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Organizations can deploy VAST AI OS clusters in public cloud environments while maintaining consistent lifecycle governance across their entire infrastructure fleet.",[],{},{"nodeType":1921,"data":12947,"content":12948},{},[12949],{"nodeType":1907,"value":12950,"marks":12951,"data":12952},"This abstraction of infrastructure location enables workload mobility without operational fragmentation. AI systems can scale across regions and providers without re-architecting management processes or compromising governance controls.",[],{},{"nodeType":12263,"data":12954,"content":12955},{},[12956],{"nodeType":1907,"value":12957,"marks":12958,"data":12959},"Polaris and DataSpace: Infrastructure and Data Abstraction Working Together",[],{},{"nodeType":1921,"data":12961,"content":12962},{},[12963],{"nodeType":1907,"value":12964,"marks":12965,"data":12966},"VAST DataSpace provides the distributed namespace and global data fabric that unify clusters across environments. 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Where DataSpace abstracts data location, Polaris abstracts infrastructure location.",[],{},{"nodeType":12292,"data":12975,"content":12994},{"target":12976},{"metadata":12977,"sys":12980,"fields":12987},{"tags":12978,"concepts":12979},[],[],{"space":12981,"id":12983,"type":934,"createdAt":12984,"updatedAt":12984,"environment":12985,"publishedVersion":847,"revision":1136,"locale":673},{"sys":12982},{"type":657,"linkType":658,"id":659},"3ygpD5eJzY6TG1HdCOZKZ","2026-02-25T15:59:09.992Z",{"sys":12986},{"id":665,"type":657,"linkType":666},{"title":12792,"description":12792,"file":12988},{"url":12989,"details":12990,"fileName":12993,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3ygpD5eJzY6TG1HdCOZKZ\u002F46ea8d96d1ed20b0c6a03a96199b17af\u002Fpolaris-brings-vast-ai-os-to-multi-cloud-environments-2.webp",{"size":12991,"image":12992},69564,{"width":1144,"height":12386},"polaris-brings-vast-ai-os-to-multi-cloud-environments-2.webp",[],{"nodeType":1921,"data":12996,"content":12997},{},[12998],{"nodeType":1907,"value":12999,"marks":13000,"data":13001},"Polaris governs how VAST AI OS clusters are instantiated, secured, upgraded, and interconnected. 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Today, that’s no longer true: a TV epic might be filmed in Vancouver, while editing, special effects, rendering, sound design, and 20 other functions happen in Hollywood, Seoul, or remotely in the cloud, before executives in New York review the dailies.",[],{},{"nodeType":1921,"data":13449,"content":13450},{},[13451],{"nodeType":1907,"value":13452,"marks":13453,"data":13454},"VAST’s Global Access lets you build a global namespace across multiple VAST clusters, making folders\u002Fbuckets available across multiple locations with local I\u002FO performance. This way, the crew in Hollywood can load the camera output into their local VAST cluster, automatically making that footage accessible in NY seconds later. Even better, users can write to those global folders as if they were local too. Unlike Google Drive, Dropbox, and most previous global namespace solutions, VAST’s global namespace is strictly consistent.",[],{},{"nodeType":12263,"data":13456,"content":13457},{},[13458],{"nodeType":1907,"value":13459,"marks":13460,"data":13462},"Why Strict Consistency Matters",[13461],{"type":12411},{},{"nodeType":1921,"data":13464,"content":13465},{},[13466],{"nodeType":1907,"value":13467,"marks":13468,"data":13469},"Strict consistency ensures that multiple users, and\u002For servers, in multiple locations can read and write to the same folder—or even the same file—and every user always sees the latest version. When a user in London updates a file, any user accessing that file from any location will see those updates immediately.",[],{},{"nodeType":1921,"data":13471,"content":13472},{},[13473],{"nodeType":1907,"value":13474,"marks":13475,"data":13476},"Sync-and-share solutions (like Dropbox), most object stores, and most legacy global namespace solutions rely on an eventual consistency model, which is far less rigorous. For instance, a user in Chicago accessing a file that’s currently being updated in London may still read see a locally cached version because the local copy has not yet synchronized with the changes.",[],{},{"nodeType":1921,"data":13478,"content":13479},{},[13480],{"nodeType":1907,"value":13481,"marks":13482,"data":13483},"When users in two different locations write to the same file faster than the system can make that file consistent, the last writer wins. That means the version of the file with the later modification time (MTIME) becomes the current version. While the system may store both conflicting versions and generate an alert indicating a “collision” on a given file, users must manually merge the updates. Only then will both Joe in Chicago’s and Nigel in London’s updates be reflected in the final document.",[],{},{"nodeType":12263,"data":13485,"content":13486},{},[13487,13492],{"nodeType":1907,"value":13488,"marks":13489,"data":13491},"Satellites Provide Local Cache",[13490],{"type":12411},{},{"nodeType":1907,"value":13493,"marks":13494,"data":13495}," ",[],{},{"nodeType":1921,"data":13497,"content":13498},{},[13499],{"nodeType":1907,"value":13500,"marks":13501,"data":13502},"Before we dig into the details of how VAST Global Access delivers strict consistency, let’s take a step back to understand how the global namespace operates from the user’s and, of course, the system operator’s point of view. Global Access, like most VAST AI Operating System services, is managed at the folder level, giving VAST customers full control over where their data is available. ",[],{},{"nodeType":1921,"data":13504,"content":13505},{},[13506,13510,13516,13520,13527],{"nodeType":1907,"value":13507,"marks":13508,"data":13509},"For each global folder, one VAST cluster serves as that global folder’s Origin",[],{},{"nodeType":1907,"value":13511,"marks":13512,"data":13515},",",[13513,13514],{"type":12326},{"type":12411},{},{"nodeType":1907,"value":13517,"marks":13518,"data":13519}," holding a full copy of the folder and its contents. Other clusters in a ",[],{},{"nodeType":1916,"data":13521,"content":13523},{"uri":13522},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fdataspace",[13524],{"nodeType":1907,"value":3525,"marks":13525,"data":13526},[],{},{"nodeType":1907,"value":13528,"marks":13529,"data":13530}," can hold Satellites of the global folder, caching its contents and making them available at the Satellite’s