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Increasingly, the developer focuses on intent, constraints, architecture, and specifications, while software takes on more of the work of turning those decisions into code.",[],{},{"nodeType":634,"data":17132,"content":17133},{},[17134],{"nodeType":633,"value":17135,"marks":17136,"data":17137},"When you change the layer you operate at, you change the tools you need. An AI application may reason about a problem, choose a tool, gather new information, reconsider what it knows, and continue working. That makes the runtime fundamentally different from the deterministic application logic developers have spent decades optimizing around. You can still have unit and integration tests, but they are no longer sufficient on their own. Developers now have to understand how an agent arrived at an answer, whether it chose the right path, and how reliably it behaves across a range of situations. That work increasingly happens inside the agent runtime itself.",[],{},{"nodeType":634,"data":17139,"content":17140},{},[17141],{"nodeType":633,"value":17142,"marks":17143,"data":17144},"A new layer, the model layer, has also become much more complicated. A single application may use one model for coding assistance, another for low-latency classification, another for difficult reasoning, and entirely different systems for voice, images, or video. Even when several tasks use the same underlying model family, developers still have to make decisions about serving, routing, context limits, caching, latency, cost, and how much infrastructure to dedicate to each workload. I would describe this as a model services layer because the developer is not choosing a single model. They are managing access to a changing set of capabilities.",[],{},{"nodeType":634,"data":17146,"content":17147},{},[17148],{"nodeType":633,"value":17149,"marks":17150,"data":17151},"All of those models need context, and this is where the data side of the developer stack changes most dramatically.",[],{},{"nodeType":634,"data":17153,"content":17154},{},[17155],{"nodeType":633,"value":17156,"marks":17157,"data":17158},"The old application world was built around relatively stable data abstractions such as files, objects, relational tables, and logs. While they remain, useful context now comes from much more dynamic sources. An agent may need to understand what just happened in a live business process, retrieve semantically related material through vectors, remember what happened earlier in a long-running interaction, inspect the result of a tool call, and combine all of that with the authoritative data already held by the application. New AI powered applications have to continuously assemble the right context from information that may have been created in very different ways and at very different times.",[],{},{"nodeType":634,"data":17160,"content":17161},{},[17162],{"nodeType":633,"value":17163,"marks":17164,"data":17165},"I think of these four surfaces as the CAMP stack: Coding Agents, Agent runtime, Model services, and Platform. CAMP is not intended to describe every component inside an AI application. It describes the parts of the environment that increasingly shape a developer's daily work.",[],{},{"nodeType":634,"data":17167,"content":17168},{},[17169],{"nodeType":633,"value":470,"marks":17170,"data":17171},[],{},{"nodeType":17115,"data":17173,"content":17174},{},[17175],{"nodeType":633,"value":17176,"marks":17177,"data":17179},"Integration Made LAMP Work",[17178],{"type":17122},{},{"nodeType":634,"data":17181,"content":17182},{},[17183],{"nodeType":633,"value":17184,"marks":17185,"data":17186},"The comparison with LAMP becomes more useful when you look at why LAMP worked so well. Its components were well integrated. Linux gave Apache a strong multiuser operating environment and mature networking. Apache modules such as mod_perl and mod_php brought application execution directly into the web server. A URL that a web browser served was literally a direct pointer to the code. Programming languages had mature database libraries that could persist state in a database as easily as in a variable. A developer did not need to constantly build translation layers between them.",[],{},{"nodeType":634,"data":17188,"content":17189},{},[17190],{"nodeType":633,"value":17191,"marks":17192,"data":17193},"That integration mattered because the stack behaved like a system rather than a collection of products. I do not think CAMP can succeed unless it develops the same property.",[],{},{"nodeType":634,"data":17195,"content":17196},{},[17197],{"nodeType":633,"value":17198,"marks":17199,"data":17200},"Consider what happens when an application needs semantic search. If an object has to be copied into a separate system before it can be represented as a vector, the developer inherits another data pipeline and another copy that has to remain synchronized with the source. The problem becomes more serious when security is involved. If the permissions protecting the original information do not remain attached to the semantic representation derived from it, then the application can retrieve something the user was never entitled to see. At that point the vector system is no longer simply a useful capability. It has become another independent data estate that the developer has to govern.",[],{},{"nodeType":634,"data":17202,"content":17203},{},[17204],{"nodeType":633,"value":17205,"marks":17206,"data":17207},"The same issue appears with live data. If an agent is supposed to react to what is happening in the business right now, but the event first has to be landed somewhere, transformed by another service, and then re-ingested before the agent can use it, the architecture has introduced delay and complexity precisely where the application was supposed to become more responsive. I think the platform underneath AI needs to make these different forms of context feel much closer to the underlying data than they often do today.",[],{},{"nodeType":634,"data":17209,"content":17210},{},[17211],{"nodeType":633,"value":17212,"marks":17213,"data":17214},"The requirement for integration extends upward through the rest of CAMP as well. An agent runtime should be able to use different model services without forcing the application to be rebuilt around every provider. Model services should be able to retrieve context without every data source requiring a custom movement pipeline. Coding agents need enough visibility into the rest of the system to understand what they are changing and why an application behaves the way it does. Security rules and operational history cannot disappear every time execution crosses from one surface into another.",[],{},{"nodeType":634,"data":17216,"content":17217},{},[17218],{"nodeType":633,"value":470,"marks":17219,"data":17220},[],{},{"nodeType":17115,"data":17222,"content":17223},{},[17224],{"nodeType":633,"value":17225,"marks":17226,"data":17228},"Developers Still Need the Controls",[17227],{"type":17122},{},{"nodeType":634,"data":17230,"content":17231},{},[17232],{"nodeType":633,"value":17233,"marks":17234,"data":17235},"LAMP had another quality that is easy to underestimate now: developers could configure almost everything.",[],{},{"nodeType":634,"data":17237,"content":17238},{},[17239],{"nodeType":633,"value":17240,"marks":17241,"data":17242},"The software infrastructure was something developers could shape. Apache rewrite rules allowed a developer to change how requests flowed through an application. Database schemas and indexes were part of application design. Process limits and operating system settings were fair game when the application demanded it. ",[],{},{"nodeType":634,"data":17244,"content":17245},{},[17246],{"nodeType":633,"value":17247,"marks":17248,"data":17249},"I think that matters even more for CAMP because the architecture is still evolving so quickly. Developers need to decide which model should handle a particular task and what should happen when that model is slow, expensive, or unavailable. They need to control how context is assembled and how much of it is retained. They need to decide when an agent can call a tool and what happens when that tool fails. They need enough control over the platform to determine how semantic data is produced and how live events enter the application.",[],{},{"nodeType":634,"data":17251,"content":17252},{},[17253],{"nodeType":633,"value":17254,"marks":17255,"data":17256},"Those are developer control points now. Hiding them behind fixed abstractions can quickly become a constraint.",[],{},{"nodeType":634,"data":17258,"content":17259},{},[17260],{"nodeType":633,"value":17261,"marks":17262,"data":17263},"That is why I think CAMP is a useful way to think about modern AI development. Coding Agents, Agent runtime, Model services, and Platform are becoming the surfaces developers spend their time inside. Their value will depend heavily on how naturally they work together, and on whether developers retain enough control to shape the stack around the application they are trying to build.",[],{},{"nodeType":634,"data":17265,"content":17266},{},[17267],{"nodeType":633,"value":17268,"marks":17269,"data":17270},"LAMP became powerful because a developer could move through the entire stack without constantly fighting the boundaries between its components. CAMP will need to do the same.",[],{},{"nodeType":634,"data":17272,"content":17273},{},[17274],{"nodeType":633,"value":470,"marks":17275,"data":17276},[],{},{"nodeType":17115,"data":17278,"content":17279},{},[17280],{"nodeType":633,"value":17281,"marks":17282,"data":17284},"Putting Names to the CAMP Stack",[17283],{"type":17122},{},{"nodeType":634,"data":17286,"content":17287},{},[17288],{"nodeType":633,"value":17289,"marks":17290,"data":17291},"It helps to put some names against these surfaces, because CAMP is already taking shape around us.  Coding Agents, Agent runtimes, Model services, and Platforms exist today.",[],{},{"nodeType":634,"data":17293,"content":17294},{},[17295],{"nodeType":633,"value":17296,"marks":17297,"data":17298},"The Coding Agent market is probably the easiest to recognize. Cursor, Claude Code, Codex, and Factory have become prominent examples of a new interface between developers and code. They are hardly alone. Pi, for example, is a popular open-source project exploring a different version of the same idea. The implementations vary, but the direction is clear. Developers spend part of their day directing software that writes software rather than entering every instruction in the programming language themselves.",[],{},{"nodeType":634,"data":17300,"content":17301},{},[17302],{"nodeType":633,"value":17303,"marks":17304,"data":17305},"I am going to skip A for a moment and move directly to Model services, because that market is already sprawling. OpenAI, Anthropic, Google, and xAI continue to push frontier general-purpose models forward. NVIDIA, Meta, Alibaba, Mistral, Cohere, and others are producing models that can be operated with control over where and how they run. Then there are increasingly specialized models. TwelveLabs, for example, focuses on understanding video, while Deepgram focuses on voice. Every new application has access to a collection of intelligence and capabilities.",[],{},{"nodeType":634,"data":17307,"content":17308},{},[17309],{"nodeType":633,"value":17310,"marks":17311,"data":17312},"Those models can be consumed in very different ways. A developer can rent GPU capacity from providers such as CoreWeave, Nscale, or Nebius and run the model directly, paying primarily for the infrastructure it occupies. Or they can consume inference as a service and pay for the work the model performs on a token basis, through services such as Together AI, Baseten, or Crusoe Managed Inference. You can choose whether you want to own more of the serving stack and pay for compute, or consume model intelligence as a service and pay for usage.",[],{},{"nodeType":634,"data":17314,"content":17315},{},[17316],{"nodeType":633,"value":17317,"marks":17318,"data":17319},"That brings us back to A and P, and there is a reason I have left them together.",[],{},{"nodeType":634,"data":17321,"content":17322},{},[17323],{"nodeType":633,"value":17324,"marks":17325,"data":17326},"VAST takes a different approach to A and P by providing them as parts of the same AI Operating System. There are individual capabilities underneath that description, including AgentEngine, DataEngine, DataBase, DataStore, PolicyEngine, and TuningEngine. I think the more important architectural point is that those pieces share a platform. The agent runtime and the data it reasons over do not begin life as separate islands that developers then have to integrate.",[],{},{"nodeType":634,"data":17328,"content":17329},{},[17330],{"nodeType":633,"value":17331,"marks":17332,"data":17333},"That changes some surprisingly fundamental things. Semantic understanding, for example, does not require creating an independent vector estate disconnected from the source data. Vectors can behave more like another representation of the underlying information, retaining their relationship to the source and its permissions. Structured filtering and vector similarity search can operate together in the same query rather than requiring the developer to shuttle data between unrelated systems. When something changes, the platform can produce an event and trigger work