, you know, the concurrency of it. So scale-out NAS, you know, it gave us parallel data; parallel file systems gave us parallel data, but they also started scaling metadata. The problem is they didn't scale it enough, and so we need to take the next step in that scaling activity. Basically, it becomes a challenge as the compute clusters increase the parallelism and the concurrency — multiple problems at the same time — and to scale metadata commensurate with the services of scaling data, you need to do new things. And the new thing that we did was we started a project called Lattice, pNFS Lattice, and it was the world's first open-source parallel file system metadata server for pNFS. And the different thing about it is it's not just a scaled-out metadata service. We broke the service up into parts so that each part could scale separately to address these concurrent needs — a lot of bandwidth happening at the same time, a lot of inserts happening at the same time that a lot of queries are happening — and so you need to be able to scale the pieces independently, and that's what Lattice does.
Okay. So, one structural idea, Gary, that keeps reoccurring across your career as you have designed state-of-the-art systems for performance, scale, and concurrency, is disaggregation of metadata and data. In modern HPC and AI workload scale, how critical is that separation?
Well, very critical. You basically have to start there. I think the problem is most architects think that if you separate data and metadata, you're done and all your scalability problems are over. And it turns out that's really not true. And it has to do with these simultaneous stressful workloads. You can, you know, splitting up the metadata and the data works great as long as your workloads are pretty homogeneous, but the minute that you have this concurrency, there's problems. So basically you could have — in HPC we don't really have that much concurrency. We run very large jobs. They compute for a long time and then they dump everything all at the same time. It's a very homogeneous workload. Yes, it's very bursty, but it's not a lot of different workloads all at the same time. AI data centers, as you said, are quite different. They have different workloads that are doing I/O all the time, and it's all pretty much stressful. So this means that long latencies can happen for some I/O operations, and inference just makes this worse because inference has to please people, and people have no time, you know, they don't like to wait. And so just like I said, that Lattice separates data and metadata and also breaks it up into different parts — this is a really important feature of it — and that is that you need to be able to scale those different parts dynamically. So you can't just scale them out and leave them scaled out all the time because that's a waste. So what you want to do is build these pieces of the metadata service and have them scale dynamically as the workloads need it. And so that's a big important feature of the Lattice project.
And where do you think architects consistently underestimate as they build out such environments?
I would argue that the biggest thing that people underestimate is quality of service. I think quality of service at massive scale in an environment where something is failing all the time is a really hard thing to do. It's really never been done completely well, ever. So I would say that that's the thing that gets overlooked because it's just really hard.
Okay. At this level of scale failure is not an event. It can be an everyday condition. Right. So that is why the data servers in Novas run on ONTAP so that we can carry forward our decades of work on resilience. Gary, you helped steer Los Alamos National Labs' contributions to Lustre.
And Lustre's answer to metadata scale was DNE, that is, it sharded the namespace across many metadata servers. What did we learn about the limits of that approach?
Uh, well, we certainly learned that there are limits. There are workloads that Lustre DNE 2.2, you know, 2.5, or 3 work pretty well for, and there are some that they don't work very well for, and it wouldn't work very well concurrently. And so we've definitely learned a lot about what not to do and we've learned some things to do. So the Lattice project essentially — you know — it's trying to do this dynamic, multi-dimensional scaling all at the same time, and that's something we didn't try in Lustre. We should have, but we made the mistake of the metadata server being monolithic, and breaking it up gives us this ability to scale each thing independently and dynamically. But something that's less obvious in breaking up the metadata server is that if you break it up, you can break it up into pieces and leverage different intellectual communities.
So if you know, for example, scalable protocols — that we've been doing scalable protocols for years — pNFS and shmem — and scale-up networks, you know, like — and scalable catalog services: where does the metadata go? The distributed key-value store community has done a lot of that kind of work, so you can rely on their expertise. And scalable state — you know, there's state: you can't insert the same file over more than once, and so you need state to be managed. And that's kind of a shared-memory thing, a distributed shared-memory thing. And there's shmem and NVMe and scale-up networks for that.
And so the whole point here was to break it up, scale independently, fail over, and dynamically scale, and also to leverage different intellectual communities. And that's what was totally different from what we did with Lustre. And then if you add to that the fact that this is all in a pNFS ecosystem. And so basically what we're trying to do is coalesce all of these things and their constituent intellectual contributors into one solution set, a technology that people can use, and basically handle all the workloads that we can imagine into the future because it's just dynamically growable in so many dimensions.
Actually, very well said, Gary. You're very right — that is one of the unique advantages of building around standard protocols and open source: you get the community intellect to come together. So last question for you, Gary, today: if you were told to design the storage systems that would power the next decade of AI factories, what would it look like?
Well, you know, in the 26 years I've been trying to get pNFS adopted — and it looks like that's happening as we speak — we've done a lot right. We took our time and we did it right. So I think, you know, leveraging global visibility, single namespaces, you know, dynamic massively scaling dynamics, you know, and non-stop — it has to be very, very reliable and have quality of service and all that kind of stuff. So I don't really think that way. I think we've got this base that we're about to — the world is about to adopt. How do we leverage that? How do we go to the next step? What do you add to that to make the thing have more value? And for us, that becomes the big question.