locations with VAST’s customary all-flash performance. ",[],{},{"nodeType":1921,"data":13532,"content":13533},{},[13534],{"nodeType":1907,"value":13535,"marks":13536,"data":13537},"It’s important to remember that Origin and Satellite are roles that apply to the relationship between VAST clusters for a specific global folder they share. These roles do not define the relationship between the VAST clusters overall. A single cluster can hold the Origin for some global folders and be a Satellite for others.",[],{},{"nodeType":12292,"data":13539,"content":13557},{"target":13540},{"metadata":13541,"sys":13544,"fields":13551},{"tags":13542,"concepts":13543},[],[],{"space":13545,"id":13547,"type":934,"createdAt":13548,"updatedAt":13548,"environment":13549,"publishedVersion":847,"revision":1136,"locale":673},{"sys":13546},{"type":657,"linkType":658,"id":659},"3iMd557tye5EcXJFHnVFrT","2025-01-18T00:08:45.399Z",{"sys":13550},{"id":665,"type":657,"linkType":666},{"title":13402,"description":13402,"file":13552},{"url":13553,"details":13554,"fileName":13437,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3iMd557tye5EcXJFHnVFrT\u002F544a116509da3af9930436e958c059f7\u002Fintroducing-global-access-vasts-global-namespace.webp",{"size":13555,"image":13556},30964,{"width":1144,"height":1145},[],{"nodeType":1921,"data":13559,"content":13560},{},[13561],{"nodeType":1907,"value":13562,"marks":13563,"data":13565},"Figure 1: Global Access makes folders available across multiple locations.",[13564],{"type":12326},{},{"nodeType":1921,"data":13567,"content":13568},{},[13569],{"nodeType":1907,"value":13570,"marks":13571,"data":13572},"Figure 1 shows how a production studio could use Global Access to simplify the process of creating a new show:",[],{},{"nodeType":12397,"data":13574,"content":13575},{},[13576,13595,13622],{"nodeType":12401,"data":13577,"content":13578},{},[13579],{"nodeType":1921,"data":13580,"content":13581},{},[13582,13586,13591],{"nodeType":1907,"value":13583,"marks":13584,"data":13585},"In LA, the cluster is the Origin of the ",[],{},{"nodeType":1907,"value":13587,"marks":13588,"data":13590},"\u002Fmedia",[13589],{"type":12326},{},{"nodeType":1907,"value":13592,"marks":13593,"data":13594}," global folder, where raw footage is saved directly from the cameras, and a Satellite of the \u002Frenders folder.",[],{},{"nodeType":12401,"data":13596,"content":13597},{},[13598],{"nodeType":1921,"data":13599,"content":13600},{},[13601,13605,13609,13613,13618],{"nodeType":1907,"value":13602,"marks":13603,"data":13604},"A VAST cluster in the cloud is a Satellite for the ",[],{},{"nodeType":1907,"value":13587,"marks":13606,"data":13608},[13607],{"type":12326},{},{"nodeType":1907,"value":13610,"marks":13611,"data":13612}," folder and the Origin for the ",[],{},{"nodeType":1907,"value":13614,"marks":13615,"data":13617},"\u002Frenders",[13616],{"type":12326},{},{"nodeType":1907,"value":13619,"marks":13620,"data":13621}," folder, where the cloud servers save processed work and intermediate outputs.",[],{},{"nodeType":12401,"data":13623,"content":13624},{},[13625],{"nodeType":1921,"data":13626,"content":13627},{},[13628,13632,13636,13640,13644],{"nodeType":1907,"value":13629,"marks":13630,"data":13631},"The VAST cluster in New York holds Satellites for both the ",[],{},{"nodeType":1907,"value":13587,"marks":13633,"data":13635},[13634],{"type":12326},{},{"nodeType":1907,"value":13637,"marks":13638,"data":13639}," and ",[],{},{"nodeType":1907,"value":13614,"marks":13641,"data":13643},[13642],{"type":12326},{},{"nodeType":1907,"value":13645,"marks":13646,"data":13647}," folders, enabling the NY executives to view the cloud server’s nightly output and the raw footage.",[],{},{"nodeType":1921,"data":13649,"content":13650},{},[13651,13655,13659,13663,13667],{"nodeType":1907,"value":13652,"marks":13653,"data":13654},"With limited GPU resources on-premises, the studio’s job manager (in this hypothetical case, the VAST DataEngine) sends some overnight rendering and color correction jobs to run on a cloud provider. This ensures that completed shots are ready for executives in New York by 8 AM Eastern Time. The VAST cluster in the cloud will fetch the required footage from the ",[],{},{"nodeType":1907,"value":13587,"marks":13656,"data":13658},[13657],{"type":12326},{},{"nodeType":1907,"value":13660,"marks":13661,"data":13662}," folder on the LA cluster as the cloud severs run their assigned jobs. The cloud cluster uses its local SSDs to cache the data, so those servers have all-flash performance during processing, and write their results to the ",[],{},{"nodeType":1907,"value":13614,"marks":13664,"data":13666},[13665],{"type":12326},{},{"nodeType":1907,"value":13668,"marks":13669,"data":13670}," folder.",[],{},{"nodeType":12263,"data":13672,"content":13673},{},[13674],{"nodeType":1907,"value":13675,"marks":13676,"data":13678},"Leases Maintain Strict Consistency",[13677],{"type":12411},{},{"nodeType":1921,"data":13680,"content":13681},{},[13682],{"nodeType":1907,"value":13683,"marks":13684,"data":13685},"This brings us to how VAST Global Access maintains strict consistency across any number of VAST clusters that provide Global Access to a folder. Strict consistency ensures that any user or application accessing a VAST element as a file or object always sees the latest version, including all changes made to that element, even if updates occur from a user who’s connected to a different cluster while accessing the file. ",[],{},{"nodeType":1921,"data":13687,"content":13688},{},[13689],{"nodeType":1907,"value":13690,"marks":13691,"data":13692},"The VAST global namespace maintains consistency through read and write leases. At any given time, only one cluster in the global namespace holds the write lease for an element. While clients can initiate writes from any cluster in the namespace, all writes are processed at the cluster holding the write lease. As of VAST 5.2, all write leases are held by the Origin cluster for the global folder. Satellites hold read leases on data in their local cache; a read lease on an element, or a byte-range within it, indicates the cached data is up to date.",[],{},{"nodeType":1921,"data":13694,"content":13695},{},[13696],{"nodeType":1907,"value":13697,"marks":13698,"data":13699},"When a client writes to an element at a Satellite cluster, the Satellite cluster synchronously proxies the write to the Origin cluster, which directly writes the update to its local, authoritative copy of the folder. The write also invalidates any read leases at Satellite clusters, informing