against the same data rather than waiting for another pipeline to copy it somewhere else.",[],{},{"nodeType":634,"data":17335,"content":17336},{},[17337],{"nodeType":633,"value":17338,"marks":17339,"data":17340},"The same integration matters as an agent continues working. Its execution history can become part of the observable state of the system. Events can be followed through the work they trigger and toward the resulting outcome. Data can remain available through the interfaces traditional applications already use while simultaneously becoming context for AI. Even when an application deliberately creates another copy, VAST's underlying data reduction can prevent that logical copy from consuming any raw capacity.",[],{},{"nodeType":634,"data":17342,"content":17343},{},[17344],{"nodeType":633,"value":17345,"marks":17346,"data":17347},"This is the part of CAMP where I think the old LAMP lesson becomes especially relevant. Nobody remembers LAMP because Linux, Apache, MySQL, and Perl happened to be four popular products. They worked well together while still giving developers enormous freedom to configure each layer. VAST's role in CAMP is to provide that tightly integrated A and P foundation while leaving the C and M layers open. A developer can choose Cursor or Codex today and something else tomorrow. The same application can reach models from OpenAI, Anthropic, NVIDIA, Alibaba, or a model running on its own infrastructure. The parts of CAMP that need to share data, identity, state, and execution context can still behave as one system.",[],{},{"nodeType":634,"data":17349,"content":17350},{},[17351],{"nodeType":633,"value":470,"marks":17352,"data":17353},[],{},{"nodeType":17115,"data":17355,"content":17356},{},[17357],{"nodeType":633,"value":17358,"marks":17359,"data":17361},"Giving the New Stack a Name",[17360],{"type":17122},{},{"nodeType":634,"data":17363,"content":17364},{},[17365],{"nodeType":633,"value":17366,"marks":17367,"data":17368},"CAMP gives a name to the way many developers already build AI applications today.",[],{},{"nodeType":634,"data":17370,"content":17371},{},[17372],{"nodeType":633,"value":17373,"marks":17374,"data":17375},"Coding agents are already part of daily development. Agent runtimes are already becoming a core application surface. Model services are already consumed through APIs, hosted inference, and rented GPU capacity. The Platform layer is where much of the remaining architectural work is happening, because AI applications need live data, vectors, events, memory, and operational context to behave as parts of one system rather than separate services joined by pipelines.",[],{},{"nodeType":634,"data":17377,"content":17378},{},[17379,17383,17389],{"nodeType":633,"value":17380,"marks":17381,"data":17382},"That is where VAST fits. The ",[],{},{"nodeType":1343,"data":17384,"content":17385},{"uri":13054},[17386],{"nodeType":633,"value":13325,"marks":17387,"data":17388},[],{},{"nodeType":633,"value":17390,"marks":17391,"data":17392}," brings the Agent runtime and Platform layers together, while leaving developers free to choose the coding agents and models they want. AgentEngine can work directly with the same data, events, vectors, permissions, and execution history managed by the rest of the platform, so developers do not have to rebuild those relationships at every layer of the application.",[],{},{"nodeType":634,"data":17394,"content":17395},{},[17396],{"nodeType":633,"value":17397,"marks":17398,"data":17399},"LAMP succeeded because its components were deeply integrated without taking control away from the developer. I think the same requirement now applies to CAMP. 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Build. 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Introducing the VAST Builders Challenge.",{"metadata":17642,"sys":17645,"fields":17652},{"tags":17643,"concepts":17644},[],[],{"space":17646,"id":17648,"type":566,"createdAt":17649,"updatedAt":17649,"environment":17650,"publishedVersion":1237,"revision":789,"locale":512},{"sys":17647},{"type":495,"linkType":496,"id":497},"734fTHCUNCeZIhZLKBOv49","2026-08-18T16:07:01.879Z",{"sys":17651},{"id":504,"type":495,"linkType":505},{"title":17653,"description":470,"file":17654},"CR-13443 BFI VAST Builders Challenge",{"url":17655,"details":17656,"fileName":17659,"contentType":583},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F734fTHCUNCeZIhZLKBOv49\u002Fa684a97fa67cb119a379159bd86f1518\u002FCR-13443_BFI_VAST_Builders_Challenge_.webp",{"size":17657,"image":17658},85572,{"width":580,"height":581},"CR-13443_BFI_VAST Builders Challenge_.webp",{"nodeType":635,"data":17661,"content":17662},{},[17663,17670,17677,17684,17691,17710,17729,17736,17743,17750,17757,17764,17827,17834,17841,17848,17855,17862,17869,17876,17883,17890,17923,17930,17937,17944,17951,17967,17974,17981,17988,18057,18064,18071],{"nodeType":634,"data":17664,"content":17665},{},[17666],{"nodeType":633,"value":17667,"marks":17668,"data":17669},"Building an AI application is easier than ever. Building one that works in the real world is still a challenge.",[],{},{"nodeType":634,"data":17671,"content":17672},{},[17673],{"nodeType":633,"value":17674,"marks":17675,"data":17676},"Developers today have access to frontier models, increasingly capable AI coding assistants, and cloud infrastructure that can scale on demand. But moving from an impressive prototype to a reliable application requires solving a much harder problem: wrangling all the pieces.",[],{},{"nodeType":634,"data":17678,"content":17679},{},[17680],{"nodeType":633,"value":17681,"marks":17682,"data":17683},"Where does the data live? How does it flow into applications? How do models access the information they need? How do developers combine APIs, agents, event processing, and application logic into a system that can respond quickly and operate reliably?",[],{},{"nodeType":634,"data":17685,"content":17686},{},[17687],{"nodeType":633,"value":17688,"marks":17689,"data":17690},"The challenge is no longer just building a model. It is building the complete AI system around the model.",[],{},{"nodeType":634,"data":17692,"content":17693},{},[17694,17698,17706],{"nodeType":633,"value":17695,"marks":17696,"data":17697},"At VAST, we believe developers should spend more time building and less time stitching together disconnected infrastructure. That belief is at the core of the ",[],{},{"nodeType":1343,"data":17699,"content":17700},{"uri":13054},[17701],{"nodeType":633,"value":4309,"marks":17702,"data":17705},[17703],{"type":17704},"underline",{},{"nodeType":633,"value":17707,"marks":17708,"data":17709},": a unified platform designed to bring together data, compute, database capabilities, and AI services so developers can build and deploy modern AI applications faster.",[],{},{"nodeType":634,"data":17711,"content":17712},{},[17713,17717,17725],{"nodeType":633,"value":17714,"marks":17715,"data":17716},"That is why we are launching the ",[],{},{"nodeType":1343,"data":17718,"content":17720},{"uri":17719},"https:\u002F\u002Fwww.vastdata.com\u002Fvast-builders-challenge",[17721],{"nodeType":633,"value":17722,"marks":17723,"data":17724},"VAST Builders Challenge",[],{},{"nodeType":633,"value":17726,"marks":17727,"data":17728},": a three-city, hands-on series for AI engineers, software developers, data scientists, and platform architects who want to build real applications using a modern AI stack.",[],{},{"nodeType":634,"data":17730,"content":17731},{},[17732],{"nodeType":633,"value":17733,"marks":17734,"data":17735},"This is not a conference where you watch demos and listen to talks. It is a day dedicated to building, experimenting, and collaborating with developers solving similar challenges.",[],{},{"nodeType":17115,"data":17737,"content":17738},{},[17739],{"nodeType":633,"value":17740,"marks":17741,"data":17742},"\nBuilding with the Modern AI Stack",[],{},{"nodeType":634,"data":17744,"content":17745},{},[17746],{"nodeType":633,"value":17747,"marks":17748,"data":17749},"Today's AI applications bring together many different technologies: data platforms, accelerated computing, AI models, developer tools, and application frameworks.",[],{},{"nodeType":634,"data":17751,"content":17752},{},[17753],{"nodeType":633,"value":17754,"marks":17755,"data":17756},"The VAST Builders Challenge gives developers the opportunity to experience how these components work together.",[],{},{"nodeType":634,"data":17758,"content":17759},{},[17760],{"nodeType":633,"value":17761,"marks":17762,"data":17763},"Participants will build applications using the VAST AI OS and technologies from our ecosystem partners, including:",[],{},{"nodeType":17765,"data":17766,"content":17767},"unordered-list",{},[17768,17783,17798,17813],{"nodeType":17769,"data":17770,"content":17771},"list-item",{},[17772],{"nodeType":634,"data":17773,"content":17774},{},[17775,17779],{"nodeType":633,"value":833,"marks":17776,"data":17778},[17777],{"type":17122},{},{"nodeType":633,"value":17780,"marks":17781,"data":17782},": A unified AI data platform leveraging the NVIDIA AI Data Platform reference design that provides the foundation for managing, processing, and activating data for AI workloads",[],{},{"nodeType":17769,"data":17784,"content":17785},{},[17786],{"nodeType":634,"data":17787,"content":17788},{},[17789,17794],{"nodeType":633,"value":17790,"marks":17791,"data":17793},"CoreWeave: ",[17792],{"type":17122},{},{"nodeType":633,"value":17795,"marks":17796,"data":17797},"the Essential AI cloud for hosting models - both self-managed and serverless - including Weights & Biases AI developer tools for experiment tracking, model evaluation, auto research, and collaborative ML development",[],{},{"nodeType":17769,"data":17799,"content":17800},{},[17801],{"nodeType":634,"data":17802,"content":17803},{},[17804,17809],{"nodeType":633,"value":17805,"marks":17806,"data":17808},"Cursor",[17807],{"type":17122},{},{"nodeType":633,"value":17810,"marks":17811,"data":17812},": AI coding agent that helps developers write, review, and ship software faster",[],{},{"nodeType":17769,"data":17814,"content":17815},{},[17816],{"nodeType":634,"data":17817,"content":17818},{},[17819,17823],{"nodeType":633,"value":212,"marks":17820,"data":17822},[17821],{"type":17122},{},{"nodeType":633,"value":17824,"marks":17825,"data":17826},": Accelerated computing, AI software tools and open models—such as Metropolis VSS Blueprint, Cosmos, Nemotron and NemoClaw, that enables developers to build and deploy advanced AI applications.",[],{},{"nodeType":17115,"data":17828,"content":17829},{},[17830],{"nodeType":633,"value":17831,"marks":17832,"data":17833},"\nLearn. Build. Demonstrate.",[],{},{"nodeType":634,"data":17835,"content":17836},{},[17837],{"nodeType":633,"value":17838,"marks":17839,"data":17840},"The Builders Challenge event follows a simple idea: the best way to understand modern AI systems is to build with them.",[],{},{"nodeType":634,"data":17842,"content":17843},{},[17844],{"nodeType":633,"value":17845,"marks":17846,"data":17847},"Participants will learn how modern AI applications are architected, how data flows through these systems, and how different components come together to create production-ready systems.",[],{},{"nodeType":634,"data":17849,"content":17850},{},[17851],{"nodeType":633,"value":17852,"marks":17853,"data":17854},"Whether you're already building AI applications or just beginning to explore modern AI infrastructure, the workshop is designed to be approachable while still offering meaningful technical depth. Experience with LLMs, embeddings, or event-driven systems is helpful, but not required. We'll provide datasets and sample content to help you get started, and if you have your own video clips or data you'd like to build with, you're welcome to bring those as well.",[],{},{"nodeType":634,"data":17856,"content":17857},{},[17858],{"nodeType":633,"value":17859,"marks":17860,"data":17861},"Developers will work in teams to create AI applications using the tools and technologies introduced during the workshop. Projects may include agentic applications, RAG workflows, data-intensive applications, multimodal experiences, or other AI-powered solutions.",[],{},{"nodeType":634,"data":17863,"content":17864},{},[17865],{"nodeType":633,"value":17866,"marks":17867,"data":17868},"Throughout the day, experts from VAST, CoreWeave, Cursor, and NVIDIA will be available to answer questions, share best practices, and help teams overcome technical challenges.",[],{},{"nodeType":634,"data":17870,"content":17871},{},[17872],{"nodeType":633,"value":17873,"marks":17874,"data":17875},"At the end of each event, participants will demonstrate their applications to a panel of judges and the broader builder community.",[],{},{"nodeType":17115,"data":17877,"content":17878},{},[17879],{"nodeType":633,"value":17880,"marks":17881,"data":17882},"\nA Community for AI Builders",[],{},{"nodeType":634,"data":17884,"content":17885},{},[17886],{"nodeType":633,"value":17887,"marks":17888,"data":17889},"The questions developers face today are increasingly complex:",[],{},{"nodeType":17765,"data":17891,"content":17892},{},[17893,17903,17913],{"nodeType":17769,"data":17894,"content":17895},{},[17896],{"nodeType":634,"data":17897,"content":17898},{},[17899],{"nodeType":633,"value":17900,"marks":17901,"data":17902},"How should AI applications access enterprise data?",[],{},{"nodeType":17769,"data":17904,"content":17905},{},[17906],{"nodeType":634,"data":17907,"content":17908},{},[17909],{"nodeType":633,"value":17910,"marks":17911,"data":17912},"How should organizations manage the growing number of AI workloads?",[],{},{"nodeType":17769,"data":17914,"content":17915},{},[17916],{"nodeType":634,"data":17917,"content":17918},{},[17919],{"nodeType":633,"value":17920,"marks":17921,"data":17922},"How do teams move from experimentation to production at scale?",[],{},{"nodeType":634,"data":17924,"content":17925},{},[17926],{"nodeType":633,"value":17927,"marks":17928,"data":17929},"The VAST Builders Challenge is designed to create that environment, a place where builders can exchange ideas, experiment with new approaches, and see what becomes possible on modern AI infrastructure.",[],{},{"nodeType":17115,"data":17931,"content":17932},{},[17933],{"nodeType":633,"value":17934,"marks":17935,"data":17936},"\nBuild. 