And so given pNFS flexfiles and local I/O and all the fancy features that are in NFS and pNFS today, there's a lot you can do. And with the metadata broken out separately, you can do things like predicate pushdown and analytics pushdown. You know, basically this could replace Hadoop hook, line, and sinker, and it would be so much better and so much easier for everybody. Virtualized formats: if you've separated all this stuff, you don't have to present the data like the user wrote it. You can present the data like the user wants to see it, which is really different, right? And so there's no reason you can't do stuff like that. And then virtual data lakehouses. What's a lakehouse? It's a catalog full of stuff with formats. We have a catalog. We can integrate all that, and it would make everybody's life way easier and way better. And so I think that's the way we have to think about it: let's get this pNFS thing out there. Let's get it to scale to crazy widths, and then let's leverage the hell out of it with these ideas.
And I think the other thing that this does is it mates very well with global indexing — having an index, being able to find everything. Earlier it was mentioned, you know, your AI product, which is really cool. We need that to scale to trillions of files. We need that to scale to, you know, and not give away security and not give away all that stuff you said. So I think we're within reach of doing that now. I couldn't have said that a year ago, I don't think. So anyway, I think we're on the right path to make this all happen.
So, if I could summarize this, Gary, we have not only disaggregated metadata from data, we have actually opened several new frontiers. Metadata enables deep indexing, and deep indexing powers contextual search and pattern recognition. By disaggregating the metadata services from the catalog, we also enable the virtual data formats that you just said, virtual lakehouses that you just mentioned. In short, we have elevated metadata to knowledge. Gary, thank you. It has been an education for me, and I'm sure for our audience as well. Thank you so much for being with us. Thank you. Thanks, everyone.
Now let me explain how we design all of this into Novas. Let's start with the physics. At 50,000 to 100,000 GPUs, no single storage cluster can feed the fleet. Not ours, not anyone else's. Every cluster has a ceiling set by its hardware and by its interconnect. And that ceiling sits far below what AI factories demand. So we stopped trying to build a bigger cluster. Our answer is federation. Many clusters, one namespace, and the throughput of the namespace is the summation of every cluster behind it. Add a storage cluster, add its throughput — transparently, and to your applications, it is still one file system tree, one mount, one namespace. But federation demands control and orchestration. Something has to own the namespace across all of the clusters. Get that wrong and the namespace comes to a standstill and the utilization of every GPU comes to a crawl. Not with NetApp Novas.
Novas federates with linear scaling, and that federation is ours to manage, not something that you need to think about. That is our disaggregated metadata layer. It assimilates the metadata from every cluster and orchestrates the whole. It is the metadata client — or your metadata server — that your clients connect to. It ensures that every node you add can truly contribute its full throughput to the entire pool. And because it sits on its own tier, data and metadata scale on different axes. Neither one waits for the other. And you'd never have to buy one to get the other. This is how we will drive maximum GPU utilization and handle maximum concurrency.
And we designed all of this on standard protocols: pNFS FlexFiles. pNFS is an IETF standard, one that NetApp helped drive into existence. FlexFiles is what stitches a single namespace across many thousands of servers spread across all these clusters. And here is the part that I want every operator in this room to hear. pNFS ships with the stock Linux kernel. There is no proprietary client. There is no kernel module of ours on a single one of your GPU nodes. Nobody else even attempting to come to this scale can say that. Which means you set your own server upgrade cycle, your own driver matrix, your own qualification window. Your storage vendor does not get a vote on when to patch your fleet.
Now, Gary made a point a moment ago that I want to come back to because that is the one architects consistently underestimate. Disaggregating metadata from data is not enough. Parallel file systems did that 20 years ago. The hard problem is scaling the metadata operations themselves. Under bimodal I/O at full concurrency, with everything happening all at once. So in Novas we went one level deeper. We disaggregated the metadata service itself into independently scalable subsystems: a protocol plane, a state plane, and a catalog plane. Each one scales on its own axis. Each one grows exactly where the pressure is. And that is what gives you linear scaling of metadata operations. And underneath all of this, metadata is held in a distributed key-value store that flattens your file system hierarchy, and your file system lookups become lightning fast. There are no hot spots here because we never left a single place for the heat to arrive.
At AI factory scale, resilience stops being a feature. It becomes structural. In a synchronized training job, every GPU is waiting for the slowest one. One stalled read, one checkpoint that did not commit, and you have not slowed a job down — you have idled an entire cluster. So we did not trade away persistence to buy speed. The Novas data servers will run ONTAP. Which means the foundation that you have depended on for 30 years will hold good even at 100 terabytes a second. End-to-end data integrity, resilience, security, multi-tenant isolation, and quality of service. Scale without persistence is a proof of concept. Persistence without scale is irrelevant. Novas is the first architecture that refuses to choose.
And one last thing, because it is where Gary and I ended and because it is the part that will matter five years from now. Once metadata is its own tier, independently scaled on its own plane with its own catalog, it stops being bookkeeping. It becomes something you can query, your agents can query. Designed for provenance, lineage, meaning across a trillion files. We are not getting metadata out of the way. We are elevating metadata. We have designed this for planet scale. So let me finish where George started: 100 terabytes per second. One namespace at zetabyte scale, the first of its kind. We believe it's an order of magnitude beyond anything anyone else has put out in the field.
We have just designed the fastest storage on the planet for you. And with that, let me call upon someone who knows a thing or two about Novas architecture. Let's bring George back out here.