the Satellites that any cached copy of the newly written data is invalid. When a client tries to read the new data from a Satellite, that Satellite will fetch the latest version of the data from the Origin.",[],{},{"nodeType":1921,"data":13701,"content":13702},{},[13703,13707,13712],{"nodeType":1907,"value":13704,"marks":13705,"data":13706},"While synchronously proxying writes ensures strict consistency, it does, like synchronous replication, add the roundtrip network latency between a Satellite",[],{},{"nodeType":1907,"value":13708,"marks":13709,"data":13711}," ",[13710],{"type":12411},{},{"nodeType":1907,"value":13713,"marks":13714,"data":13715},"and a folder’s Origin for remote writes. To mitigate this, VAST customers can deploy multiple global folders and, as in our example above, place the Origin of each folder near the clients that that will write in that folder.",[],{},{"nodeType":1921,"data":13717,"content":13718},{},[13719],{"nodeType":1907,"value":13720,"marks":13721,"data":13722},"In the future, write-leases will become portable, the write-lease for a newly created file on the cluster where the file is created. Once the new file is asynchronously replicated to the Origin, the write lease can migrate there.",[],{},{"nodeType":12263,"data":13724,"content":13725},{},[13726,13731],{"nodeType":1907,"value":13727,"marks":13728,"data":13730},"Controlling Satellite Caches",[13729],{"type":12411},{},{"nodeType":1907,"value":13493,"marks":13732,"data":13733},[],{},{"nodeType":1921,"data":13735,"content":13736},{},[13737],{"nodeType":1907,"value":13738,"marks":13739,"data":13740},"When configuring a VAST cluster as a Satellite for a global folder, you can control how that Satellite caches and prefetches data from the Origin. As part of this configuration, you specify the amount of the Satellite’s local flash to allocate as a cache for the global folder. The Satellite cluster manages the specified capacity by evicting stale data and replacing it with new data when the cache is full.",[],{},{"nodeType":1921,"data":13742,"content":13743},{},[13744],{"nodeType":1907,"value":13745,"marks":13746,"data":13747},"By default, Satellites fetch data and metadata from the Origin only when clients request access. Satellite clusters will do some fetch-ahead on sequential reads, but this is limited to files, objects, and folders accessed by a client at the Satellite. ",[],{},{"nodeType":1921,"data":13749,"content":13750},{},[13751,13755,13759,13763,13767,13771,13775],{"nodeType":1907,"value":13752,"marks":13753,"data":13754},"VAST 5.2 gives admins control over how Satellites prefetch data. Admins can configure each Satellite",[],{},{"nodeType":1907,"value":13708,"marks":13756,"data":13758},[13757],{"type":12411},{},{"nodeType":1907,"value":13760,"marks":13761,"data":13762},"to prefetch data from a global folder as it changes, enabling users to access updated data at the Satellite",[],{},{"nodeType":1907,"value":13708,"marks":13764,"data":13766},[13765],{"type":12411},{},{"nodeType":1907,"value":13768,"marks":13769,"data":13770},"without the delay of fetching it across the WAN. In our movie studio scenario, the New York cluster could be configured to prefetch all data from the ",[],{},{"nodeType":1907,"value":13587,"marks":13772,"data":13774},[13773],{"type":12326},{},{"nodeType":1907,"value":13776,"marks":13777,"data":13778}," folder. Since executives aren’t known for their patience, prefetching ensures that everything they might want is readily available, justifying the expense of additional network bandwidth. ",[],{},{"nodeType":1921,"data":13780,"content":13781},{},[13782,13786,13790],{"nodeType":1907,"value":13783,"marks":13784,"data":13785},"Admins also have the option to configure a Satellite to prefetch only metadata changes to the global folder. This allows clients attached to the Satellite to see new files\u002Fobjects appear in the global folder immediately, without the overhead of sending data that may never be accessed. Going back to our example, the cloud cluster could be set to prefetch metadata for the \u002F",[],{},{"nodeType":1907,"value":12176,"marks":13787,"data":13789},[13788],{"type":12326},{},{"nodeType":1907,"value":13791,"marks":13792,"data":13793}," folder. This would accelerate the metadata lookups required for the cloud cluster to run its assigned jobs while avoiding the unnecessary bandwidth consumption and storage overhead of prefetching data most jobs will not access. ",[],{},{"nodeType":1921,"data":13795,"content":13796},{},[13797],{"nodeType":1907,"value":13798,"marks":13799,"data":13800},"As you can see, Global Access, VAST’s global namespace, enables customers to make their data available across multiple VAST clusters, both on-premises and in the cloud, without read\u002Fwrite limitations. ",[],{},{"nodeType":1921,"data":13802,"content":13803},{},[13804,13808,13815],{"nodeType":1907,"value":13805,"marks":13806,"data":13807},"For more on the VAST global namespace, check out the below video: 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How VAST Built the De Facto Data Layer for AI","n-equals-1-vast-data-layer-ai","2026-04-22","Jeff Denworth, VAST Data Co-Founder",{"metadata":14020,"sys":14023,"fields":14032},{"tags":14021,"concepts":14022},[],[],{"space":14024,"id":14026,"type":14,"createdAt":14027,"updatedAt":14027,"environment":14028,"publishedVersion":901,"revision":1136,"contentType":14030,"locale":673},{"sys":14025},{"type":657,"linkType":658,"id":659},"2xmrLlDxYRoHNa8x4uSzug","2026-04-21T16:52:49.492Z",{"sys":14029},{"id":665,"type":657,"linkType":666},{"sys":14031},{"type":657,"linkType":671,"id":12176},{"entryTitle":14015,"image":14033},{"metadata":14034,"sys":14037,"fields":14044},{"tags":14035,"concepts":14036},[],[],{"space":14038,"id":14040,"type":934,"createdAt":14041,"updatedAt":14041,"environment":14042,"publishedVersion":847,"revision":1136,"locale":673},{"sys":14039},{"type":657,"linkType":658,"id":659},"5Y69mg54AfT7dNvy5S1vzj","2026-04-21T16:52:46.340Z",{"sys":14043},{"id":665,"type":657,"linkType":666},{"title":14015,"description":14015,"file":14045},{"url":14046,"details":14047,"fileName":14050,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5Y69mg54AfT7dNvy5S1vzj\u002F254f3fc834c05e9ea73097ddebd76365\u002FFeature_Image_Series-F-Blog_VAST-Data.webp",{"size":14048,"image":14049},36146,{"width":1144,"height":1145},"Feature