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Overhead",{"url":18861,"details":18862,"fileName":18865,"contentType":583},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FHRBK04G2XVFuG89Y0pAvS\u002F6be9eef1214d1d83ea6d78389a3a01d1\u002FCR-12495_BFI_SEO-1_Total-Data-Overhead_VAST-Data.webp",{"size":18863,"image":18864},89946,{"width":580,"height":581},"CR-12495_BFI_SEO-1_Total-Data-Overhead_VAST-Data.webp",{"data":18867,"content":18868,"nodeType":635},{},[18869,18877,18884,18891,18898,18905,18912,18919,18927,18934,18941,18948,18956,18963,18970,18993,19000,19008,19017,19024,19031,19054,19061,19080,19087,19095,19102,19145,19152,19159,19166,19174,19181,19214,19233,19240,19248,19255,19298,19305,19312,19320,19329,19336,19343,19350,19373,19380,19388,19395,19402,19409,19452,19459,19466,19473,19480,19488,19495,19502,19509,19552,19559,19579,19587,19594,19601,19621,19629,19636,19689,19696,19703,19710,19717,19724,19731,19738,19745,19752],{"data":18870,"content":18871,"nodeType":17115},{},[18872],{"data":18873,"marks":18874,"value":18876,"nodeType":633},{},[18875],{"type":17122},"The Hidden Problem in Enterprise Storage",{"data":18878,"content":18879,"nodeType":634},{},[18880],{"data":18881,"marks":18882,"value":18883,"nodeType":633},{},[],"Most enterprises assume a storage shortage begins when they run out of capacity.",{"data":18885,"content":18886,"nodeType":634},{},[18887],{"data":18888,"marks":18889,"value":18890,"nodeType":633},{},[],"Long before procurement becomes a constraint, a significant portion of the flash you've already purchased has been consumed. In legacy environments, that portion can run to 30% or more of raw capacity — consumed not by applications, analytics, or AI workloads, but by the storage system itself.",{"data":18892,"content":18893,"nodeType":634},{},[18894],{"data":18895,"marks":18896,"value":18897,"nodeType":633},{},[],"That lost capacity rarely shows up in purchasing conversations. But it shows up later in budget pressure, utilization gaps, and repeated storage expansion.",{"data":18899,"content":18900,"nodeType":634},{},[18901],{"data":18902,"marks":18903,"value":18904,"nodeType":633},{},[],"Total data overhead is the metric storage planning should run on, but rarely does. It measures the percentage of raw flash consumed by protection, metadata, reserved buffers, and system overhead before a single workload uses it.",{"data":18906,"content":18907,"nodeType":634},{},[18908],{"data":18909,"marks":18910,"value":18911,"nodeType":633},{},[],"It explains why enterprises hit flash constraints even after adding more SSDs.",{"data":18913,"content":18914,"nodeType":634},{},[18915],{"data":18916,"marks":18917,"value":18918,"nodeType":633},{},[],"Understanding total data overhead is the first step toward improving efficiency, reducing waste, and making better infrastructure decisions.\n",{"data":18920,"content":18921,"nodeType":17115},{},[18922],{"data":18923,"marks":18924,"value":18926,"nodeType":633},{},[18925],{"type":17122},"What Is Total Data Overhead?",{"data":18928,"content":18929,"nodeType":634},{},[18930],{"data":18931,"marks":18932,"value":18933,"nodeType":633},{},[],"Total data overhead is the percentage of raw storage capacity consumed by system functions (before any application data is stored): data protection, metadata, buffering, and architectural inefficiencies.",{"data":18935,"content":18936,"nodeType":634},{},[18937],{"data":18938,"marks":18939,"value":18940,"nodeType":633},{},[],"Raw capacity is not usable capacity.",{"data":18942,"content":18943,"nodeType":634},{},[18944],{"data":18945,"marks":18946,"value":18947,"nodeType":633},{},[],"Every storage system consumes part of what it stores to keep itself operational. The question is not whether overhead exists. It is how much.\n",{"data":18949,"content":18950,"nodeType":17115},{},[18951],{"data":18952,"marks":18953,"value":18955,"nodeType":633},{},[18954],{"type":17122},"Why Total Data Overhead Matters",{"data":18957,"content":18958,"nodeType":634},{},[18959],{"data":18960,"marks":18961,"value":18962,"nodeType":633},{},[],"Most storage decisions are made using the wrong number: procurement optimizes for purchased capacity, not delivered efficiency. That’s where inefficiency begins. ",{"data":18964,"content":18965,"nodeType":634},{},[18966],{"data":18967,"marks":18968,"value":18969,"nodeType":633},{},[],"The same flash can yield very different usable capacity depending on the architecture:",{"data":18971,"content":18972,"nodeType":17765},{},[18973,18983],{"data":18974,"content":18975,"nodeType":17769},{},[18976],{"data":18977,"content":18978,"nodeType":634},{},[18979],{"data":18980,"marks":18981,"value":18982,"nodeType":633},{},[],"One may deliver 85–90% usable storage",{"data":18984,"content":18985,"nodeType":17769},{},[18986],{"data":18987,"content":18988,"nodeType":634},{},[18989],{"data":18990,"marks":18991,"value":18992,"nodeType":633},{},[],"Another may lose 20–66% to data protection alone, before reduction",{"data":18994,"content":18995,"nodeType":634},{},[18996],{"data":18997,"marks":18998,"value":18999,"nodeType":633},{},[],"The difference is rarely the hardware, it's the architecture. Without measuring total data overhead, enterprises can’t accurately forecast storage growth, evaluate flash capacity utilization, or see where capacity is actually being consumed.\n",{"data":19001,"content":19002,"nodeType":17115},{},[19003],{"data":19004,"marks":19005,"value":19007,"nodeType":633},{},[19006],{"type":17122},"The Four Layers of Storage Overhead",{"data":19009,"content":19010,"nodeType":19016},{},[19011],{"data":19012,"marks":19013,"value":19015,"nodeType":633},{},[19014],{"type":17122},"1. Data Protection Overhead (Replication vs Erasure Coding)","heading-5",{"data":19018,"content":19019,"nodeType":634},{},[19020],{"data":19021,"marks":19022,"value":19023,"nodeType":633},{},[],"The largest source of storage overhead is data protection.",{"data":19025,"content":19026,"nodeType":634},{},[19027],{"data":19028,"marks":19029,"value":19030,"nodeType":633},{},[],"Traditional storage systems rely on replication or Reed-Solomon erasure coding for resilience. The cost varies dramatically:",{"data":19032,"content":19033,"nodeType":17765},{},[19034,19044],{"data":19035,"content":19036,"nodeType":17769},{},[19037],{"data":19038,"content":19039,"nodeType":634},{},[19040],{"data":19041,"marks":19042,"value":19043,"nodeType":633},{},[],"Replication: 50–66% of raw capacity lost (3x replication triples storage consumption)",{"data":19045,"content":19046,"nodeType":17769},{},[19047],{"data":19048,"content":19049,"nodeType":634},{},[19050],{"data":19051,"marks":19052,"value":19053,"nodeType":633},{},[],"Reed-Solomon erasure coding: 15–25% of capacity lost to parity",{"data":19055,"content":19056,"nodeType":634},{},[19057],{"data":19058,"marks":19059,"value":19060,"nodeType":633},{},[],"This creates immediate capacity loss before workloads are even considered.",{"data":19062,"content":19063,"nodeType":634},{},[19064,19068,19076],{"data":19065,"marks":19066,"value":19067,"nodeType":633},{},[],"Modern erasure coding distributes protection more efficiently than replication. The most sophisticated implementations push the savings further:",{"data":19069,"content":19071,"nodeType":1343},{"uri":19070},"..\u002F..\u002Fblog\u002Fbringing-rack-level-resilience-to-vast-part-one",[19072],{"data":19073,"marks":19074,"value":19075,"nodeType":633},{},[]," VAST's locally decodable erasure codes",{"data":19077,"marks":19078,"value":19079,"nodeType":633},{},[]," run on a 146+4 scheme that brings protection overhead below 3%, while delivering the same eleven nines of durability you'd get from a cloud object store.",{"data":19081,"content":19082,"nodeType":634},{},[19083],{"data":19084,"marks":19085,"value":19086,"nodeType":633},{},[],"Protection models are the single biggest lever in any storage overhead calculation.\n",{"data":19088,"content":19089,"nodeType":19016},{},[19090],{"data":19091,"marks":19092,"value":19094,"nodeType":633},{},[19093],{"type":17122},"2. Metadata and System Overhead",{"data":19096,"content":19097,"nodeType":634},{},[19098],{"data":19099,"marks":19100,"value":19101,"nodeType":633},{},[],"Storage systems generate metadata constantly. This includes:",{"data":19103,"content":19104,"nodeType":17765},{},[19105,19115,19125,19135],{"data":19106,"content":19107,"nodeType":17769},{},[19108],{"data":19109,"content":19110,"nodeType":634},{},[19111],{"data":19112,"marks":19113,"value":19114,"nodeType":633},{},[],"File indexes",{"data":19116,"content":19117,"nodeType":17769},{},[19118],{"data":19119,"content":19120,"nodeType":634},{},[19121],{"data":19122,"marks":19123,"value":19124,"nodeType":633},{},[],"System maps",{"data":19126,"content":19127,"nodeType":17769},{},[19128],{"data":19129,"content":19130,"nodeType":634},{},[19131],{"data":19132,"marks":19133,"value":19134,"nodeType":633},{},[],"Control structures",{"data":19136,"content":19137,"nodeType":17769},{},[19138],{"data":19139,"content":19140,"nodeType":634},{},[19141],{"data":19142,"marks":19143,"value":19144,"nodeType":633},{},[],"Internal tracking information",{"data":19146,"content":19147,"nodeType":634},{},[19148],{"data":19149,"marks":19150,"value":19151,"nodeType":633},{},[],"These functions are essential, but they consume flash.",{"data":19153,"content":19154,"nodeType":634},{},[19155],{"data":19156,"marks":19157,"value":19158,"nodeType":633},{},[],"As environments scale, metadata grows with them. In fragmented architectures with separate metadata systems per silo, the overhead compounds across stacks. Architectures that share a single metadata layer across the platform — VAST keeps it inside the 9% system reserve — avoid that compounding.",{"data":19160,"content":19161,"nodeType":634},{},[19162],{"data":19163,"marks":19164,"value":19165,"nodeType":633},{},[],"This is one of the least visible forms of enterprise storage inefficiency because it rarely appears as an explicit procurement cost.\n",{"data":19167,"content":19168,"nodeType":19016},{},[19169],{"data":19170,"marks":19171,"value":19173,"nodeType":633},{},[19172],{"type":17122},"3. Reserved Capacity and Buffers",{"data":19175,"content":19176,"nodeType":634},{},[19177],{"data":19178,"marks":19179,"value":19180,"nodeType":633},{},[],"Not all unused flash is actually available. Storage systems reserve capacity for:",{"data":19182,"content":19183,"nodeType":17765},{},[19184,19194,19204],{"data":19185,"content":19186,"nodeType":17769},{},[19187],{"data":19188,"content":19189,"nodeType":634},{},[19190],{"data":19191,"marks":19192,"value":19193,"nodeType":633},{},[],"Write stability",{"data":19195,"content":19196,"nodeType":17769},{},[19197],{"data":19198,"content":19199,"nodeType":634},{},[19200],{"data":19201,"marks":19202,"value":19203,"nodeType":633},{},[],"Rebuild operations",{"data":19205,"content":19206,"nodeType":17769},{},[19207],{"data":19208,"content":19209,"nodeType":634},{},[19210],{"data":19211,"marks":19212,"value":19213,"nodeType":633},{},[],"Performance consistency",{"data":19215,"content":19216,"nodeType":634},{},[19217,19221,19229],{"data":19218,"marks":19219,"value":19220,"nodeType":633},{},[],"This reserved capacity appears as available raw storage during procurement planning, when in reality it's already spoken for. On overprovisioned TLC, this category alone typically costs 7–28% of total capacity (",{"data":19222,"content":19224,"nodeType":1343},{"uri":19223},"https:\u002F\u002Fwww.usenix.org\u002Fconference\u002Ffast22\u002Fpresentation\u002Fmaneas",[19225],{"data":19226,"marks":19227,"value":19228,"nodeType":633},{},[],"Maneas et al., FAST '22",{"data":19230,"marks":19231,"value":19232,"nodeType":633},{},[],").",{"data":19234,"content":19235,"nodeType":634},{},[19236],{"data":19237,"marks":19238,"value":19239,"nodeType":633},{},[],"Without accounting for reserved capacity, storage utilization rates can look healthier than they actually are.",{"data":19241,"content":19242,"nodeType":19016},{},[19243],{"data":19244,"marks":19245,"value":19247,"nodeType":633},{},[19246],{"type":17122},"\n4. Fragmentation Across Silos",{"data":19249,"content":19250,"nodeType":634},{},[19251],{"data":19252,"marks":19253,"value":19254,"nodeType":633},{},[],"Many enterprises still operate separate file, block, and object storage systems. Each carries its own:",{"data":19256,"content":19257,"nodeType":17765},{},[19258,19268,19278,19288],{"data":19259,"content":19260,"nodeType":17769},{},[19261],{"data":19262,"content":19263,"nodeType":634},{},[19264],{"data":19265,"marks":19266,"value":19267,"nodeType":633},{},[],"Data