Thank you, Arindam, for all the hard work. And to all of the teams that have been involved in building Novas — man, it's been fun. The most fun I've had in a long time. And Gary, I want to thank you for the wisdom, the generosity, and for being the inspiration behind Novas. I'm grateful to all our partners who have co-innovated with us. And I want to share one more with you: SAP, with whom we are expanding our strategic partnership. This is a deepening of a long-standing relationship, one built on years of joint work supporting the world's most mission-critical enterprise environments. You'll hear more about this partnership tomorrow.
We are so excited about the work that we've done with all of you. At the dawn of each era, NetApp has built the architectures and platforms that enabled organizations and mankind to do what was not possible before. Decades ago, with network file storage, we enabled teams of engineers to share large data sets to design the semiconductors that power the AI era today. And today, as those chips and AI models have become enormously powerful, we are privileged with Novas and the NetApp platform to deliver the capabilities so that we can play our part in helping humanity and all of you solve problems that were not possible before today.
We are so excited about what we discussed today. And so I want to end where I began, with memory and context. In the agentic AI era, agents will work alongside your employees to make decisions, take actions, and drive real results for your business. And to do so successfully, agents need to have knowledge — knowledge enabled by memory, namely your data, and context, the metadata and knowledge graph that go with it. Today, the NetApp platform is uniquely designed to address and solve your data challenges for the AI era by delivering AI-ready data, unified storage, proactive protection, and complete intelligent control.
With the NetApp platform, you have everything you need to build an intelligent data infrastructure to transform your data into knowledge. So as you leave here, I have an ask of all of us: let us be builders together of a better future, not just disruptors. Let us take responsibility for, and act with agency to make the products and services that we create safe for those who trust us and use them. Let us use the enormous power and potential of these technologies to bring opportunity for everyone. And as dad to two young adults, let us replace the fear that our industry has instilled in the next generation for their future with hope, as our parents did so generously for us. Thank you all for being here. Enjoy Insight. We got an awesome conference.
Thank you for attending today's program. For the audience here in Las Vegas, enjoy the workshops. And for our online audience, stay right here for the NetApp On Air Snapshot, which is coming up next.
Welcome to NetApp On Air Snapshot, coming to you live here from Insight. I'd like to welcome all of our audience viewing online. My name is Jeff Baxter, VP of technical marketing at NetApp, and I'll be your host for the next, let's say, hour and a half or so to interview many of the architects behind the announcements that you heard today. We're behind the scenes right now in the NetApp garage, our version of the expo hall, and you're actually getting an early access, say, a sneak peek into it. The doors don't open out there until about noon Pacific. So you'll see us putting our last finishing touches on many of the exhibitions, visiting with many of the sponsors here in the expo, and being able to get hands-on with the technologies you just heard about from George, Sham, Arindam, and more. Right behind me, actually, is an amazing activation where people will be able to get hands-on with the new enhancements to the NetApp AI data engine. I'd like to invite all of you, if you want to continue to engage with us, to join us on the NetApp Discord server, available at netappdiscord.com. You can come join the conversation with us live right now, or please join us at any point in the future for a vibrant community from people at NetApp, people in our partner community, customers, and more, engaging and learning together. Now, we're very much live right now, so we'll be bringing guests in and out over the course of the next hour and a half. Excuse any chaos as we do. So, this is a live production, and we want to get a lot of the people who you may have heard from on stage, some of the people behind the stage, up here to double-click into these technologies so that you can learn more about them.
We'll take a little break around noon Pacific here for those of you on the West Coast to grab lunch, those of you on the East Coast and in Europe to perhaps grab dinner or a late snack. But be sure to tune back in at about 2 p.m. Pacific for the first of our featured sessions. We will have featured sessions at 2 p.m. and 4 p.m. today, and tomorrow at 11:00 a.m. and 3 p.m. And of course, set your alarm clock — 9:00 a.m. tomorrow morning for our day-two keynote where you'll hear even more about a lot of the major announcements, with a special double-click into NetApp Novas that you just heard announced.
Now, for anyone who joined our keynote a little bit late today, that's fine. Replays will be available on netapp.com, YouTube, and LinkedIn a little bit later on today. But I'll go ahead and give you a recap of the major announcements that were just made, all the news that just broke. So, we heard from NetApp CEO George Kurian, who reminded us that data is becoming the constraint to productively scale AI. And we made three major categories of announcements around hybrid multicloud, the agentic enterprise, and a major announcement for AI factories. With the hybrid multicloud, there were a number of different announcements. Perhaps the most notable was around our integration directly into the public cloud. NetApp was the first to have a first-party native service available, starting with Azure back in 2017, adding AWS a few years later, and then adding Google Cloud NetApp Volumes. And that gave us a presence as the only provider to be natively available on the world's three largest public clouds. Well, this morning we were happy to announce our partnership with Oracle to produce the Oracle Cloud Infrastructure NetApp Storage Service, which puts us now in all four of the world's largest public clouds.
And we announced integrations for the NetApp console to provide local deployment capabilities — the ability for people to deploy the NetApp console and access the NetApp platform either through the cloud-based service that exists today at console.netapp.com, or through the console local deployment that they could install in their own data center, even in dark sites. We also announced the integration of AI directly into the console. So people can bring their own large language model directly into the NetApp console, so they can use their own trusted AI model to manage their NetApp and their whole NetApp hybrid multicloud environment. And we announced the introduction of Keystone Sovereign, and we'll have some guests on here later today to talk about Keystone Sovereign and provide you additional details on how that will let you deploy an as-a-service version of the NetApp platform without having to go outside your sovereign's particular boundaries.