Image_Series-F-Blog_VAST-Data.webp",{"nodeType":1922,"data":14052,"content":14053},{},[14054,14073,14106,14122,14129,14136,14170,14186,14208,14224,14260,14267,14301,14308,14324,14349,14357,14364,14383,14405,14412,14455,14471,14494,14501,14523,14530,14542,14604],{"nodeType":1921,"data":14055,"content":14056},{},[14057,14061,14069],{"nodeType":1907,"value":14058,"marks":14059,"data":14060},"Today, we announced that ",[],{},{"nodeType":1916,"data":14062,"content":14064},{"uri":14063},"https:\u002F\u002Fwww.vastdata.com\u002Fpress-releases\u002Fvast-series-f-financing-at-30-billion-valuation",[14065],{"nodeType":1907,"value":14066,"marks":14067,"data":14068},"VAST Data is now valued at $30B",[],{},{"nodeType":1907,"value":14070,"marks":14071,"data":14072},", establishing the company as the most valuable private data infrastructure software company in history. This new Series F round was led by Drive Capital and included many of the world’s most respected infrastructure investors, such as Access Industries, NVIDIA, Fidelity Management & Research Company, NEA, and more. Between the primary company investment and secondary sales of VAST stock, this round represents nearly $1B of investment and welcomes VAST into an elite circle of next-generation companies who’ve found product-market fit in the $100T AI market that has emerged seemingly overnight.\n\nGiven this unique position in the market, I thought I would take a moment to provide some perspective on how we’ve gotten here.\n\nTo start, it’s fair to say that VAST is working hard to build a generational company at the center of the data and AI revolution. From our perspective, generational companies share three fundamental qualities:",[],{},{"nodeType":12397,"data":14074,"content":14075},{},[14076,14086,14096],{"nodeType":12401,"data":14077,"content":14078},{},[14079],{"nodeType":1921,"data":14080,"content":14081},{},[14082],{"nodeType":1907,"value":14083,"marks":14084,"data":14085},"They build products that are 10x better than previous approaches",[],{},{"nodeType":12401,"data":14087,"content":14088},{},[14089],{"nodeType":1921,"data":14090,"content":14091},{},[14092],{"nodeType":1907,"value":14093,"marks":14094,"data":14095},"They address a market disruption that causes customers to abandon status quo",[],{},{"nodeType":12401,"data":14097,"content":14098},{},[14099],{"nodeType":1921,"data":14100,"content":14101},{},[14102],{"nodeType":1907,"value":14103,"marks":14104,"data":14105},"They out-execute, turning product-market fit and timing into a sustainable business",[],{},{"nodeType":1921,"data":14107,"content":14108},{},[14109,14113,14118],{"nodeType":1907,"value":14110,"marks":14111,"data":14112},"Unsurprisingly, these qualities combine to create the foundation of the VAST story and the thesis of our long-term potential. It turns out this mix is very hard to perfect, and as we were working through the investment and sharing our story to some of the most respected technology investors, we kept hearing back from them the same phrase … “N=1”.\n\nWhen something is described as “N=1,” it’s a signal that indicates that someone has ",[],{},{"nodeType":1907,"value":14114,"marks":14115,"data":14117},"“never seen anything like this before.”",[14116],{"type":12326},{},{"nodeType":1907,"value":14119,"marks":14120,"data":14121}," A category of one. Today, I’ll attempt to explain N=1 in terms of both VAST’s business posture, as well as our technology advantage.",[],{},{"nodeType":12263,"data":14123,"content":14124},{},[14125],{"nodeType":1907,"value":14126,"marks":14127,"data":14128},"N=1: The VAST Data business story",[],{},{"nodeType":1921,"data":14130,"content":14131},{},[14132],{"nodeType":1907,"value":14133,"marks":14134,"data":14135},"Since the dawn of ChatGPT, VAST Data’s business has grown exponentially. Six years earlier, we started working on a next-generation parallel systems architecture that found itself in the right time and right place when the world discovered the commercial application of deep learning. During periods of exponential growth, nearly every growth story is punctuated by a lack of profitability and significant burn of venture capital. To see examples of this, look no further than today’s AI labs, which are each growing their business like crazy while burning historic levels of venture capital on R&D.",[],{},{"nodeType":1921,"data":14137,"content":14138},{},[14139,14143,14148,14152,14157,14161,14166],{"nodeType":1907,"value":14140,"marks":14141,"data":14142},"The tradeoff between corporate efficiency and growth is not limited to AI labs. Over the last 40 years, companies have been forced to choose between investing in technology (aka ",[],{},{"nodeType":1907,"value":14144,"marks":14145,"data":14147},"innovation that comes with significant expenses",[14146],{"type":12326},{},{"nodeType":1907,"value":14149,"marks":14150,"data":14151},") or investing in a sustainable business (profitable and cash efficient operation). This tradeoff is best expressed in a financial metric that is known in technology circles as the ",[],{},{"nodeType":1907,"value":14153,"marks":14154,"data":14156},"Rule of X",[14155],{"type":12411},{},{"nodeType":1907,"value":14158,"marks":14159,"data":14160},". The Rule of X (also referred to as the Rule of 40) is often defined as the sum of a company’s growth and its free cashflow margin, where anything over 40% is considered a healthy and sustainable business. Not surprisingly, there’s a spectrum of performance as companies get measured by the Rule of 40 — where growth can be traded for efficiency or vice versa, but the sum of these numbers is the measure of how promising a business is.\n\nIf 40% is the benchmark of a “good” company, the best ten $10B+ public software companies (ranked by Rule of X performance) average out to 61%. What excites investors about VAST is our unprecedented mix of growth and profitability, demonstrating to the world that a radically disruptive product and focused team can break fundamental business tradeoffs. Without concern for cash burn, we’ve shown the world that we’ve built a business that can innovate ",[],{},{"nodeType":1907,"value":14162,"marks":14163,"data":14165},"and",[14164],{"type":12326},{},{"nodeType":1907,"value":14167,"marks":14168,"data":14169}," grow, since our business model is ultimately sustainable.",[],{},{"nodeType":1921,"data":14171,"content":14172},{},[14173,14177,14182],{"nodeType":1907,"value":14174,"marks":14175,"data":14176},"More succinctly: ",[],{},{"nodeType":1907,"value":14178,"marks":14179,"data":14181},"At 228%, our Rule of X measurement is wholly without comparison",[14180],{"type":12411},{},{"nodeType":1907,"value":14183,"marks":14184,"data":14185},".  