copies",{"data":19269,"content":19270,"nodeType":17769},{},[19271],{"data":19272,"content":19273,"nodeType":634},{},[19274],{"data":19275,"marks":19276,"value":19277,"nodeType":633},{},[],"Metadata layers",{"data":19279,"content":19280,"nodeType":17769},{},[19281],{"data":19282,"content":19283,"nodeType":634},{},[19284],{"data":19285,"marks":19286,"value":19287,"nodeType":633},{},[],"Protection models",{"data":19289,"content":19290,"nodeType":17769},{},[19291],{"data":19292,"content":19293,"nodeType":634},{},[19294],{"data":19295,"marks":19296,"value":19297,"nodeType":633},{},[],"Reserved capacity pools",{"data":19299,"content":19300,"nodeType":634},{},[19301],{"data":19302,"marks":19303,"value":19304,"nodeType":633},{},[],"This creates duplicated storage consumption across environments.",{"data":19306,"content":19307,"nodeType":634},{},[19308],{"data":19309,"marks":19310,"value":19311,"nodeType":633},{},[],"Fragmentation is one of the least obvious but most expensive sources of flash waste because it compounds every other layer of overhead. It also reduces data reduction efficiency by isolating datasets that could otherwise be optimized together.",{"data":19313,"content":19314,"nodeType":17115},{},[19315],{"data":19316,"marks":19317,"value":19319,"nodeType":633},{},[19318],{"type":17122},"\nThe Simple Formula for Total Data Overhead",{"data":19321,"content":19322,"nodeType":634},{},[19323],{"data":19324,"marks":19325,"value":19328,"nodeType":633},{},[19326],{"type":19327},"code","Total Data Overhead = (Protection + Metadata + Buffers + Fragmentation Waste) ÷ Raw Capacity",{"data":19330,"content":19331,"nodeType":634},{},[19332],{"data":19333,"marks":19334,"value":19335,"nodeType":633},{},[],"This formula provides a simple way to evaluate how much storage is being consumed before workloads ever use it. It reveals the difference between what an organization bought and what it can actually use.",{"data":19337,"content":19338,"nodeType":634},{},[19339],{"data":19340,"marks":19341,"value":19342,"nodeType":633},{},[],"This is the gap most storage planning models fail to measure.",{"data":19344,"content":19345,"nodeType":634},{},[19346],{"data":19347,"marks":19348,"value":19349,"nodeType":633},{},[],"In legacy environments:",{"data":19351,"content":19352,"nodeType":17765},{},[19353,19363],{"data":19354,"content":19355,"nodeType":17769},{},[19356],{"data":19357,"content":19358,"nodeType":634},{},[19359],{"data":19360,"marks":19361,"value":19362,"nodeType":633},{},[],"Total overhead can reach 50–66% with replication, or 21–28% on shared-nothing flash systems with erasure coding plus system reserve",{"data":19364,"content":19365,"nodeType":17769},{},[19366],{"data":19367,"content":19368,"nodeType":634},{},[19369],{"data":19370,"marks":19371,"value":19372,"nodeType":633},{},[],"Usable storage becomes the minority of purchased capacity",{"data":19374,"content":19375,"nodeType":634},{},[19376],{"data":19377,"marks":19378,"value":19379,"nodeType":633},{},[],"Architectures designed without these legacy tradeoffs collapse total overhead by an order of magnitude, increasing usable storage without increasing purchased flash. That's why total data overhead is one of the most important efficiency metrics an enterprise can measure.\n",{"data":19381,"content":19382,"nodeType":17115},{},[19383],{"data":19384,"marks":19385,"value":19387,"nodeType":633},{},[19386],{"type":17122},"Why Enterprises Don't See the Waste",{"data":19389,"content":19390,"nodeType":634},{},[19391],{"data":19392,"marks":19393,"value":19394,"nodeType":633},{},[],"Most enterprises don't measure storage inefficiency directly. Traditional storage reporting focuses on allocated capacity, not consumed overhead.",{"data":19396,"content":19397,"nodeType":634},{},[19398],{"data":19399,"marks":19400,"value":19401,"nodeType":633},{},[],"That leaves major sources of waste hidden.",{"data":19403,"content":19404,"nodeType":634},{},[19405],{"data":19406,"marks":19407,"value":19408,"nodeType":633},{},[],"What often gets missed:",{"data":19410,"content":19411,"nodeType":17765},{},[19412,19422,19432,19442],{"data":19413,"content":19414,"nodeType":17769},{},[19415],{"data":19416,"content":19417,"nodeType":634},{},[19418],{"data":19419,"marks":19420,"value":19421,"nodeType":633},{},[],"Replication overhead across systems",{"data":19423,"content":19424,"nodeType":17769},{},[19425],{"data":19426,"content":19427,"nodeType":634},{},[19428],{"data":19429,"marks":19430,"value":19431,"nodeType":633},{},[],"Metadata growth over time",{"data":19433,"content":19434,"nodeType":17769},{},[19435],{"data":19436,"content":19437,"nodeType":634},{},[19438],{"data":19439,"marks":19440,"value":19441,"nodeType":633},{},[],"Reserved capacity unavailable to workloads",{"data":19443,"content":19444,"nodeType":17769},{},[19445],{"data":19446,"content":19447,"nodeType":634},{},[19448],{"data":19449,"marks":19450,"value":19451,"nodeType":633},{},[],"Duplicate data spread across silos",{"data":19453,"content":19454,"nodeType":634},{},[19455],{"data":19456,"marks":19457,"value":19458,"nodeType":633},{},[],"Most storage vendors don't expose total system overhead clearly.",{"data":19460,"content":19461,"nodeType":634},{},[19462],{"data":19463,"marks":19464,"value":19465,"nodeType":633},{},[],"Procurement gets triggered by capacity pressure, so the default response is predictable:",{"data":19467,"content":19468,"nodeType":634},{},[19469],{"data":19470,"marks":19471,"value":19472,"nodeType":633},{},[],"Buy more storage.",{"data":19474,"content":19475,"nodeType":634},{},[19476],{"data":19477,"marks":19478,"value":19479,"nodeType":633},{},[],"In many environments, that increases capacity consumption and capacity waste at the same time.\n",{"data":19481,"content":19482,"nodeType":17115},{},[19483],{"data":19484,"marks":19485,"value":19487,"nodeType":633},{},[19486],{"type":17122},"What Good Looks Like: Reducing Overhead at the Platform Level",{"data":19489,"content":19490,"nodeType":634},{},[19491],{"data":19492,"marks":19493,"value":19494,"nodeType":633},{},[],"In practice, the architectures that hold up under flash scarcity reduce overhead by design at the platform layer.",{"data":19496,"content":19497,"nodeType":634},{},[19498],{"data":19499,"marks":19500,"value":19501,"nodeType":633},{},[],"Instead of optimizing storage system by system, organizations can consolidate storage into a unified platform where protection, metadata, and capacity management operate once across the environment instead of being duplicated across silos.",{"data":19503,"content":19504,"nodeType":634},{},[19505],{"data":19506,"marks":19507,"value":19508,"nodeType":633},{},[],"This reduces:",{"data":19510,"content":19511,"nodeType":17765},{},[19512,19522,19532,19542],{"data":19513,"content":19514,"nodeType":17769},{},[19515],{"data":19516,"content":19517,"nodeType":634},{},[19518],{"data":19519,"marks":19520,"value":19521,"nodeType":633},{},[],"Duplicate data copies",{"data":19523,"content":19524,"nodeType":17769},{},[19525],{"data":19526,"content":19527,"nodeType":634},{},[19528],{"data":19529,"marks":19530,"value":19531,"nodeType":633},{},[],"Redundant metadata systems",{"data":19533,"content":19534,"nodeType":17769},{},[19535],{"data":19536,"content":19537,"nodeType":634},{},[19538],{"data":19539,"marks":19540,"value":19541,"nodeType":633},{},[],"Multiple protection layers",{"data":19543,"content":19544,"nodeType":17769},{},[19545],{"data":19546,"content":19547,"nodeType":634},{},[19548],{"data":19549,"marks":19550,"value":19551,"nodeType":633},{},[],"Capacity stranded across silos",{"data":19553,"content":19554,"nodeType":634},{},[19555],{"data":19556,"marks":19557,"value":19558,"nodeType":633},{},[],"Across the VAST installed fleet, total overhead averages approximately 12%: 3% from locally decodable erasure coding and 9% reserved for system data management. 93% of data operates below the 21% overhead typical of competitive shared-nothing flash systems.",{"data":19560,"content":19561,"nodeType":634},{},[19562,19566,19575],{"data":19563,"marks":19564,"value":19565,"nodeType":633},{},[],"When combined with",{"data":19567,"content":19569,"nodeType":1343},{"uri":19568},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fdatastore",[19570],{"data":19571,"marks":19572,"value":19574,"nodeType":633},{},[19573],{"type":17704}," VAST's similarity-based data reduction",{"data":19576,"marks":19577,"value":19578,"nodeType":633},{},[]," capabilities, the capacity advantage grows further. Across the fleet, the median data reduction ratio is 1.87:1, with some datasets exceeding 8:1.\n",{"data":19580,"content":19581,"nodeType":17115},{},[19582],{"data":19583,"marks":19584,"value":19586,"nodeType":633},{},[19585],{"type":17122},"How Total Data Overhead Connects to the SSD Shortage",{"data":19588,"content":19589,"nodeType":634},{},[19590],{"data":19591,"marks":19592,"value":19593,"nodeType":633},{},[],"The SSD shortage is an external constraint. Total data overhead is an internal one.",{"data":19595,"content":19596,"nodeType":634},{},[19597],{"data":19598,"marks":19599,"value":19600,"nodeType":633},{},[],"The shortage has made flash more expensive and harder to procure — and AI is simultaneously increasing the volume of data organizations need to retain and process, making storage efficiency more important than ever.",{"data":19602,"content":19603,"nodeType":634},{},[19604,19608,19617],{"data":19605,"marks":19606,"value":19607,"nodeType":633},{},[],"That's why the ",{"data":19609,"content":19611,"nodeType":1343},{"uri":19610},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fovercoming-the-ssd-shortage-with-vast",[19612],{"data":19613,"marks":19614,"value":19616,"nodeType":633},{},[19615],{"type":17704},"enterprise SSD shortage",{"data":19618,"marks":19619,"value":19620,"nodeType":633},{},[]," and total data overhead have to be understood together. One explains the supply pressure; the other explains why that pressure feels worse than it should. The fastest path to more usable storage is reducing waste, not procurement.",{"data":19622,"content":19623,"nodeType":17115},{},[19624],{"data":19625,"marks":19626,"value":19628,"nodeType":633},{},[19627],{"type":17122},"\nWhat You Should Measure Before Buying More Storage",{"data":19630,"content":19631,"nodeType":634},{},[19632],{"data":19633,"marks":19634,"value":19635,"nodeType":633},{},[],"Before purchasing additional storage, measure:",{"data":19637,"content":19638,"nodeType":17765},{},[19639,19649,19659,19669,19679],{"data":19640,"content":19641,"nodeType":17769},{},[19642],{"data":19643,"content":19644,"nodeType":634},{},[19645],{"data":19646,"marks":19647,"value":19648,"nodeType":633},{},[],"Effective usable capacity versus raw capacity",{"data":19650,"content":19651,"nodeType":17769},{},[19652],{"data":19653,"content":19654,"nodeType":634},{},[19655],{"data":19656,"marks":19657,"value":19658,"nodeType":633},{},[],"Replication factors across systems",{"data":19660,"content":19661,"nodeType":17769},{},[19662],{"data":19663,"content":19664,"nodeType":634},{},[19665],{"data":19666,"marks":19667,"value":19668,"nodeType":633},{},[],"Duplicate datasets across silos",{"data":19670,"content":19671,"nodeType":17769},{},[19672],{"data":19673,"content":19674,"nodeType":634},{},[19675],{"data":19676,"marks":19677,"value":19678,"nodeType":633},{},[],"Reserved versus actively used flash",{"data":19680,"content":19681,"nodeType":17769},{},[19682],{"data":19683,"content":19684,"nodeType":634},{},[19685],{"data":19686,"marks":19687,"value":19688,"nodeType":633},{},[],"Storage utilization consistency across environments",{"data":19690,"content":19691,"nodeType":634},{},[19692],{"data":19693,"marks":19694,"value":19695,"nodeType":633},{},[],"These measurements create visibility into where flash is being consumed and where usable capacity can be recovered. They also provide a more accurate storage overhead calculation for procurement planning.",{"data":19697,"content":19698,"nodeType":634},{},[19699],{"data":19700,"marks":19701,"value":19702,"nodeType":633},{},[],"Without this visibility, infrastructure decisions are incomplete.",{"data":19704,"content":19705,"nodeType":634},{},[19706],{"data":19707,"marks":19708,"value":19709,"nodeType":633},{},[],"The traditional storage model scaled by buying more.",{"data":19711,"content":19712,"nodeType":634},{},[19713],{"data":19714,"marks":19715,"value":19716,"nodeType":633},{},[],"The model that fits the post-2025 flash market scales by getting more out of what's already deployed.",{"data":19718,"content":19719,"nodeType":634},{},[19720],{"data":19721,"marks":19722,"value":19723,"nodeType":633},{},[],"That changes how flash gets evaluated as an infrastructure investment. The question isn't how much capacity you can buy. It's how much of it actually shows up as usable storage.",{"data":19725,"content":19726,"nodeType":634},{},[19727],{"data":19728,"marks":19729,"value":19730,"nodeType":633},{},[],"This is where concepts like capacity amplification and flash efficiency become important operational KPIs.",{"data":19732,"content":19733,"nodeType":634},{},[19734],{"data":19735,"marks":19736,"value":19737,"nodeType":633},{},[],"Until organizations measure total data overhead, they will continue to misinterpret storage shortages as