And then when it comes to the agentic enterprise, we announced the major integrations I talked about with the NetApp AI data engine. And in particular, we announced the ability to extend that to heterogeneous data, to expand it to a customer's entire data state so they can understand, gain that memory and context that George just spoke about, and bring it to the entire data state. And finally, of course, one of the major announcements: NetApp Novas, extending the scale of the NetApp platform out to the AI factory. And just a little bit later here, you'll have a chance — we'll be sitting down with Arindam Banerjee, who you heard from on stage, as well as additional members of the Novas team, to dig a little bit more into that and to get you a sneak peek into the day-two keynote where they'll be taking the stage to tell us even more details about it. So, I want to go ahead and turn to our first guest.
We have our first guest here to join us, and thank you. I'd like to go ahead and introduce Jennifer Prrener, SVP of product marketing, NetApp. Thanks for coming.
Yeah, thanks for coming. You look very good in this new role — I appreciate it.
I'll have to — Can you tell my boss that?
I will. I'll think about it.
Okay. Thank you. So, I know there were quite the set of announcements that were made today — I mean, that was just an amazing keynote. So, I know we only have a few minutes together, but what really excited you coming out of those keynotes?
God. Uh, man, if you paid attention to that keynote, it was hard not to be excited about the whole thing, to be honest. But I think Novas is game-changing. It's world-changing. And George's closing remark about, you know, utilizing these announcements to create hope for the world and hope for the next generation on what AI has the possibility and capability to do was just, you know, it hit me here. And with what we've accomplished, we've gone from always being a credible, amazing storage partner to being a full-scale data infrastructure partner, and this announcement with Novas leapfrogs our competition. And I can't wait to get out there and talk to customers and have it deployed and see what that's going to be like.
Yeah, I know. I'll admit it. I saw some of the slides ahead of time, but even when you see up on the screen 100 terabytes a second and multi-zetabyte and everything, I mean, it's just — it's an order of magnitude.
And fastest storage on the planet. I mean, as a marketer, being able to say something like fastest storage on the planet is like — it's a marketer's dream come true.
Love it. Absolutely. So, what, you know — George and Sham both talked a lot about the NetApp platform, and there's obviously been an evolution over the last few years. Tell us, tell us how the NetApp platform has sort of evolved, and it seemed to become so central in today's keynote.
Yeah, I think we've talked about the NetApp platform in a few different ways, you know, specifically about a few products here and there over time. And what the NetApp platform is, it's an intelligent, governed system that allows you to create intelligent data infrastructure in ways where you don't have to have a bunch of disparate products or disparate vendors or disparate systems. You can use your one trusted partner, NetApp, who we've been your trusted partner for 30 years. And as you saw from George and Sham and Arindam today, you know, when you think about what our platform is: unified storage — that is unified across protocols, unified across locations, unified across workloads. Like, we are allowing you to run your data and your workloads wherever you need to — in the cloud, on the edge, on-prem — and we will unify it for you so that you can have one way to manage it, one way to think about it, one way to govern it, and that is huge. That's the foundation of the platform.
Then you think about AI-ready data or AI-ready data services. And, you know, Sham talked a lot about one of the biggest announcements of the conference, I think, which is the ability to take AI — not only all of the evolution that we've talked about, but on heterogeneous data. So we are now saying, if you have data on something that's not NetApp, that's great — we still are going to help you turn that into data that works for your AI projects. Huge announcement. And then the other two: complete control — one autonomous control plane, like, that is amazing. You don't have to have separate teams running separate systems — you know, that's extra money, that's extra cost. And then finally, proactive protection. Both Sham and George talked about, you know, agents are doing things that maybe we didn't always expect, and having proactive protection at every layer so that your data is governed, it's connected, it's protected, and that you don't lose that anywhere in the system, is so important in the agentic enterprise. So with our platform, it now helps you do all of that as one, and you don't have to pick and choose. We're also not trying to be a walled garden. So we have partners who we've built in, we have our own things, and what will continue to be developed for the platform will be even more amazing.
Thanks, Jen. I know you have a bunch of other commitments, so I'll do one final question for you. As you think about what's ahead for NetApp, what really excites you most?
Oh, that is a great question. Wow. I think there's so much — NetApp Novas, obviously. I think we are — you know, we talked a lot about neoclouds today, but NetApp Novas can be extended to any large-scale AI need that customers have. Like I mentioned, it's leapfrogging the competition. So that to me — I just want to go talk to customers right now. I want to see what our competitors are saying already. But this is just the beginning, if I'm honest. Like, we are at day one of what AI can be, what we can do with our customers for AI. So I think for me, probably what I'm most excited about is just the possibilities. The possibilities are endless with NetApp as your partner. I don't know. I'm so excited that it's hard to say only one thing.
That's awesome. Well, you know, you said you can't wait to go talk to customers. I know you have to go talk to customers now, right? I have a number of them coming. I'll let you go, and thank you for joining us here.
Thanks for doing this. Look forward to seeing what folks think.
Yeah, absolutely. Thank you, Jen. And so for everyone, we'll go ahead — we have a packed agenda, we'll keep moving along. And next, we're going to keep diving in deep. We're going to talk about AI and how we get data, how we get knowledge from AI. We heard a lot from George about memory and context providing knowledge and how we extract that knowledge from data. And so our major AI data engine announcement was the ability to provide access to heterogeneous data storage — that could be any sort of open standards-based storage out there, to be able to go out, see it, attach context, attach governance to it, and get additional detail. So we'll be joined in just a few minutes by two guests here to talk about the NetApp AI data engine.