N=1.",[],{},{"nodeType":12292,"data":14187,"content":14207},{"target":14188},{"metadata":14189,"sys":14192,"fields":14199},{"tags":14190,"concepts":14191},[],[],{"space":14193,"id":14195,"type":934,"createdAt":14196,"updatedAt":14196,"environment":14197,"publishedVersion":692,"revision":1136,"locale":673},{"sys":14194},{"type":657,"linkType":658,"id":659},"6rxw2lSvxWTNowGZ6Z8tLb","2026-04-21T17:34:39.623Z",{"sys":14198},{"id":665,"type":657,"linkType":666},{"title":14200,"description":14015,"file":14201},"N=1: How VAST Built the De Facto Data Layer for AI - Inline 1",{"url":14202,"details":14203,"fileName":14206,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6rxw2lSvxWTNowGZ6Z8tLb\u002Fc73509438170d3e0305da6b7b82cde58\u002FBlog-Rule40.webp",{"size":14204,"image":14205},48308,{"width":1144,"height":1145},"Blog-Rule40.webp",[],{"nodeType":1921,"data":14209,"content":14210},{},[14211,14215,14220],{"nodeType":1907,"value":14212,"marks":14213,"data":14214},"Given this, you would be right to ask yourself, “",[],{},{"nodeType":1907,"value":14216,"marks":14217,"data":14219},"Why did VAST take an investment if it’s profitable and generating cash?",[14218],{"type":12326},{},{"nodeType":1907,"value":14221,"marks":14222,"data":14223},"” Good question. Just as with the funds that came from our series B, C, D, and E, the money invested into VAST in our Series F will go right into the bank and sit alongside our growing cash reserves while they gain interest. In reality, public financing rounds serve two primary purposes for our company:",[],{},{"nodeType":14225,"data":14226,"content":14227},"ordered-list",{},[14228,14250],{"nodeType":12401,"data":14229,"content":14230},{},[14231],{"nodeType":1921,"data":14232,"content":14233},{},[14234,14238,14246],{"nodeType":1907,"value":14235,"marks":14236,"data":14237},"Each investment is a public and independent validation of our progress, and serves as a signal to the world that we’ve raised the bar yet again because of the worldwide adoption of ",[],{},{"nodeType":1916,"data":14239,"content":14241},{"uri":14240},"..\u002F..\u002Fplatform\u002Fai-os",[14242],{"nodeType":1907,"value":14243,"marks":14244,"data":14245},"VAST’s AI Operating System",[],{},{"nodeType":1907,"value":14247,"marks":14248,"data":14249},". Our customers take comfort in the vote of confidence that comes from companies such as Drive Capital, Fidelity, NEA, NVIDIA, and others.\n",[],{},{"nodeType":12401,"data":14251,"content":14252},{},[14253],{"nodeType":1921,"data":14254,"content":14255},{},[14256],{"nodeType":1907,"value":14257,"marks":14258,"data":14259},"Our ambitions are scaling with our warchest. As our cash reserves scale, we can make meaningful investments that will help our customers get more value from their AI efforts. With well over $1B of cash in the bank, VAST now has the optionality to make strategic investments that can accelerate the market adoption of our AI Operating System.",[],{},{"nodeType":12263,"data":14261,"content":14262},{},[14263],{"nodeType":1907,"value":14264,"marks":14265,"data":14266},"N=1: The VAST Data AI Operating System story",[],{},{"nodeType":1921,"data":14268,"content":14269},{},[14270,14274,14279,14283,14288,14292,14297],{"nodeType":1907,"value":14271,"marks":14272,"data":14273},"Once our business success story is appreciated, the natural question is: ",[],{},{"nodeType":1907,"value":14275,"marks":14276,"data":14278},"“Why is VAST able to achieve this breakout business story?\" ",[14277],{"type":12326},{},{"nodeType":1907,"value":14280,"marks":14281,"data":14282},"The answer leads people to understand that while we are displacing many legacy competitors across the data center stack, there is no company that offers a product as ambitious, as comprehensive, and as purpose-built for scalable AI workloads as VAST Data.\n\nIn 2016, we envisioned a future where the computational pressures of deep learning would upend every layer of the data center stack. At the time, OpenAI was just getting off the ground and NVIDIA was just starting to ship the world's first deep learning supercomputer, the DGX. Since then, parallel data processing has been more and more critical to the world’s AI endeavors. To put this into perspective, the AI factories that VAST supports today have over 1 ",[],{},{"nodeType":1907,"value":14284,"marks":14285,"data":14287},"billion ",[14286],{"type":12411},{},{"nodeType":1907,"value":14289,"marks":14290,"data":14291},"CUDA cores or over 1 ",[],{},{"nodeType":1907,"value":14293,"marks":14294,"data":14296},"million ",[14295],{"type":12411},{},{"nodeType":1907,"value":14298,"marks":14299,"data":14300},"tensor cores, all accessing a single VAST data platform.",[],{},{"nodeType":1921,"data":14302,"content":14303},{},[14304],{"nodeType":1907,"value":14305,"marks":14306,"data":14307},"The infrastructure we support does not stop scaling. As large-scale agency and agent swarms define the next generation of computing, all the world’s data infrastructure will need to stand up and answer the call of this level of parallelism. However, the universal need for scale was not so obvious until reasoning models and agents emerged in 2024-2025, and this was the spark that ignited a global re-platforming of the world’s data systems and has led our customers to conclude that VAST’s AI Operating System is critical to achieving future scaling ambitions.",[],{},{"nodeType":1921,"data":14309,"content":14310},{},[14311,14315,14320],{"nodeType":1907,"value":14312,"marks":14313,"data":14314},"We’ve been students of the predictions made by Ark Capital for the last 5 years, a firm that took an early stance on the impact of deep learning that is now undeniable. Looking forward, Ark paints a picture where our AI infrastructure investments grow 10x-15x over the next 4 years … much of this infrastructure may be in space, and, ",[],{},{"nodeType":1907,"value":14316,"marks":14317,"data":14319},"yes,",[14318],{"type":12326},{},{"nodeType":1907,"value":14321,"marks":14322,"data":14323}," VAST is working with partners to build space-based AI factories. That’s a good case for a global namespace, huh? 