procurement problems rather than architectural inefficiencies.",{"data":19739,"content":19740,"nodeType":634},{},[19741],{"data":19742,"marks":19743,"value":19744,"nodeType":633},{},[],"The SSD shortage is real. Architectural waste is the larger constraint, and the one you can actually do something about.",{"data":19746,"content":19747,"nodeType":634},{},[19748],{"data":19749,"marks":19750,"value":19751,"nodeType":633},{},[],"Understanding how enterprises are overcoming the SSD shortage creates a clearer path from capacity pressure to storage efficiency.",{"data":19753,"content":19754,"nodeType":634},{},[19755],{"data":19756,"marks":19757,"value":19758,"nodeType":633},{},[],"The real constraint is usable capacity, and architecture determines how much of it you keep. None of this means the supply problem is solved — that's outside any one company's control. What architecture can do is shift the math: more usable storage out of every drive that can be procured, while the supply side works itself 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the Flash Crunch With Efficiency, Not Supply-Web.jpg",{"data":19943,"content":19944,"nodeType":635},{},[19945,19952,19971,19978,19985,19992,19999,20006,20013,20020,20027,20034,20077,20084,20091,20098,20105,20112,20119,20126,20192],{"data":19946,"content":19947,"nodeType":634},{},[19948],{"data":19949,"marks":19950,"value":19951,"nodeType":633},{},[],"For years, the storage industry has been conditioned to treat shortages as temporary. Cyclical downturns, followed by density gains, followed by relief.",{"data":19953,"content":19954,"nodeType":634},{},[19955,19959,19967],{"data":19956,"marks":19957,"value":19958,"nodeType":633},{},[],"But as we unpack in a",{"data":19960,"content":19962,"nodeType":1343},{"uri":19961},"https:\u002F\u002Fshared-everything.simplecast.com\u002Fepisodes\u002Fbeating-the-flash-crunch-with-efficiency-not-supply",[19963],{"data":19964,"marks":19965,"value":19966,"nodeType":633},{},[]," recent episode of the Shared Everything",{"data":19968,"marks":19969,"value":19970,"nodeType":633},{},[]," podcast from VAST, what’s unfolding now looks different.",{"data":19972,"content":19973,"nodeType":634},{},[19974],{"data":19975,"marks":19976,"value":19977,"nodeType":633},{},[],"The current flash supply crunch is not simply a matter of demand outrunning manufacturing for a quarter or two. Instead we’re looking at the collision of AI-scale consumption with the physical limits of NAND production, the long timelines of fab expansion, and a market that no longer has meaningful slack.",{"data":19979,"content":19980,"nodeType":634},{},[19981],{"data":19982,"marks":19983,"value":19984,"nodeType":633},{},[],"That reality is forcing a reframing of where leverage actually exists, as VAST Co-Founder Jeff Denworth and Solidigm’s Scott Shadley explain.",{"data":19986,"content":19987,"nodeType":634},{},[19988],{"data":19989,"marks":19990,"value":19991,"nodeType":633},{},[],"AI infrastructure has changed the profile of storage demand in three important ways. First, the sheer volume of data being ingested has exploded. Training pipelines, inference logs, embeddings, checkpoints, and intermediate artifacts all want to live close to compute and stay accessible.",{"data":19993,"content":19994,"nodeType":634},{},[19995],{"data":19996,"marks":19997,"value":19998,"nodeType":633},{},[],"Second, access patterns have shifted. Random access is no longer a performance optimization; it is a requirement.",{"data":20000,"content":20001,"nodeType":634},{},[20002],{"data":20003,"marks":20004,"value":20005,"nodeType":633},{},[],"And third, tolerance for latency and inefficiency has collapsed. When GPUs cost tens of thousands of dollars per unit and are deployed by the hundreds of thousands, wasted I\u002FO capacity becomes wasted compute.",{"data":20007,"content":20008,"nodeType":634},{},[20009],{"data":20010,"marks":20011,"value":20012,"nodeType":633},{},[],"At the same time, NAND manufacturing has lost the easy gains it once enjoyed. Layer counts continue to rise, but fab throughput does not scale linearly with density. Process times increase, toolchains diverge, and new fabs take years to bring online. Even the hard drive industry, long assumed to be the safety valve during flash shortages, is facing its own mechanical and power constraints. The result is a market where buying more media is no longer a reliable strategy.",{"data":20014,"content":20015,"nodeType":634},{},[20016],{"data":20017,"marks":20018,"value":20019,"nodeType":633},{},[],"In conversations with customers navigating the current crunch, one pattern shows up repeatedly. Organizations are discovering that their real flash consumption has far less to do with the amount of data they generate than with how inefficiently that data is stored. Triplicated databases sitting on top of triplicated object stores. Erasure codes chosen for simplicity rather than efficiency. Performance workarounds that multiply capacity requirements by three, six, or nine times.",{"data":20021,"content":20022,"nodeType":634},{},[20023],{"data":20024,"marks":20025,"value":20026,"nodeType":633},{},[],"Those design decisions were tolerable when flash was abundant and prices were falling, but they are untenable when allocation is fixed and lead times stretch into quarters.",{"data":20028,"content":20029,"nodeType":634},{},[20030],{"data":20031,"marks":20032,"value":20033,"nodeType":633},{},[],"VAST approaches the problem from a different direction, as Denworth outlines in this episode.",{"data":20035,"content":20036,"nodeType":634},{},[20037,20041,20049,20053,20061,20065,20073],{"data":20038,"marks":20039,"value":20040,"nodeType":633},{},[],"Instead of assuming flash is cheap and infinite, the system is built around ",{"data":20042,"content":20044,"nodeType":1343},{"uri":20043},"..\u002F..\u002Fblog\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity",[20045],{"data":20046,"marks":20047,"value":20048,"nodeType":633},{},[],"extracting maximum usable capacity",{"data":20050,"marks":20051,"value":20052,"nodeType":633},{},[]," from every drive. That starts with ",{"data":20054,"content":20056,"nodeType":1343},{"uri":20055},"..\u002F..\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes",[20057],{"data":20058,"marks":20059,"value":20060,"nodeType":633},{},[],"erasure coding",{"data":20062,"marks":20063,"value":20064,"nodeType":633},{},[]," designed for flash from day one, delivering overhead on the order of a few percent rather than the double-digit penalties common in legacy systems. It continues with ",{"data":20066,"content":20068,"nodeType":1343},{"uri":20067},"..\u002F..\u002Fblog\u002Fbreaking-data-reduction-trade-offs-with-global-compression",[20069],{"data":20070,"marks":20071,"value":20072,"nodeType":633},{},[],"data reduction that operates globally",{"data":20074,"marks":20075,"value":20076,"nodeType":633},{},[]," across the dataset, not as a bolt-on feature or a best-effort optimization, but as a first-class architectural principle.",{"data":20078,"content":20079,"nodeType":634},{},[20080],{"data":20081,"marks":20082,"value":20083,"nodeType":633},{},[],"This goes well beyond theory. Across production environments, customers consistently see usable capacity expand by multiples relative to raw flash. In practical terms, this means organizations constrained by NAND allocation can meet ingest and retention goals without waiting for the supply chain to catch up. In extreme cases, environments that previously required multiple replicated copies for performance and protection collapse into a single, efficient footprint.",{"data":20085,"content":20086,"nodeType":634},{},[20087],{"data":20088,"marks":20089,"value":20090,"nodeType":633},{},[],"This also reframes the flash shortage from a procurement problem into a software problem.",{"data":20092,"content":20093,"nodeType":634},{},[20094],{"data":20095,"marks":20096,"value":20097,"nodeType":633},{},[],"What’s notable is how quickly this shift is happening. Efficiency used to be discussed in the context of power, cooling, and cost optimization. Those still matter, but they are no longer the primary drivers. Today, efficiency determines whether a deployment can happen at all. When hyperscalers are willing to place open-ended orders and enterprises are competing for the same constrained supply, the ability to make flash go two or three times further becomes a strategic advantage.",{"data":20099,"content":20100,"nodeType":634},{},[20101],{"data":20102,"marks":20103,"value":20104,"nodeType":633},{},[],"There’s also a second-order effect. As AI workloads increasingly demand instant access to data beyond GPU and CPU memory, flash becomes an extension of the compute fabric rather than a passive storage tier. Algorithms that assume random access and consistent latency unlock capabilities that simply are not possible on spinning media or heavily replicated architectures. This tight coupling between compute and data makes inefficiency even more expensive, because it directly impacts utilization upstream.",{"data":20106,"content":20107,"nodeType":634},{},[20108],{"data":20109,"marks":20110,"value":20111,"nodeType":633},{},[],"None of this eliminates the need for more flash. Fabs will expand, supply will eventually loosen, and the market will find a new equilibrium. But the lesson of this cycle is unlikely to fade. AI has exposed how fragile capacity planning becomes when it relies on abundance rather than design.",{"data":20113,"content":20114,"nodeType":634},{},[20115],{"data":20116,"marks":20117,"value":20118,"nodeType":633},{},[],"In the near term, the organizations that succeed will not be the ones that secured the largest allocations, but the ones that extracted the most value from what they already had. In that sense, the flash crunch is doing something the industry rarely gets forced to do: confront inefficiency head-on, and treat software architecture as a first-order supply chain lever.",{"data":20120,"content":20121,"nodeType":17115},{},[20122],{"data":20123,"marks":20124,"value":20125,"nodeType":633},{},[],"Efficiency is the new supply. 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They found that the combination can ",{"data":21362,"marks":21363,"value":21365,"nodeType":633},{},[21364],{"type":17122},"cut TCO",{"data":21367,"marks":21368,"value":21369,"nodeType":633},{},[]," for an exabyte of storage ",{"data":21371,"marks":21372,"value":21374,"nodeType":633},{},[21373],{"type":17122},"by nearly 60% ",{"data":21376,"marks":21377,"value":21378,"nodeType":633},{},[],"compared with storing the same data on hard disks, and ",{"data":21380,"marks":21381,"value":21383,"nodeType":633},{},[21382],{"type":17122},"compressed the hardware footprint from 52 racks to 5 racks",{"data":21385,"marks":21386,"value":1347,"nodeType":633},{},[],{"data":21388,"content":21389,"nodeType":634},{},[21390,21394,21403],{"data":21391,"marks":21392,"value":21393,"nodeType":633},{},[],"We supplemented the whitepaper with ",{"data":21395,"content":21397,"nodeType":1343},{"uri":21396},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity",[21398],{"data":21399,"marks":21400,"value":21402,"nodeType":633},{},[21401],{"type":17704},"a blog post that addresses the findings",{"data":21404,"marks":21405,"value":21406,"nodeType":633},{},[]," at a higher level, some highlights from which are here:",{"data":21408,"content":21409,"nodeType":17765},{},[21410,21432,21455],{"data":21411,"content":21412,"nodeType":17769},{},[21413],{"data":21414,"content":21415,"nodeType":634},{},[21416,21420,21428],{"data":21417,"marks":21418,"value":21419,"nodeType":633},{},[],"Solidigm’s ",{"data":21421,"content":21422,"nodeType":1343},{"uri":20182},[21423],{"data":21424,"marks":21425,"value":21427,"nodeType":633},{},[21426],{"type":17704},"122TB drive, based on 192-layer, QLC NAND flash",{"data":21429,"marks":21430,"value":21431,"nodeType":633},{},[],", provides unprecedented storage density. This allows for a massive reduction in the physical footprint of storage systems.",{"data":21433,"content":21434,"nodeType":17769},{},[21435],{"data":21436,"content":21437,"nodeType":634},{},[21438,21442,21451],{"data":21439,"marks":21440,"value":21441,"nodeType":633},{},[],"VAST’s advanced ",{"data":21443,"content":21445,"nodeType":1343},{"uri":21444},"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper#a-breakthrough-approach-to-data-reduction",[21446],{"data":21447,"marks":21448,"value":21450,"nodeType":633},{},[21449],{"type":17704},"Similarity data reduction",{"data":21452,"marks":21453,"value":21454,"nodeType":633},{},[]," technique goes far beyond standard compression to find and eliminate redundancy at a granular level, meaning you need nearly 3x less raw storage capacity compared to HDD solutions. This level of granular data reduction can only be practical on flash because spinning media depends on sequential data for speed. When duplicate data is removed, many sequential reads look like random IO, which flash drives excel at, but HDDs struggle with.",{"data":21456,"content":21457,"nodeType":17769},{},[21458],{"data":21459,"content":21460,"nodeType":634},{},[21461,21464,21473],{"data":21462,"marks":21463,"value":470,"nodeType":633},{},[],{"data":21465,"content":21467,"nodeType":1343},{"uri":21466},"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes",[21468],{"data":21469,"marks":21470,"value":21472,"nodeType":633},{},[21471],{"type":17704},"Locally