So, with the NetApp AI data engine, the other major thing is to be able to provide access to it to any of you out there who want to try it. So one of the major things in the keynote that was announced was the ability to get a free six-month trial of the NetApp AI data engine so you can map out your data. So you can check out for details about that at netapp.com. There was a QR code a little bit earlier during the keynote that you can go back and find, or it'll be available on netapp.com for you to get access to that technology.
So, we're pivoting here, and I think we'll go ahead briefly here. We're just getting our guests settled here at the last moment. So I want to go ahead and welcome Assad Khan, our senior vice president and GM for AI. Assad, thanks for joining us.
Thank you for having me here.
Appreciate it. And Darnell Fadagati, our principal product marketing manager for AI at NetApp. Thank you for being here.
Hey Darnell, good to see you. So I'll start off, and really for either of you — but Assad, obviously big announcements this morning. What excited you most about the AI announcement?
I think the biggest excitement for me is that how NetApp is able to map all of customers' environment, even if their data is in NetApp storage or it is not, because from the customer point of view it is very important for them to have a full view of their data state, and I think that is a great path for the AI to take.
Yeah, I know when I was talking to customers over the last year about the AI data engine, even like the most fervent NetApp customers — you know, maybe they have 80 or 90% of their data on NetApp, but there's always those AWS S3 buckets. So, there's always that legacy storage sitting in the corner. And I don't think we're arrogant enough to think that, you know, every customer...
In the world is going to be 100% NetApp. So, I was super excited about the development work that your team has been doing.
Um, I'd say, what sort of unique differentiators do you think the NetApp AI data engine brings to the mix there? Like, if you're using the NetApp AI data engine on your heterogeneous storage or on your NetApp storage, I guess the question would be why — what value does it bring in particular?
I think the biggest one is security and how we deal with the data and all the ACLs and all the security settings that you have — never make a copy and put it out. We bring actually the AI models and the intelligence to where the data is sitting, and that is very important because today, if you think about it, when you think from the agents' perspective, they are really good at scaling the problem, and you give them the problem statement, they will figure out a way to get around it, and in that environment the security becomes paramount. So the two things we do really well is that we understand your unstructured data better than anyone else, and the second thing is how we handle the data by making sure the data is not copied and the security settings stay intact.
Yeah, and just to add to that — security is paramount, right? But no copying, no data movement. It's also about keeping data recent, right? Like, you don't want to have stale data. You don't want your agents working off of old data, even duplicate data. So yeah, I think that's something definitely that's a big differentiator for us, and just the fact that we're covering the entire data estate regardless of whether it's on StorageGrid, NFS, S3, non-NetApp — yeah, really key.
So, Darnell, I know — correct me if I'm wrong — but I think you'll be on a featured session doing some of the demos a little bit later today, starting at 4 o'clock, right? Your featured session, 4 o'clock Pacific. For all of you watching, it'll be available on the live stream. So I'll put you on the spot. Do you want to give me a little sneak peek? Any things you'll be — you don't have to tell me the details, but what sort of stuff will you be demoing during that?
Yeah. Well, you'll see how easy it is to just connect any of your data sources and then filter it for what you're looking for. So you could literally create data sets that say, 'I want to remove anything that's unsafe for AI or irrelevant for AI,' create a nice curated data set, and then make that available to your agents through MCP or, you know, through our GUI.
So, Arindom, I know — I think it's month four for you at NetApp, something like that, right? What I'll ask you is just what made you want to join up? Obviously, you know, you came on board to run our AI initiatives, you knew AI data engine. So what interested you about NetApp? What made you want to join up?
Two things. One is that the opportunity in general — and if you look at the storage layer and the unstructured environment, it's amazing. It has never been there before. For a long, long time the companies have only been dealing with the structured data and the semistructured; now with AI, this whole new space is opening up. The second part is that the approach NetApp is taking, which is the whole platform play — not specific to storage or not a specific thing — is very much what I loved when I looked at it. And we were working very closely with NetApp even in my prior job; I loved it in terms of the platform approach and then how they are going up the stack and solving the complete scenario for the customer rather than just giving a piecemeal — this is where you store the data and then you have to go to other vendors to figure out how you complete your story. So I think that was very, very powerful for me.
Great, and thank you for doing so. We appreciate having you on board. It's been amazing what you helped develop. I guess I'll kind of do a final question here. One thing I think that's been emphasized with the NetApp platform is sort of the extensibility, and we talked a little bit about that with the NetApp AI data engine. How do we work with partners — both those that are here in our expo and others? How do we work with partners with the NetApp AI data engine to tie all that together?
Yeah. So I think that is very important that we don't create a walled garden where we give you the full solution but then this is the only thing you can go and get. AI is a space where it will always be shared, and customers will have their own opinions all the way — whether they are using a specific model or an environment. So it is important for us that we are super differentiated in our stack. We depend on open APIs and we enable the integration with the partners, and you will see some of that even in the — as you mentioned, the 4 p.m. Pacific — some of the partners we will bring on the stage, and they will talk about how we are going about it.
And so from our customers' perspective, we're not asking you to even replace the tools that you're already using. So you have that flexibility to leverage your tool of choice, and you can integrate with us and have it seamlessly integrated.
I love that. That's one thing that drew me to NetApp in the beginning and has kept me here, right? We're not trying to monopolize everything, right? We recognize it's an ecosystem. We connect into all that.