🙂 ",[],{},{"nodeType":12292,"data":14325,"content":14348},{"target":14326},{"metadata":14327,"sys":14330,"fields":14338},{"tags":14328,"concepts":14329},[],[],{"space":14331,"id":14333,"type":934,"createdAt":14334,"updatedAt":14335,"environment":14336,"publishedVersion":712,"revision":901,"locale":673},{"sys":14332},{"type":657,"linkType":658,"id":659},"7pYLHKNNYUojInoMg7H3i5","2026-04-21T17:33:51.690Z","2026-04-21T17:34:58.859Z",{"sys":14337},{"id":665,"type":657,"linkType":666},{"title":14339,"description":14015,"file":14340},"N=1: How VAST Built the De Facto Data Layer for AI - inline 2",{"url":14341,"details":14342,"fileName":14347,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7pYLHKNNYUojInoMg7H3i5\u002F0f86d1d628e76f8fa3c26db4f8cf0082\u002Fark-big-ideas.webp",{"size":14343,"image":14344},98832,{"width":14345,"height":14346},1600,900,"ark-big-ideas.webp",[],{"nodeType":1921,"data":14350,"content":14351},{},[14352],{"nodeType":1907,"value":14353,"marks":14354,"data":14356},"Source: Ark Invest's Big Ideas 2026",[14355],{"type":12326},{},{"nodeType":1921,"data":14358,"content":14359},{},[14360],{"nodeType":1907,"value":14361,"marks":14362,"data":14363},"Every aspect of the computing stack needs to be reimagined for this level of investment, this level of scale and application parallelism.",[],{},{"nodeType":1921,"data":14365,"content":14366},{},[14367,14371,14379],{"nodeType":1907,"value":14368,"marks":14369,"data":14370},"That’s why in 2016 we began work on redefining an altogether new systems architecture, called Disaggregated and Shared Everything (",[],{},{"nodeType":1916,"data":14372,"content":14374},{"uri":14373},"https:\u002F\u002Fwww.vastdata.com\u002Fplatform\u002Fhow-it-works",[14375],{"nodeType":1907,"value":14376,"marks":14377,"data":14378},"DASE",[],{},{"nodeType":1907,"value":14380,"marks":14381,"data":14382},", for short), that is today the foundation for our unified AI system. DASE picks up from the work Google did in the early 2000s, creating a new, more scalable, more resilient, and more affordable blueprint for distributed storage, databases, and computing. DASE is the world’s first “embarrassingly parallel” distributed system. DASE is famous for breaking tradeoffs between performance, capacity, scale, simplicity, and resilience … and it’s the foundation for the data and compute services provided by our AI Operating System.",[],{},{"nodeType":12292,"data":14384,"content":14404},{"target":14385},{"metadata":14386,"sys":14389,"fields":14396},{"tags":14387,"concepts":14388},[],[],{"space":14390,"id":14392,"type":934,"createdAt":14393,"updatedAt":14393,"environment":14394,"publishedVersion":692,"revision":1136,"locale":673},{"sys":14391},{"type":657,"linkType":658,"id":659},"3uXesk8BSVmIMnBPAQXLgE","2026-04-21T17:35:52.960Z",{"sys":14395},{"id":665,"type":657,"linkType":666},{"title":14397,"description":14015,"file":14398},"N=1: How VAST Built the De Facto Data Layer for AI - inline 3",{"url":14399,"details":14400,"fileName":14403,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3uXesk8BSVmIMnBPAQXLgE\u002Fa1d7d9ea52d9e25ca60ce772be9f7481\u002FBlog-DASE.webp",{"size":14401,"image":14402},63762,{"width":1144,"height":1145},"Blog-DASE.webp",[],{"nodeType":1921,"data":14406,"content":14407},{},[14408],{"nodeType":1907,"value":14409,"marks":14410,"data":14411},"With this foundation built, we then layer on a broad set of capabilities needed for VAST’s AI Operating System to be the essential full-stack computing platform for deep learning:",[],{},{"nodeType":12397,"data":14413,"content":14414},{},[14415,14425,14435,14445],{"nodeType":12401,"data":14416,"content":14417},{},[14418],{"nodeType":1921,"data":14419,"content":14420},{},[14421],{"nodeType":1907,"value":14422,"marks":14423,"data":14424},"The VAST DataStore is the first universal storage for all of your enterprise data",[],{},{"nodeType":12401,"data":14426,"content":14427},{},[14428],{"nodeType":1921,"data":14429,"content":14430},{},[14431],{"nodeType":1907,"value":14432,"marks":14433,"data":14434},"The VAST DataBase is the world’s first transactional + analytical data analytics platform",[],{},{"nodeType":12401,"data":14436,"content":14437},{},[14438],{"nodeType":1921,"data":14439,"content":14440},{},[14441],{"nodeType":1907,"value":14442,"marks":14443,"data":14444},"The VAST DataEngine hosts the RAG and agentic workflows that leverage the DataStore and DataBase as resources for real-time AI computing",[],{},{"nodeType":12401,"data":14446,"content":14447},{},[14448],{"nodeType":1921,"data":14449,"content":14450},{},[14451],{"nodeType":1907,"value":14452,"marks":14453,"data":14454},"The VAST DataSpace federates your data and computing environments to provide anywhere-access as well as global compute function routing from edge to cloud",[],{},{"nodeType":1921,"data":14456,"content":14457},{},[14458,14462,14467],{"nodeType":1907,"value":14459,"marks":14460,"data":14461},"Our DASE architecture is our advantage that creates new capabilities. Our ambition and capability leads us to invent across a number of technology categories. The result is one platform designed to do many things very well: file, object and block storage, events, EDW, vectors, runtimes, agents.\n \nVAST is not a category killer. The broad reach of VAST’s AI OS establishes us as a",[],{},{"nodeType":1907,"value":14463,"marks":14464,"data":14466}," killer of categories",[14465],{"type":12326},{},{"nodeType":1907,"value":14468,"marks":14469,"data":14470},". Never in the history of data center infrastructure has a single software stack had this much best-in-class capability. The scale and tradeoff-breaking nature of the DASE architecture makes all of this now practical. 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Are we an enterprise systems supplier?  Yes.\n\nVAST thrives everywhere where data and computing collide. Our AI-native and enterprise customers have chosen the AI OS to power over 3 million GPUs around the world. Moreover, our experience at the AI-builder frontier provides us with a unique perspective into the future of AI scaling that we bring to enterprise customer deployments. 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whisper.\na stop sign.\na prompt.\nan answer.\nan idea.\nan altogether new ",{"data":14816,"marks":14817,"value":14819,"nodeType":1907},{},[14818],{"type":12411},"discovery",{"data":14821,"marks":14822,"value":1920,"nodeType":1907},{},[],{"data":14824,"content":14825,"nodeType":1921},{},[14826],{"data":14827,"marks":14828,"value":14829,"nodeType":1907},{},[],"Every age of technical innovation is defined by an exponential curve of progress. ",{"data":14831,"content":14832,"nodeType":1921},{},[14833],{"data":14834,"marks":14835,"value":14836,"nodeType":1907},{},[],"In 2010, Apple introduced the world to the idea that a computer could listen and respond to natural language. “Hey, Siri.” In 2012, Google found cats. In 2015, DARPA closed the books on their robotics challenge, sufficiently convinced that we could build autonomous systems. In 2020, the first autonomous taxis were approved for driverless ride hailing. Today, GPTs are powering the fastest growing internet applications of all time.",{"data":14838,"content":14839,"nodeType":1921},{},[14840],{"data":14841,"marks":14842,"value":14843,"nodeType":1907},{},[],"And this is only the beginning.",{"data":14845,"content":14846,"nodeType":1921},{},[14847],{"data":14848,"marks":14849,"value":14850,"nodeType":1907},{},[],"The age of deep learning is here. This age will be defined not by digital transformation, where humans make sense of data we’ve stored in machines. 