decodable codes",{"data":21474,"marks":21475,"value":21476,"nodeType":633},{},[]," provide an extremely efficient and resilient form of data protection, further enhancing cost-effectiveness by reducing wasted overhead.",{"data":21478,"content":21499,"nodeType":20767},{"target":21479},{"metadata":21480,"sys":21483,"fields":21490},{"tags":21481,"concepts":21482},[],[],{"space":21484,"id":21486,"type":566,"createdAt":21487,"updatedAt":21487,"environment":21488,"publishedVersion":723,"revision":789,"locale":512},{"sys":21485},{"type":495,"linkType":496,"id":497},"3E6tKrtcliZ4T9MdGRdogh","2025-09-15T14:07:10.188Z",{"sys":21489},{"id":504,"type":495,"linkType":505},{"title":21491,"description":21491,"file":21492},"Calculated Raw Capacity Required (EB)",{"url":21493,"details":21494,"fileName":21498,"contentType":583},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3E6tKrtcliZ4T9MdGRdogh\u002F8fe6bbe268068d2fc2bcfc16aa0f2ef3\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-2.webp",{"size":21495,"image":21496},26834,{"width":580,"height":21497},939,"new-storage-economics-solidigm-vast-deliver-all-ssd-capacity-2.webp",[],{"data":21501,"content":21502,"nodeType":634},{},[21503],{"data":21504,"marks":21505,"value":21506,"nodeType":633},{},[],"More broadly, though, the VAST DataStore is just built for efficiency. Global namespace? Check. Block, file, and object storage on a single platform? Check. Automatic and real-time deduplication, similarity reduction, and compression across all of it? Check.",{"data":21508,"content":21509,"nodeType":634},{},[21510,21513,21522,21526,21535],{"data":21511,"marks":21512,"value":470,"nodeType":633},{},[],{"data":21514,"content":21516,"nodeType":1343},{"uri":21515},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers",[21517],{"data":21518,"marks":21519,"value":21521,"nodeType":633},{},[21520],{"type":17704},"Our customers",{"data":21523,"marks":21524,"value":21525,"nodeType":633},{},[]," are happy to share their stories of revamping their storage architectures with VAST, and this quote from ",{"data":21527,"content":21529,"nodeType":1343},{"uri":21528},"https:\u002F\u002Fwww.vastdata.com\u002Fcustomers\u002Flola-vfx",[21530],{"data":21531,"marks":21532,"value":21534,"nodeType":633},{},[21533],{"type":17704},"Lola VFX",{"data":21536,"marks":21537,"value":21538,"nodeType":633},{},[]," founder Edson Williams sums up a lot of what we hear:",{"data":21540,"content":21541,"nodeType":21549},{},[21542],{"data":21543,"content":21544,"nodeType":634},{},[21545],{"data":21546,"marks":21547,"value":21548,"nodeType":633},{},[],"After we evaluated all our options, we chose VAST because of the scalability, the performance, and the value. We immediately started to see the impact of VAST. With their global namespace, we’ve been able to essentially make all our data accessible anywhere it is needed. And then the 2:1 reduction rate has allowed us to store more data than we had first thought.","blockquote",{"data":21551,"content":21552,"nodeType":21549},{},[21553],{"data":21554,"content":21555,"nodeType":634},{},[21556],{"data":21557,"marks":21558,"value":21559,"nodeType":633},{},[],"So, the return on investment was faster than we had even guessed. Initially, we were anticipating 24 to 36 months, and it’s going to be closer to 15 to 18 months before the VAST system pays for itself in increased productivity.",{"data":21561,"content":21562,"nodeType":17115},{},[21563,21567,21572],{"data":21564,"marks":21565,"value":21566,"nodeType":633},{},[],"Step 2: Invest ",{"data":21568,"marks":21569,"value":21571,"nodeType":633},{},[21570],{"type":21052},"more",{"data":21573,"marks":21574,"value":21575,"nodeType":633},{},[]," in other areas",{"data":21577,"content":21578,"nodeType":634},{},[21579],{"data":21580,"marks":21581,"value":21582,"nodeType":633},{},[],"Beyond the obvious benefits of what VAST offers at the storage tier, there are the indirect benefits from freeing up budget, space, and power consumption. This is particularly important today, as everyone is looking to step up their AI strategies and they need often-expensive, power-hungry GPUs to do it.",{"data":21584,"content":21585,"nodeType":634},{},[21586,21590,21598],{"data":21587,"marks":21588,"value":21589,"nodeType":633},{},[],"If you’re running a data center, condensing your storage footprint delivers immediate results. Fewer racks means more room for GPUs. Fewer disks means more power for GPUs. Lower TCO means more budget for GPUs. This, combined with top-of-the-line performance, is a big reason why so many of today’s leading AI clouds utilize VAST — and why so many of their customers ",{"data":21591,"content":21593,"nodeType":1343},{"uri":21592},"https:\u002F\u002Fyoutu.be\u002FjGVNDItuHCU?list=TLGGa3ftA_a6rIIxODEyMjAyNQ",[21594],{"data":21595,"marks":21596,"value":21597,"nodeType":633},{},[],"ask for VAST by name",{"data":21599,"marks":21600,"value":1347,"nodeType":633},{},[],{"data":21602,"content":21603,"nodeType":634},{},[21604,21608,21616],{"data":21605,"marks":21606,"value":21607,"nodeType":633},{},[],"If you’re running AI workloads in the cloud, storing data on VAST means you can choose cloud providers based on who’s delivering the best compute platform for your needs at any given time. Or, in the worst case, who even has available compute capacity. As part of our recently announced ",{"data":21609,"content":21611,"nodeType":1343},{"uri":21610},"https:\u002F\u002Fwww.vastdata.com\u002Fpress-releases\u002Fvast-data-and-google-cloud-partner-to-enable-enterprise-ai-hybrid-cloud",[21612],{"data":21613,"marks":21614,"value":21615,"nodeType":633},{},[],"partnership with Google Cloud",{"data":21617,"marks":21618,"value":21619,"nodeType":633},{},[],", for example, we demonstrated a configuration linking two compute clusters — one TPU-based cluster in the United States and one GPU-based cluster in Japan — with a single VAST DataSpace instance. The result: seamless, near real-time access to the same data in both locations while running inference workloads.",{"data":21621,"content":21622,"nodeType":634},{},[21623,21627,21635,21639,21644,21648,21653],{"data":21624,"marks":21625,"value":21626,"nodeType":633},{},[],"VAST has also powered data access for some of the world’s largest AI training runs, and our own analysis showed that many ",{"data":21628,"content":21630,"nodeType":1343},{"uri":21629},"..\u002F..\u002Fblog\u002Foptimizing-checkpoint-bandwidth-for-llm-training",[21631],{"data":21632,"marks":21633,"value":21634,"nodeType":633},{},[],"could actually use far less storage bandwidth",{"data":21636,"marks":21637,"value":21638,"nodeType":633},{},[]," without risking checkpoint failure. 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New Storage Economics: Solidigm and VAST Deliver All-SSD Capacity at Half the Cost of HDD","new-storage-economics-solidigm-vast-deliver-all-ssd-capacity","2025-09-18",{"metadata":22531,"sys":22534,"fields":22543},{"tags":22532,"concepts":22533},[],[],{"space":22535,"id":22537,"type":499,"createdAt":22538,"updatedAt":22538,"environment":22539,"publishedVersion":572,"revision":789,"contentType":22541,"locale":512},{"sys":22536},{"type":495,"linkType":496,"id":497},"6YIf6pfUcPfL6HcofY8bD","2025-09-15T14:27:00.539Z",{"sys":22540},{"id":504,"type":495,"linkType":505},{"sys":22542},{"type":495,"linkType":510,"id":17056},{"entryTitle":22527,"image":22544},{"metadata":22545,"sys":22548,"fields":22555},{"tags":22546,"concepts":22547},[],[],{"space":22549,"id":22551,"type":566,"createdAt":22552,"updatedAt":22552,"environment":22553,"publishedVersion":723,"revision":789,"locale":512},{"sys":22550},{"type":495,"linkType":496,"id":497},"1nJgZvqCKZvqs3NqeNyEhi","2025-09-15T14:26:51.858Z",{"sys":22554},{"id":504,"type":495,"linkType":505},{"title":22527,"description":22527,"file":22556},{"url":22557,"details":22558,"fileName":22561,"contentType":583},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1nJgZvqCKZvqs3NqeNyEhi\u002F5115d442b711aec3bb49149687988423\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity.webp",{"size":22559,"image":22560},44864,{"width":580,"height":581},"new-storage-economics-solidigm-vast-deliver-all-ssd-capacity.webp","For years, enterprise IT has played a strategic, often frustrating, game of resource allocation. Flash storage devices, with their undeniable speed, were the prized assets - reserved for the \"hot tier,\" the critical applications, the workloads requiring the lowest latencies and the highest throughput. The rest of the \"warm\" or \"cold\" data was relegated to slower, more economical HDD tiers. This was the accepted compromise, a necessary balancing act between performance and budget.\n\nHowever, when analyzing the Total Cost of Ownership (TCO), that equation has been inverted. Flash now provides a superior economic advantage. A \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Ftechnology\u002Feconomics-of-exabyte-data-storage.html?fbclid=IwY2xjawMCs9lleHRuA2FlbQIxMQBicmlkETBrdkJHVmZROXhWcWFVa3RWAR7Cdu2fEX4BwDCDDhbs7LoO83F0iz2-nlVbvkztCC58CiA3aPgGAKVOFDrHGw_aem_9gUqa28yTbJw3i2IvSirKQ\">whitepaper by Solidigm and VAST\u003C\u002Fa> quantifies this, revealing that flash offers a __58.9% lower TCO__ than traditional HDD tiers.\n\nRecent innovations from Solidigm and VAST are changing storage economics and eliminating the tiered storage paradigm completely.  It is not cheaper to store cold data on flash. The cheapest medium for data is flash. Gone is the need for tiers, and with them the auto-tiering policies, the need for separate storage cluster designs, and the constant moving of data.\n\n#### The Technology Driving the Economic Shift\n\nCurrent hardware and software architectures have fundamentally altered storage economics, making a universal all-flash infrastructure more cost-effective than traditional hard drive systems. This shift is driven by two key innovations working in tandem.\n\n- __Hardware Breakthroughs in Density:__ The primary hardware change is the introduction of \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Fdata-center\u002Fd5\u002Fp5336.html#form=U.2%2015mm&cap=122.88TB\">Solidigm's 122-layer QLC NAND flash\u003C\u002Fa>, which provides unprecedented storage density. This allows for a massive reduction in the physical footprint of storage systems.\n- __Compounding Software Efficiencies:__ These hardware gains are amplified by VAST’s innovative software.\n  - VAST’s advanced \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper#a-breakthrough-approach-to-data-reduction\">Similarity data reduction\u003C\u002Fa> technique goes far beyond standard compression to find and eliminate redundancy at a granular level, meaning you need nearly 3x less raw storage capacity compared to HDD solutions. This level of granular data reduction can only be practical on flash because spinning media depends on sequential data for speed. When duplicate data is removed, many sequential reads look like random IO, which flash drives excel at, but HDDs struggle with.\n  - \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fblog\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes\">Locally decodable codes\u003C\u002Fa> provide an extremely efficient and resilient form of data protection, further enhancing cost-effectiveness by reducing wasted overhead.\n  - \u003Ca href=\"https:\u002F\u002Fwww.vastdata.com\u002Fwhitepaper\u002F#VASTForesight\">VAST Foresight\u003C\u002Fa> is an intelligent data placement feature that groups data based on its predicted retention time before writing it to QLC flash, a strategy that improves the endurance and extends the lifespan of the storage system.\n  - Crucially, this entire solution is built for longevity. By offering 10-year support on QLC SSDs, the VAST platform eliminates the costly and disruptive hardware refresh cycles required by HDD systems every few years.\n\nTogether, these technologies mean a VAST system can store significantly more data on less physical hardware. An Exabyte of data that once required 52 racks of HDDs can now fit into just 5 racks, cutting data center space costs by over 90% and power and cooling costs by more than 75% leading to a total cost of ownership (TCO) that is over 50% lower than traditional HDD-based systems.\n\n#### The Proof is in the Numbers: A TCO Analysis\n\nAll of this is now documented in a \u003Ca href=\"https:\u002F\u002Fwww.solidigm.com\u002Fproducts\u002Ftechnology\u002Feconomics-of-exabyte-data-storage.html\">10-year Total Cost of Ownership (TCO) analysis\u003C\u002Fa>. While this analysis focuses on a 1EB deployment, the technical and economic advantages delivered by the platform begin at the smallest VAST cluster and extend to Zettabyte (ZB) scale. These savings come from several compounding factors:\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Eliminating Refresh Cycles:\u003C\u002Fspan> Traditional HDD infrastructures require costly and disruptive hardware refreshes every 3-5 years. The VAST Data platform offers 10-year support on QLC SSDs, avoiding multiple hardware repurchases and the associated labor costs. While the initial outlay for the SSD\u002FVAST solution might be higher, the total 10-year hardware cost is nearly 30% lower because those expensive HDD refresh cycles are eliminated.