Absolutely. Well, thank you for spending the time with us. I know you've got customer meetings and many other things to get to, and I'll plug again your featured session at 4:00 p.m. Pacific on the live stream, and obviously you'll be able to catch replays later for anyone there who wants to do it, and really looking forward to it.
Thanks for taking the time. I appreciate it.
No, thank you for having us.
Thank you. Thank you, guys.
Okay, we're moving right along here on NetApp on Air Snapshot. We're going to go ahead and start to double click down into our next topic. If you were watching the keynote, you may have seen a major announcement just happen — NetApp Novas. George actually talked about it being the biggest announcement in over a decade, going back to when we first introduced Data Fabric back in 2013, 2014, and first extended to the hybrid multicloud. This is almost taking that Data Fabric to the final frontier.
Taking it out to these AI factories, these neoclouds, and getting to this AI factory scale of — I can't believe I'm saying this — but multi-zetabyte, 100 terabytes per second. Just amazing things that the team has been able to do. And we're going to hear directly in a moment from some of the key architects behind NetApp Novas. So with NetApp Novas, and just to recap that announcement in case you're coming in a little bit late to the keynote: NetApp Novas is our new offering for AI factories. It provides the ability to separate and independently scale metadata from data, allowing the achievement of this massive sort of throughput, resiliency, and overall performance, and the ability to scale metadata separately from data to achieve these tremendous results.
While still on the back end for those data storage servers, leveraging NetApp's 30-plus years of rich history, innovation, and production resilience available from ONTAP. So we're going to be joined here — we're just making our final touches to get our guests up here — we're going to be joined by Arindom Banerjee, chief platform and technology officer. If you were watching the keynote, he may look very familiar to the guy you just saw about 15 minutes ago, and joined by Gona Mariputi, our VP of product management for Novas, two of the driving forces behind Novas. Gentlemen, thanks for joining this morning. I appreciate it.
Thank you, Jeff. Jeff, good. Thanks for having us, and thanks for running over from the keynote stage. You did great. Very, very nice. I love the demos. Those were good.
So, not to put you on the spot — I don't think you were put on the spot — but how's it feel having the CEO say you're in charge of the biggest announcement that we made in about a decade? Like, what makes this such a huge announcement for George, for you, everything like that?
So the CEO announcing that is a responsibility, and you need to enjoy that responsibility, right? Pressure doesn't come to everybody. It only comes to a select few. So take that pressure. But where we are excited is about what we are doing. As you just heard, this is an order of magnitude better than what anybody else has put out in the field.
Right. So now we are ready to take on the largest gigafactories or AI factory sovereign clouds at scale. Nobody will have a scale that comes even near to us. Nobody will have performance that comes near to us. Nobody can do this on standard protocols like we do, and nobody can have all their data integrity and security managed by 30 years of ONTAP that we can do.
Wow. Why shouldn't we be excited? Just a small thing, right? Yeah. I mean, like, amazing, amazing innovation. Gonu, any thoughts on that? I mean, I know Arindom said so much there, but anything that adds on to you?
No — the most important thing Arindom keeps telling us is that AI — Sham was actually telling us earlier today — like, AI is a horizontal. We're actually preparing for the future where AI is going to hit everybody, and these innovations are not just for the gigafactories or like when you really need thousands of GPUs. We're going to serve every use case.
Yeah. Yeah, AI is moving beyond just being a workload, right? I mean, it's intrinsic into the entire infrastructure.
So, Arindom, you were joined by Gary Grider this morning. And I wish I could get a cool title like this, right? The godfather of HPC, right? Like, I don't know who gives those out, but I've got to be a godfather of something at some point. Godfather of PowerPoint — I don't know if that's a thing. But, so, obviously, behind Lustre, and now basically in a lot of ways endorsing this approach. That must have been a very cool moment. You've been working with Gary for a while on this.
Yes. So George and I met Gary in Santa Fe in June. We actually flew down there to talk to him. And when we started talking about Novas, he absolutely resonated. And when I asked him, 'Gary, is this something you would like to build?' he says, 'Yes, this is what I would like to build,' given the scale and the enormity of the workloads that we are seeing. So those three hours with Gary were like soaking in a ton of information, but also realizing where the world is moving — that this person has really seen it all over the last 26 years, as you said. And the amount of wisdom he shared did enable us to make some of the very smart design choices for Novas.
That's awesome. So, Gonu, let me ask — NetApp has this fairly full portfolio now using ONTAP, right? With AFF, AFF A, Novas. What kind of differentiates them? How should our audience sort of think about these offerings when it comes to AI?
Oh, absolutely. Like, with AFF today, that's our mainstream portfolio product — all the enterprise workloads. You know, we deliver — like what I'm talking about — data management, resiliency, all the critical capabilities that run your operations day in and day out. AFF A actually gives that very nice combination of capacity, performance, disaggregation, so it meets, you know, certain use cases. Novas — like, you know, as we talked about earlier — it is going to give us that unbridled scale that we never imagined. Like, in fact, no more imagining: we're actually here today, not only in the context of AI gigafactories, like the next-generation EDA workflows. Anywhere scale is paramount, performance is your friend across all these three platforms.
So, I don't want to spoil anything, but I've gotten to play with Novas a little bit. And
Only a little bit. And the interface itself — it's so obvious how provider-centric and tenant-centric is built into that. Can you talk a little bit about that ethos and the design?