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AI is combining with unstructured and data-rich streams of data to learn and they are starting to reason about the natural world.",{"data":14852,"content":14853,"nodeType":1921},{},[14854],{"data":14855,"marks":14856,"value":14857,"nodeType":1907},{},[],"As with every age of technology, deep learning is creating new value.",{"data":14859,"content":14860,"nodeType":12397},{},[14861,14871,14881],{"data":14862,"content":14863,"nodeType":12401},{},[14864],{"data":14865,"content":14866,"nodeType":1921},{},[14867],{"data":14868,"marks":14869,"value":14870,"nodeType":1907},{},[],"For society, deep learning will help us further automate mundane tasks and continue to improve quality of life.",{"data":14872,"content":14873,"nodeType":12401},{},[14874],{"data":14875,"content":14876,"nodeType":1921},{},[14877],{"data":14878,"marks":14879,"value":14880,"nodeType":1907},{},[],"As machines become more capable, they will also be able to reason about the natural world and create new discoveries in the areas of math, science and even the arts. AI-discovered advancements for clean energy, food abundance and human health are on the immediate horizon.",{"data":14882,"content":14883,"nodeType":12401},{},[14884],{"data":14885,"content":14886,"nodeType":1921},{},[14887],{"data":14888,"marks":14889,"value":14890,"nodeType":1907},{},[],"From a business perspective, generative AI and deep learning have the potential to raise global GDP by as much as 7% (or $7T) according to Goldman Sachs**",{"data":14892,"content":14893,"nodeType":1921},{},[14894],{"data":14895,"marks":14896,"value":14897,"nodeType":1907},{},[],"Deep learning is also a technological shift that will create new winners and losers across the business landscape. Ford, IBM, Dell, Google, Amazon – these companies all became market leaders on the back of a major technological shift that contributed to the obsolescence of legacy market players. By delivering on the promise of automation and superintelligence, deep learning is a catalyst that can redefine the business landscape arguably more than any preceding technology wave.",{"data":14899,"content":14900,"nodeType":1921},{},[14901],{"data":14902,"marks":14903,"value":14904,"nodeType":1907},{},[],"Today, VAST Data announced that it has nearly tripled its valuation over the last two years and is now worth $9.1 billion. We are proud to add new growth-stage and cross-over investors such as Fidelity Management & Research Company, BOND Capital, NEA and Drive Capital to this amazing story VAST is writing.",{"data":14906,"content":14907,"nodeType":12397},{},[14908,14918],{"data":14909,"content":14910,"nodeType":12401},{},[14911],{"data":14912,"content":14913,"nodeType":1921},{},[14914],{"data":14915,"marks":14916,"value":14917,"nodeType":1907},{},[],"For those of you who are already familiar with our story, know that we have been continuing our track record of operating on a cash-flow positive basis, so this funding is simply being used to raise awareness of VAST and our mission.",{"data":14919,"content":14920,"nodeType":12401},{},[14921],{"data":14922,"content":14923,"nodeType":1921},{},[14924],{"data":14925,"marks":14926,"value":14927,"nodeType":1907},{},[],"For those of you who are new to our story, allow me to break it down for you…",{"data":14929,"content":14930,"nodeType":12263},{},[14931],{"data":14932,"marks":14933,"value":14934,"nodeType":1907},{},[],"Who is VAST Data?",{"data":14936,"content":14937,"nodeType":1921},{},[14938],{"data":14939,"marks":14940,"value":14941,"nodeType":1907},{},[],"VAST is a team of doers. A team determined to challenge the status quo and bring first-principles thinking to problem solving. At over 700 people strong, VAST is a global force that is taking its mission to all reaches of the world.",{"data":14943,"content":14944,"nodeType":1921},{},[14945],{"data":14946,"marks":14947,"value":14948,"nodeType":1907},{},[],"Our product mission is to build a data platform for the age of deep learning. The VAST Data Platform integrates unstructured data management services with a structured data environment and a compute runtime that is intended on refining unstructured data into query-able, actionable information. If the superpower of Deep Learning is the transformation of unstructured data into structured data, the VAST Data Platform is the software-based real-time engine that sits between AI applications and the HW layer that drives modern computing and storage.",{"data":14950,"content":14968,"nodeType":12292},{"target":14951},{"metadata":14952,"sys":14955,"fields":14962},{"tags":14953,"concepts":14954},[],[],{"space":14956,"id":14958,"type":934,"createdAt":14959,"updatedAt":14959,"environment":14960,"publishedVersion":847,"revision":1136,"locale":673},{"sys":14957},{"type":657,"linkType":658,"id":659},"7Idbtq6awWg6Kanavwv663","2023-12-05T23:43:28.597Z",{"sys":14961},{"id":665,"type":657,"linkType":666},{"title":14767,"description":14767,"file":14963},{"url":14964,"details":14965,"fileName":14803,"contentType":14804},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7Idbtq6awWg6Kanavwv663\u002F8dfb98217bdb8380a1190aa8185c787b\u002Fa-new-value.gif",{"size":14966,"image":14967},825835,{"width":8338,"height":8339},[],{"data":14970,"content":14971,"nodeType":1921},{},[14972,14976,14982],{"data":14973,"marks":14974,"value":14975,"nodeType":1907},{},[],"In 2019, we announced the first distributed systems architecture concept in the market since the advent of the Google File System (which Google introduced to the world in 2003). This new concept, which we call ",{"data":14977,"content":14978,"nodeType":1916},{"uri":7111},[14979],{"data":14980,"marks":14981,"value":14376,"nodeType":1907},{},[],{"data":14983,"marks":14984,"value":14985,"nodeType":1907},{},[],", introduces radical levels of scale, simplicity, affordability and enterprise data management for distributed computing. While the first application of this system was to serve files and objects to unstructured data applications, we’re on a journey to build a system that continuously computes on data at hyperscale across a collection of customer compute environments that extends from edge to cloud.",{"data":14987,"content":14988,"nodeType":12263},{},[14989],{"data":14990,"marks":14991,"value":14992,"nodeType":1907},{},[],"Why are we at the center of today’s AI action?",{"data":14994,"content":14995,"nodeType":1921},{},[14996],{"data":14997,"marks":14998,"value":14999,"nodeType":1907},{},[],"Data is the fuel of AI.