\u003C\u002Fp>\n\n![Total 10-year Hardware Cost ($M)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6IYwEHSOKe6RK1ThlhdQwD\u002F9713a93eb27ff55820b87e92a334a0af\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-1.webp)\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Effective Data Reduction:\u003C\u002Fspan> VAST’s Similarity data reduction technology achieves remarkable data reduction rates without the performance penalties that cripple deduplication solutions on HDDs. Because HDDs depend on sequential I\u002FO, HDD based tiers often use little to no effective data reduction (modeled at 1:1). The result: you need only one-third of the raw physical storage capacity with VAST compared to an HDD solution to achieve the same usable capacity.\u003C\u002Fp>\n\n![Calculated Raw Capacity Required (EB)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3E6tKrtcliZ4T9MdGRdogh\u002F8fe6bbe268068d2fc2bcfc16aa0f2ef3\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-2.webp)\n\n\u003Cp style=\"text-indent: -20px; margin-left: 36px;\">\u003Cb style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">3.\u003C\u002Fb> \u003Cspan style=\"font-weight: 700;\">Higher Density, LowerFootprint:\u003C\u002Fspan> The combination of Solidigm's ultra-dense 122TB SSDs and VAST's software efficiencies means a dramatically smaller physical presence. What might require 52 racks for an Exabyte of usable HDD storage can be consolidated into just five racks with the Solidigm + VAST solution. This translates into over 90% savings in data center space costs and a power and cooling cost reduction of over 75%. Together, the lower power and space requirements support more sustainable data center operations.\u003C\u002Fp>\n\n![Racks Needed](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1VE9KfFfGqxbQ8rkxyUbl3\u002Fb7968b4faaa541547d72bc192d111ae8\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-3.webp)\n\n![Total 10-Year Power Cost ($M)](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4GG8JLYtJ9YcwCrQ58kfSI\u002Fecd0faceed24172dfb3b466f7e1e534a\u002Fnew-storage-economics-solidigm-vast-deliver-all-ssd-capacity-4.webp)\n\nThis combination of extended hardware life, superior data reduction, and higher density fundamentally rewires the economics of enterprise storage.\n\nWhen flash is no longer a scarce, premium resource, the whole equation of enterprise computing changes. IT leaders no longer have to ration performance or make uncomfortable compromises between speed and cost. Instead, they can design around possibilities:\n\n- __AI at Full Fidelity:__ With flash now the most economical tier, enterprises can finally train and analyze on their entire data history, not just today’s “hot” subset. Aquatic Capital Management, for example, uses VAST to unify decades-worth of structured and unstructured market, documentation, and proprietary data into a single high-performance flash infrastructure—giving researchers both full corpus fidelity and extended historical breadth. As their Head of Infrastructure notes: “the more different multi-variant data sources that they can assemble, the more accurate their models can become.” The result? AI models that are smarter, more contextually rich, and more accurate—made possible by collapsing the old trade-offs of cost, speed, and retention.\n- __Analytics Without Delay:__ The NHL rebuilt its media platform on VAST, shifting a massive video archive onto flash and expanding into real-time game-footage operations so editors and teams can cut highlights and move content while games are still underway. With replication across all 32 arenas, the league shortened the path from capture to distribution and set itself up for computer-vision and AI discovery on the same library.   A flash-backed foundation turned archival footage into something you can actually work with in the moment, rather than overnight.  \n- __Operational Agility:__ At spinTwo, VAST transformed months-long life sciences compute workloads into just weeks, freeing researchers to focus on discovery instead of infrastructure headaches. 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since the introduction of RAID, and the storage array, in the late ‘80s storage architects have had to trade storage capacity for the resiliency they need to live up to the storage administrator’s first commandment “Thou shalt not lose thy user’s data.” Our Universal Storage systems break those trade-offs with new math, locally decodable erasure codes that can protect against multiple device failures without the overhead and rebuild complexity conventional solutions impose.",{"data":23076,"content":23077,"nodeType":635},{},[23078,23085,23093,23112,23135,23154,23161,23168,23175,23182,23189,23208,23215,23222,23229,23236,23243,23250,23273,23281,23288,23295,23314,23321,23328,23589],{"data":23079,"content":23080,"nodeType":634},{},[23081],{"data":23082,"marks":23083,"value":23084,"nodeType":633},{},[],"Ever since the introduction of RAID, and the storage array, in the late ‘80s storage architects have had to trade storage capacity for the resiliency they need to live up to the storage administrator’s first commandment “Thou shalt not lose thy user’s data.” The VAST Data Platform breaks those trade-offs with new math, locally decodable erasure codes that can protect against multiple device failures without the overhead and rebuild complexity conventional solutions impose.",{"data":23086,"content":23087,"nodeType":23092},{},[23088],{"data":23089,"marks":23090,"value":23091,"nodeType":633},{},[],"Reed-Solomon protection at a price","heading-3",{"data":23094,"content":23095,"nodeType":634},{},[23096,23100,23108],{"data":23097,"marks":23098,"value":23099,"nodeType":633},{},[],"The vast majority (no pun intended) of storage systems that use erasure coding, including most RAID 6 implementations, today use a class of error correction codes developed in 1960 by mathematicians Irving S. Reed and Gustave Solomon. A system using ",{"data":23101,"content":23103,"nodeType":1343},{"uri":23102},"https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FReed%E2%80%93Solomon_error_correction",[23104],{"data":23105,"marks":23106,"value":23107,"nodeType":633},{},[],"Reed-Solomon",{"data":23109,"marks":23110,"value":23111,"nodeType":633},{},[]," encoding can calculate two, three or even more ECC strips to protect a set of data strips.",{"data":23113,"content":23134,"nodeType":20767},{"target":23114},{"metadata":23115,"sys":23118,"fields":23125},{"tags":23116,"concepts":23117},[],[],{"space":23119,"id":23121,"type":566,"createdAt":23122,"updatedAt":23122,"environment":23123,"publishedVersion":723,"revision":789,"locale":512},{"sys":23120},{"type":495,"linkType":496,"id":497},"6DWk0gvfOQWlnMdKI71ebq","2023-08-31T22:31:01.617Z",{"sys":23124},{"id":504,"type":495,"linkType":505},{"title":23028,"description":23028,"file":23126},{"url":23127,"details":23128,"fileName":23133,"contentType":583},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6DWk0gvfOQWlnMdKI71ebq\u002Fa11bcd72ac66bb6e4b82a87a98bcf25e\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-1.webp",{"size":23129,"image":23130},33722,{"width":23131,"height":23132},2200,983,"breaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-1.webp",[],{"data":23136,"content":23137,"nodeType":634},{},[23138,23142,23150],{"data":23139,"marks":23140,"value":23141,"nodeType":633},{},[],"Since parity is the simplest form of erasure code the generalized notation for describing erasure coded data stripes is nD+xP for n data strips and x ECC or parity strips. A RAID6 or similar, system using 7 data strips and 2 ECC strips would, therefore, be 7D+2P. Reed-Solomon codes aren’t limited to one or two ECC strips theoretically supporting an arbitrary number of ECC strips. Some ",{"data":23143,"content":23145,"nodeType":1343},{"uri":23144},"..\u002F..\u002Fglossary\u002Fscale-out\u002F",[23146],{"data":23147,"marks":23148,"value":23149,"nodeType":633},{},[],"scale-out",{"data":23151,"marks":23152,"value":23153,"nodeType":633},{},[]," storage systems use Reed-Solomon codes with three or more protection strips.",{"data":23155,"content":23156,"nodeType":17115},{},[23157],{"data":23158,"marks":23159,"value":23160,"nodeType":633},{},[],"Wide stripes create problems",{"data":23162,"content":23163,"nodeType":634},{},[23164],{"data":23165,"marks":23166,"value":23167,"nodeType":633},{},[],"The problem with Reed-Solomon codes, beyond the complex Galois field math they use, is that reconstructing an unreadable data strip requires the data from all the surviving data strips, and at least one of the ECC strips to rebuild the data. In a 7D+2P system like our example above one 4KB read request from a user-application would be amplified to a dozen I\u002FOs and rebuilding from a failed drive will keep the remaining 8 drives in the set busy for a long while.",{"data":23169,"content":23170,"nodeType":634},{},[23171],{"data":23172,"marks":23173,"value":23174,"nodeType":633},{},[],"As a result, storage architects have to trade-off the greater efficiency and read performance, of wide data protection stripes against the I\u002FO amplification wide stripes, can create. Should a conventional storage array receive a request to perform a 4KB write in the middle of a 16D+4P erasure code stripe (the widest I know of in common use), the system would have to read all 16 strips of data, recalculate the four protection strips and then write all 20 strips back.",{"data":23176,"content":23177,"nodeType":634},{},[23178],{"data":23179,"marks":23180,"value":23181,"nodeType":633},{},[],"While log structures ameliorate this read-modify-write problem to some extent rebuilding that 16D+4P system after a device failure will require reading the entire contents of at least 16 of the surviving devices. As a result, most storage architects use narrower stripes like with lower resiliency and 33% overhead vs. the 25% overhead of a 16+4 stripe, sacrificing both resilience and efficiency to the downsides of Reed-Solomon coding.",{"data":23183,"content":23184,"nodeType":23092},{},[23185],{"data":23186,"marks":23187,"value":23188,"nodeType":633},{},[],"Locally Decodable Codes Break the Trade-Offs",{"data":23190,"content":23191,"nodeType":634},{},[23192,23196,23204],{"data":23193,"marks":23194,"value":23195,"nodeType":633},{},[],"VAST Data has invented a new class of error correction codes that borrow from principals that mathematicians define as locally-decodable codes to break the long-standing tradeoffs between writes stripe resilience and the cost of data protection. The locally-decodable aspect of our concept means that the ",{"data":23197,"content":23199,"nodeType":1343},{"uri":23198},"..\u002F..\u002Fglossary\u002Fvast-cluster\u002F",[23200],{"data":23201,"marks":23202,"value":23203,"nodeType":633},{},[],"VAST cluster",{"data":23205,"marks":23206,"value":23207,"nodeType":633},{},[]," can reconstruct unreadable data from only a “local” fraction of the total number of data strips in a protection stripe. For our codes that fraction is 1\u002FXth of the data strips where X is the number of protection strips in the set.",{"data":23209,"content":23210,"nodeType":17115},{},[23211],{"data":23212,"marks":23213,"value":23214,"nodeType":633},{},[],"Higher Resiliency, Lower Rebuild Cost",{"data":23216,"content":23217,"nodeType":634},{},[23218],{"data":23219,"marks":23220,"value":23221,"nodeType":633},{},[],"Using locally decodable codes allows us to use wide stripes, with very high levels of resiliency. The codes scale dynamically with the cluster as the system scales, where both N (data) and (K) resilience can scale independently. Using Markov calculations, we’ve determined that an example 150+4 encoded stripe has an MTTDL (Mean Time To Data Loss) of over 42 million years with just 2.66% overhead. Data is protected against four or more failures - which is relatively very high as compared to how traditional systems protect a failure domain - while the system can reconstruct the data by reading from just 1\u002F4th of the remaining stripes in an encoded write stripe.",{"data":23223,"content":23224,"nodeType":634},{},[23225],{"data":23226,"marks":23227,"value":23228,"nodeType":633},{},[],"While 150+4 is a somewhat typical example of the scale of our codes on an average petabyte-sized system, these codes can scale to provide greater levels of protection on smaller systems and can even scale wider on larger systems to feature higher levels of data protection - such as 500+8 - resulting in a mean time to data loss of over 1 quadrillion years.",{"data":23230,"content":23231,"nodeType":17115},{},[23232],{"data":23233,"marks":23234,"value":23235,"nodeType":633},{},[],"How Locally Decodable Codes Work",{"data":23237,"content":23238,"nodeType":634},{},[23239],{"data":23240,"marks":23241,"value":23242,"nodeType":633},{},[],"Reed-Solomon codes generate P protection strips from a single set of data strips. Since each Reed-Solomon protection strip is generated from all of the protected data strips the calculation for regenerating the data from a lost strip requires all the surviving data strips and one or more protection strips.",{"data":23244,"content":23245,"nodeType":634},{},[23246],{"data":23247,"marks":23248,"value":23249,"nodeType":633},{},[],"Our locally decodable codes, on the other hand, calculate each protection strip from slightly different sets of data strips across more than just one stripe of data. This allows protection strips to “stand in” for groups of data strips in