Yeah, thank you. One of the things we talk a lot about on ONTAP, right? Maybe we are the only storage company that works with all the hyperscalers now, including the fourth hyperscaler that got announced today. The good thing is we learned a lot. Like Arindom talked about, talking to Gary gives a different perspective of the scale we need to think about when you work with these large hyperscalers. Operations do matter. Operational complexity does matter when they work at this scale. We'll talk about how to handle failures — not revealing too much, tomorrow — because this is not an exception. So the ethos basically is to reduce the operational toil as much as possible. You know, in the current world, everything is going to be agentic apps, so we're going to leapfrog, not just providing UI dashboards — a general holistic view of simplifying your apps.
Yeah, I think the sense of scale — getting your mind around it, right? If you're used to running a few racks of storage like I did 20 years ago and you walk into some of the largest enterprise data centers, and then you think about what these gigawatt-scale AI factories really look like, and the operations just have to be totally different at that scale. So I'll do a final question, and I'll do two parts to it. First of all, if there's anything else you want to add, please feel free. Second of all, you're both on tomorrow morning at 9:00 a.m. Pacific — this is a programming note — day two keynote, tune in. So you're both on there to help double click into Novas. Any more sort of hints or sneak peeks of what you're doing, tell us without spoiling it. We don't want to get certain people mad at us, but any sort of sneak peek about what you're doing.
We would like everybody to attend our 9 a.m. keynote tomorrow, where we talk about the Novas architecture. And you will know why we chose this path and why certain architectures that the world sees today will not work at this scale. It's very important for people to tune in and understand why we chose this path.
Yeah, it's — you know, I've had the opportunity to kind of watch and work with you guys on it. And it's just what you've put together for the hour-plus tomorrow is just a master class in designing a data infrastructure for AI factories. So, I think I stole your line. Sorry.
I think we're also going to announce some partnerships with ecosystem partners who are coming along with us to the party, and some core innovations and the timelines. You know, it's not just architecture — we're also going to deliver very soon.
Yeah, that's one of the cool things to me: a lot of this is working. It's not vaporware — it's there today. And so you'll be talking more about the timelines tomorrow with it. And I love what you said — it's not a closed ecosystem, right? None of these AI factories is going to be closed off. I mean, for one thing, we're providing this amazing data layer for the AI factory, but there's everything above it. So from NVIDIA to all of the different people that are involved in the AI ecosystem, it's obvious the thought that's gone into making sure that this truly does play well with others.
Yes, that we work with the community. We have a lot of modeling that we have done in our labs at scale. So we have seen linear scale every time we added building blocks to this. So that gives me a lot of confidence, and the fact that we are launching it as a product very soon — we'll talk about that. I'm not going to steal Gonu's thunder for tomorrow.
So tune in to our keynote tomorrow. Welcome on board.
Absolutely. Well, thank you. I mean, it's amazing work that you have done and all of your teams, by the way. I know you have amazing engineering teams behind you. So thank you for everything you've done. I mean, we've all been here a fair amount of time — I've been here 18 years, you guys have been a fair stretch — and this is, I second George, one of the most exciting announcements that I've seen at NetApp. So congratulations, and thanks for the leadership on that.
Thank you. Appreciate it.
Thank you. Appreciate it. Talking to you.
Okay, we are going to keep rolling ahead. And again, remember: if you want to hear more — well, even if you don't want to hear more — from Arindom and Gonu, you got to tune in tomorrow at 9:00 a.m. Pacific for the day two keynote to hear more about this amazing innovation in AI factories. So let's change tack a little bit and talk about how you can best consume or take advantage of the NetApp platform.
Obviously, we talked about the ability to have the NetApp platform available in now the world's four largest public clouds, and for 30 years plus we've brought elements of the NetApp platform into customers' data centers, including today the announcements around Keystone Local and making it possible to run more of the platform within dark sites or within sovereign environments. But a lot of customers — an increasing number of customers every day, every year, every month — are adopting the NetApp platform as a service, and they're choosing to do that through NetApp Keystone. NetApp Keystone is the complete as-a-service way to consume the NetApp platform. And we made some exciting announcements this morning, including the ability to take Keystone and have sovereign instantiations of it — so that in various countries and regions, customers can choose to have Keystone deployed, have it available, and all of their data, telemetry, support, everything they need for that done within that region or geography. So that's an exciting announcement for Keystone. And I'm joined by two guests here to tell us even more about that announcement and even more with Keystone. So I want to introduce Shamela Sarard, the director of NetApp Keystone, and Dale Degan, director of product marketing for Keystone. Thank you both for joining.
Appreciate it. Thank you so much.
So, I'll start, Shamela. What are you hearing from customers right now? What's sort of driving this interest in as-a-service in general?
Yeah, I'm going to start with a statement. I think a lot of people who are listening will resonate with that: we are seeing that a lot of data demands are changing day by day. People are buying storage, and now the time is that they want to replace it, refresh it, and the costs are up. That wasn't planned or forecasted, and now by the time they plan it, we don't know where it's going to be in the next couple of years either. So we see this as a challenge where customers are trying not to take a bigger bet on how much data they should buy up front. So we see that there's a sentiment among customers that they want to buy something that is more for now, and then they keep growing from there.
And additionally, AI is landing on everyone's road map now. So a lot of customers who are investing in the AI infrastructure — there's a data demand, and no one is able to 100% predict where they're going to be in the next five years, right? So that has shuffled a lot of road maps. A lot of money that was going into infrastructure has now been shifted towards the AI projects. So storage as a service starts becoming more viable for those things where there is not a lot of predictability, where we know there are unseen situations. So I think STaaS would be one of the areas that is more popular when you don't know what you're betting on. So I would put it in a very simple statement: you're basically betting on your forecast. So if you want to bet, it means that you are maybe growing up or buying low. It means if you guess low, then you might be buying again in two years. And if you guess high, then there's a lot of cash that you invest in the capacity that is not being used, which could have been shifted towards your other initiatives. So it's all about what bet you make, and storage as a service reduces that risk because you don't have to bet on that.
Excellently said. Yeah. And that — it's really about sort of shared risk, right? You're sharing the risk with your provider, in this case, NetApp. And the program we have for that is NetApp Keystone. I think a lot of people watching would be familiar with it, and I kind of explained it a little bit while you were getting up here. But you're obviously the owner, you're the leader on it. Tell us — for anyone who doesn't know — what is NetApp Keystone?
So NetApp Keystone is exactly solving the issue that we were trying to address: it's storage as a service. You don't have to buy up front — you exactly need to buy what you need right now. So you don't have to forecast for five years, and then you can just grow from there. So it gives you that comfort, and there's a predictability just for the near future. You don't have to plan for the next five years. So that gives you the comfort to start from there, and Dale will share more insights later on about how we are continuously enhancing the storage as a service. So yeah, I think that's a great case for the current AI situation we have.
So what makes you know — Keystone obviously isn't the only as-a-service offering. The whole industry seems to be moving that way. What makes Keystone specifically different?
So I'm going to use two words for that: one is flexibility, and the second is choice. So there are a lot of storage services around — there's one way to buy it, and then you are going to hold on to it. How Keystone differentiates is that you buy what you need. You have a committed capacity — you can decide how much capacity you require, or you can decide you're paying for the storage after the efficiency. You have a standard, decided per-TB rate that you're holding on to, and you can also get the headroom of 20, 40, or 60% on top of that, exactly on the same rate that you have offered before. So that's the flexibility you have, and additionally going forward, if you're longer-term committed to Keystone, you might want to size down because you're seeing a low demand into certain workloads — you can size down as well. So Keystone is providing you that flexibility that you can size up when you need and you can size down when required. You can add more capacity as you require. So I think that's the biggest differentiator for Keystone over others.
Awesome. So I do hear — and you kind of teased this before — I hear more is coming from Keystone, even more than we heard about this morning. You want to give us kind of a sneak peek or an update on what's coming out?
Yeah, I think this is one of the things we're most excited about. We heard our customers, we meet them day to day, we learn what are the things that are working for them, the things that are not working for them, and have redesigned. And it's a continuous, evolving process for Keystone to improve. So Dale is going to share the announcements that we have for Insight.
Sure. Thank you. So, as we were discussing, we're listening to our customers, and they have different concerns on there. And one of them we hear consistently is: am I paying for performance that I don't need? And the background on that is many of the offerings in the market are based on all flash, and as you know, with the economics of everything, that's getting quite pricey. We're actually launching a new performance service level called Standard Hybrid. It's based on our FAS — it's a mixture of flash and hard drives. And it's a perfect balance of performance and cost. It's a great fit for all your classic virtualization, the backup and recovery, different types of file shares. So we find that it's a really compelling offering that can give you the price and performance fix that you're looking for.
The second issue a lot of our customers are asking about is: what will the growth cost me? It's interesting in this consumption model. Imagine a situation where you have a terabyte at $10 — just making these numbers up for the story — and after a while, you actually grow beyond what you purchased and you need to buy more, but now the market price is at $15. And that overall balances out and increases your overall dollar per terabyte quite a bit. We're introducing the capacity add-hold pricing. So effectively, if you're using at 80% consumption or higher — which is great, because that means you're perfectly optimizing your investment with Keystone — if you're able to do that for 90 days, that guarantees that you'll still get the same price you started with. And I think that's a real compelling differentiator, and it's a real value-add that our customers are looking for.
Thirdly, the last thing a lot of our customers are looking for is just more guarantees and things they can trust. And so we're turning a lot of our promises into contractual guarantees. We've got a six-nines service level agreement, where if for any reason that's violated, we'll credit back the customer. In a similar sense, we've got the storage efficiency SLA — if we don't hit the storage efficiency levels that we promise, same thing: we're monitoring it, we're proactively going to build that back and make sure the customer is right. There's a multi-protocol SLA, which I think is great — it allows you to manage and bill all your block, file, and object all on one system, and I think that really simplifies the story for the customer.
And then, if you were watching this morning, we announced Keystone Sovereign. This is specific for all the customers that are looking for those sovereign capabilities, where we enable them to not only specifically define where their data is, but also where the telemetry is, where the support is, where the management of this is. So we enable our customers to define their sovereign criteria as they need, and we give them all the bells and whistles to make that happen. So again, I think it's all about that flexibility and listening to our customers.
Yeah, I love that we've started to move into — and over the last year, done more and more with sovereign cloud and some of the major hyperscalers — and now we're matching that so people can do a similar sort of thing as a service in their on-prem data centers to complete the sort of hybrid multicloud.
Yeah. And actually, I want to give a shout-out: tomorrow in the featured session on the NetApp platform, we're going to go a little bit deeper on both of those topics. We're going to have customers that are doing it. We're going to talk about how our Keystone offering provides it, and it's really an important topic on top of mind not only for European countries but also regulated industries like financial institutions and healthcare and things like that. And people are looking for answers on this.