the rebuild calculations. A VAST system using P protection strips can reconstruct data from 1\u002FPth the data strips. For our typical nD+4P encoding we need to read just 1\u002F4th the data during a reconstruction where a Reed-Solomon based system would need to read it all.",{"data":23251,"content":23272,"nodeType":20767},{"target":23252},{"metadata":23253,"sys":23256,"fields":23263},{"tags":23254,"concepts":23255},[],[],{"space":23257,"id":23259,"type":566,"createdAt":23260,"updatedAt":23260,"environment":23261,"publishedVersion":723,"revision":789,"locale":512},{"sys":23258},{"type":495,"linkType":496,"id":497},"7f2xs9JPmdEMqjnjLBnB0V","2023-08-31T22:31:36.828Z",{"sys":23262},{"id":504,"type":495,"linkType":505},{"title":23028,"description":23028,"file":23264},{"url":23265,"details":23266,"fileName":23271,"contentType":583},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7f2xs9JPmdEMqjnjLBnB0V\u002F88b6932c5f2a93988792fd33bbdfe5f6\u002Fbreaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-2.webp",{"size":23267,"image":23268},86276,{"width":23269,"height":23270},2126,2006,"breaking-resiliency-trade-offs-with-locally-decodable-erasure-codes-2.webp",[],{"data":23274,"content":23275,"nodeType":634},{},[23276],{"data":23277,"marks":23278,"value":23280,"nodeType":633},{},[23279],{"type":21052},"Reconstructing a failed drive with 1\u002F4th the I\u002FO",{"data":23282,"content":23283,"nodeType":634},{},[23284],{"data":23285,"marks":23286,"value":23287,"nodeType":633},{},[],"For our typical 150D+4P protection level that means the system has to read 1\u002F4th of 150 data strips or 38 strips. The four protection strips contain enough data about the remaining 112 data strips that the contents of the missing strip can be calculated without them. This allows us to break the trade-off between storage efficiency and rebuild complexity that systems using Reed-Solomon codes are forced into.",{"data":23289,"content":23290,"nodeType":23092},{},[23291],{"data":23292,"marks":23293,"value":23294,"nodeType":633},{},[],"Empowered by DASE",{"data":23296,"content":23297,"nodeType":634},{},[23298,23302,23310],{"data":23299,"marks":23300,"value":23301,"nodeType":633},{},[],"The math of locally decodable codes is only part of the story. Conventional storage platforms would struggle to assemble 150 data strips in memory, and then write that data to 154 backend SSDs without bottlenecking on their SAS links or inter-node networks. Our disaggregated shared everything architecture allows our system to accumulate write data in ",{"data":23303,"content":23305,"nodeType":1343},{"uri":23304},"..\u002F..\u002Fglossary\u002Fstorage-class-memory\u002F",[23306],{"data":23307,"marks":23308,"value":23309,"nodeType":633},{},[],"Storage Class Memory",{"data":23311,"marks":23312,"value":23313,"nodeType":633},{},[]," and destage it to SSDs in strips optimized to the SSD’s internal erase blocks, minimizing wear at the same time. More on that next time.",{"data":23315,"content":23316,"nodeType":23092},{},[23317],{"data":23318,"marks":23319,"value":23320,"nodeType":633},{},[],"Breaking Tradeoffs",{"data":23322,"content":23323,"nodeType":634},{},[23324],{"data":23325,"marks":23326,"value":23327,"nodeType":633},{},[],"Locally decodable codes and the DASE architecture allow us to break the traditional trade-off between efficiency, protection and rebuild complexity. 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seems like every week another ransomware attack against a major company is in the news—and those are only the attacks announced publicly. Gartner expects at least 75% of IT organizations to face one or more ransomware attacks by 2025. And many of the IT leaders I meet operate with a “not if or when, but how many” mindset.",{"data":23690,"content":23691,"nodeType":634},{},[23692],{"data":23693,"marks":23694,"value":23695,"nodeType":633},{},[],"It used to be enough for organizations to develop a hardened network perimeter that would satisfy security concerns, but the interconnectedness of today’s various on-prem, hybrid cloud, and SaaS applications present a massive opportunity for bad actors to infiltrate your network.",{"data":23697,"content":23698,"nodeType":634},{},[23699],{"data":23700,"marks":23701,"value":23702,"nodeType":633},{},[],"That means the primary DR consideration for almost any organization is how fast you can recover. Having backup data is only part of the equation. You must ensure you can quickly restore those backups – and this is where legacy backup targets cannot help you.",{"data":23704,"content":23705,"nodeType":23092},{},[23706],{"data":23707,"marks":23708,"value":23709,"nodeType":633},{},[],"The Evolving Nature of Restores",{"data":23711,"content":23712,"nodeType":634},{},[23713],{"data":23714,"marks":23715,"value":23716,"nodeType":633},{},[],"We used to think about RTO objectives in hourly timeframes, but today’s sophisticated attacks that lock up entire IT estates have shifted such intervals to weeks, if not months. And while the nature of ransomware attacks is well understood, the path to recovery is less clear. Some organizations will pay the attackers the ransom to (presumably) regain access to their data, while others may choose to immediately start restoring data to their last known non-infected state. If you can do the latter well, you can save your organization massive ransom payouts, and more importantly, protect yourself from future attacks.",{"data":23718,"content":23719,"nodeType":634},{},[23720],{"data":23721,"marks":23722,"value":23723,"nodeType":633},{},[],"The path to fast restores is typically dependent on a few variables:",{"data":23725,"content":23726,"nodeType":23779},{},[23727,23737,23747,23769],{"data":23728,"content":23729,"nodeType":17769},{},[23730],{"data":23731,"content":23732,"nodeType":634},{},[23733],{"data":23734,"marks":23735,"value":23736,"nodeType":633},{},[],"Your network topology, or the path your data takes to move from source to destination and the time it needs to get there. Organizations often optimize bandwidth through data reduction, load balancing, throttling, etc.",{"data":23738,"content":23739,"nodeType":17769},{},[23740],{"data":23741,"content":23742,"nodeType":634},{},[23743],{"data":23744,"marks":23745,"value":23746,"nodeType":633},{},[],"Your number of data movers (physical or virtual), i.e. the backup agents running on your servers. Increasing the number of data movers in a system enables multi-streamed restores.",{"data":23748,"content":23749,"nodeType":17769},{},[23750],{"data":23751,"content":23752,"nodeType":634},{},[23753,23757,23765],{"data":23754,"marks":23755,"value":23756,"nodeType":633},{},[],"Your landing zone, or a clean network destination for the recovery of your immutable data, or ",{"data":23758,"content":23760,"nodeType":1343},{"uri":23759},"..\u002F..\u002Fblog\u002Fmaking-data-indestructible\u002F",[23761],{"data":23762,"marks":23763,"value":23764,"nodeType":633},{},[],"indestructible snapshots",{"data":23766,"marks":23767,"value":23768,"nodeType":633},{},[],", in the event of an attack.",{"data":23770,"content":23771,"nodeType":17769},{},[23772],{"data":23773,"content":23774,"nodeType":634},{},[23775],{"data":23776,"marks":23777,"value":23778,"nodeType":633},{},[],"Your data protection target. In enterprise data centers this is most often the bottleneck, as traditional backup appliances read (restore) data about 20% as fast as they write (backup) data. Therefore, even if you do everything else right (steps 1-3 above), restores are only as fast as the read performance of the backup target.","ordered-list",{"data":23781,"content":23782,"nodeType":634},{},[23783],{"data":23784,"marks":23785,"value":23786,"nodeType":633},{},[],"Purpose-built backup appliances (PBBAs) simply cannot deliver strong read performance. While they can write data quickly to disk and deduplicate it to optimize for capacity, these same characteristics inherently limit them from restoring large-scale data sets quickly, which is the name of the game in the ransomware era. PBBAs are a bit like Hotel California in that way: easy to get data in for a backup, but hard to get it out for a restore.",{"data":23788,"content":23789,"nodeType":634},{},[23790,23794,23802],{"data":23791,"marks":23792,"value":23793,"nodeType":633},{},[],"In fact, the smaller the system block size (Dell EMC PowerProtect\u002FData Domain has variable block lengths between 4kb and 8kb, for example) the more individual blocks that must be rehydrated via random IO when restoring data. Disk doesn’t do random IO well, thanks to time-consuming disk head movement. (Read more about the “rehydration tax” in conventional storage systems in our ",{"data":23795,"content":23797,"nodeType":1343},{"uri":23796},"..\u002F..\u002Fresources\u002Fforms\u002Fwhite-papers\u002Fall-data-reduction-is-not-created-equal",[23798],{"data":23799,"marks":23800,"value":23801,"nodeType":633},{},[],"data reduction white paper",{"data":23803,"marks":23804,"value":23805,"nodeType":633},{},[],".) And when you consider that a large dataset was 500GB when PBBAs came to market in the early 2000s, it’s no wonder that today’s backups weigh heavily upon the limitations of the architecture. The result is that large, full system recoveries via legacy backup appliances can last weeks.",{"data":23807,"content":23808,"nodeType":634},{},[23809,23813,23818,23822,23829],{"data":23810,"marks":23811,"value":23812,"nodeType":633},{},[],"In fact, let’s consider it a modern data protection truth: ",{"data":23814,"marks":23815,"value":23817,"nodeType":633},{},[23816],{"type":17122},"deduplication on hard drives will never work for fast restores",{"data":23819,"marks":23820,"value":23821,"nodeType":633},{},[],". It’s for this reason that Data Domain and other legacy PBBAs never discuss restore performance in their spec sheets, leaving customers to discover their recovery limitations at the worst possible times. Backup throughput and fitting your backup jobs into tight windows is important, but as my colleague ",{"data":23823,"content":23825,"nodeType":1343},{"uri":23824},"..\u002F..\u002Fresources\u002Fvideos\u002Fhow-to-enable-99-9999-uptime-across-100s-of-petabytes-vast-forward-ep-4",[23826],{"data":23827,"marks":23828,"value":23031,"nodeType":633},{},[],{"data":23830,"marks":23831,"value":23832,"nodeType":633},{},[]," likes to say, you only back up to restore.",{"data":23834,"content":23835,"nodeType":634},{},[23836],{"data":23837,"marks":23838,"value":23839,"nodeType":633},{},[],"Therefore, it’s imperative to ensure that your backup storage has the performance to meet your RTO needs. For many organizations, this means a backup target built on all-flash storage while leveraging global data algorithms that optimize cost without impacting performance.",{"data":23841,"content":23842,"nodeType":23092},{},[23843],{"data":23844,"marks":23845,"value":23846,"nodeType":633},{},[],"All-Flash Restores. Archive Economics.",{"data":23848,"content":23849,"nodeType":634},{},[23850],{"data":23851,"marks":23852,"value":23853,"nodeType":633},{},[],"I joined VAST to lead our data protection division and help customers understand how they could use an all-flash storage system for all of their data – backups and archives included.",{"data":23855,"content":23856,"nodeType":634},{},[23857,23861,23869],{"data":23858,"marks":23859,"value":23860,"nodeType":633},{},[],"VAST has flipped the script on backup target restore performance: the fundamental differences in our ",{"data":23862,"content":23864,"nodeType":1343},{"uri":23863},"..\u002F..\u002Fglossary\u002Fdisaggregated-shared-everything-architecture-dase",[23865],{"data":23866,"marks":23867,"value":23868,"nodeType":633},{},[],"DASE",{"data":23870,"marks":23871,"value":23872,"nodeType":633},{},[]," architecture allows our system to read data 8x faster than we write it. And so whereas legacy PBBAs like Dell PowerProtect\u002FData Domain are almost always where restores bottleneck, the VAST Data Platform is the fastest part of the restore function.",{"data":23874,"content":23875,"nodeType":634},{},[23876],{"data":23877,"marks":23878,"value":23879,"nodeType":633},{},[],"But performance is just one part of the equation. With VAST customers enjoy an exabyte-scale platform with a single global data reduction pool, rather than having multiple dedupe pools with the same data on each pool.",{"data":23881,"content":23882,"nodeType":634},{},[23883],{"data":23884,"marks":23885,"value":23886,"nodeType":633},{},[],"Moreover, our architecture and data algorithms enable unprecedented levels of flash affordability and system longevity that eliminate the complexity of buying, deploying, and managing backup infrastructure. No longer do customers have to balance cost versus restore performance or worry about capacity and scale.",{"data":23888,"content":23889,"nodeType":634},{},[23890,23894,23901],{"data":23891,"marks":23892,"value":23893,"nodeType":633},{},[],"Don’t wait to get protected from the scourge of ransomware. Learn more about how VAST Data can help you protect your environment and meet your goals by scheduling a demo, or reaching out to ",{"data":23895,"content":23896,"nodeType":1343},{"uri":17574},[23897],{"data":23898,"marks":23899,"value":23900,"nodeType":633},{},[],"hello@vastdata.com",{"data":23902,"marks":23903,"value":23904,"nodeType":633},{},[]," to learn 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