\nHigh quality data is the foundation for the most successful AI models.",{"data":15001,"content":15002,"nodeType":1921},{},[15003],{"data":15004,"marks":15005,"value":15006,"nodeType":1907},{},[],"Our strategy has been to build enterprise data infrastructure that is not only used in the context of model training and inference, but we’re also building data infrastructure which can simplify and scale data access for every application. Customers today use us as the basis of their enterprise application containerization efforts, for enterprise file management, for archives, for high performance computing and more. To date, VAST has been entrusted by customers all over the world with over 10 exabytes of their critical enterprise data. As an enterprise systems’ provider, the VAST thesis has been to modernize every aspect of data and database storage so customers can easily “bring AI to their data” vs “bring their data to some complicated AI data platform”. We’ve now seen this story play out time and time again – with customers such as Disney, Zoom, NASA, the US Department of Energy and more. Our valuation announcement today also serves as testament to the fact that this strategy is working.",{"data":15008,"content":15009,"nodeType":1921},{},[15010],{"data":15011,"marks":15012,"value":15013,"nodeType":1907},{},[],"Put into context, we see a massive opportunity to unlock access to the world’s vast reserves of unstructured data, which (until now) has been static and trapped dormant in old school archives. If structured data represents roughly 5% of the world’s stored data, our belief is that the emergence of deep learning into the enterprise represents an opportunity to grow the enterprise big data opportunity by 20x.",{"data":15015,"content":15034,"nodeType":12292},{"target":15016},{"metadata":15017,"sys":15020,"fields":15027},{"tags":15018,"concepts":15019},[],[],{"space":15021,"id":15023,"type":934,"createdAt":15024,"updatedAt":15024,"environment":15025,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15022},{"type":657,"linkType":658,"id":659},"6lhM3vNzS2fbvQGeQoFdKV","2023-12-05T21:08:31.875Z",{"sys":15026},{"id":665,"type":657,"linkType":666},{"title":14767,"description":14767,"file":15028},{"url":15029,"details":15030,"fileName":15033,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6lhM3vNzS2fbvQGeQoFdKV\u002F77c4e663e660f71a5c6f40ad2c76393c\u002Fa-new-value-1.webp",{"size":15031,"image":15032},57032,{"width":1144,"height":1145},"a-new-value-1.webp",[],{"data":15036,"content":15037,"nodeType":1921},{},[15038],{"data":15039,"marks":15040,"value":15041,"nodeType":1907},{},[],"In business terms, we’ve built one of the fastest-growing technology companies in history. Our focus on storing and protecting the world’s data does not just extend to conventional enterprise customers, but we also build service provider readiness directly into the VAST Data Platform. The combination of hyperscale performance and zero-trust multi-tenancy is why VAST is also being trusted to power today’s new emerging AI clouds.",{"data":15043,"content":15044,"nodeType":1921},{},[15045],{"data":15046,"marks":15047,"value":15048,"nodeType":1907},{},[],"In context, we see a few select companies emerging that are redefining hyperscale in real-time. When surveying the list of funding announcements this year, only 5 companies (of those valued over $5B) have emerged from 2023 with greater than a 100% valuation increase. Interestingly, these are all aligned to the new era of deep learning and represent a new stack we see emerging as a representation of the new value AI is creating in the market.",{"data":15050,"content":15071,"nodeType":12292},{"target":15051},{"metadata":15052,"sys":15055,"fields":15063},{"tags":15053,"concepts":15054},[],[],{"space":15056,"id":15058,"type":934,"createdAt":15059,"updatedAt":15060,"environment":15061,"publishedVersion":811,"revision":901,"locale":673},{"sys":15057},{"type":657,"linkType":658,"id":659},"4jX9zQdoFFPpgiB0t8bMG1","2023-12-05T21:11:34.653Z","2023-12-06T00:47:55.315Z",{"sys":15062},{"id":665,"type":657,"linkType":666},{"title":14767,"description":14767,"file":15064},{"url":15065,"details":15066,"fileName":15070,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4jX9zQdoFFPpgiB0t8bMG1\u002F581be6caa451635478c3764ffa5c3964\u002FAI_Stack_Valuation_-_BLOG.webp",{"size":15067,"image":15068},196136,{"width":15069,"height":15069},1338,"AI Stack Valuation - BLOG.webp",[],{"data":15073,"content":15074,"nodeType":12263},{},[15075],{"data":15076,"marks":15077,"value":15078,"nodeType":1907},{},[],"How is this unique?",{"data":15080,"content":15081,"nodeType":1921},{},[15082],{"data":15083,"marks":15084,"value":15085,"nodeType":1907},{},[],"As mentioned in the title of this blog, we see a new era of value creation that is unfolding in real-time in the market. Yesterday’s big data and machine learning companies are not capable of capturing the new unstructured, big data opportunity. Moreover, as VAST is working with data that does not naturally conform to tables or BI systems, we work in the world’s largest GPU environments.",{"data":15087,"content":15107,"nodeType":12292},{"target":15088},{"metadata":15089,"sys":15092,"fields":15099},{"tags":15090,"concepts":15091},[],[],{"space":15093,"id":15095,"type":934,"createdAt":15096,"updatedAt":15096,"environment":15097,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15094},{"type":657,"linkType":658,"id":659},"cw5Lx7nZcLCQejfBYgVQl","2023-12-05T21:17:10.523Z",{"sys":15098},{"id":665,"type":657,"linkType":666},{"title":14767,"description":14767,"file":15100},{"url":15101,"details":15102,"fileName":15106,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002Fcw5Lx7nZcLCQejfBYgVQl\u002F969db3a08639d679efa91a07eae7d663\u002Fa-new-value-3.webp",{"size":15103,"image":15104},56154,{"width":1144,"height":15105},997,"a-new-value-3.webp",[],{"data":15109,"content":15110,"nodeType":1921},{},[15111],{"data":15112,"marks":15113,"value":15114,"nodeType":1907},{},[],"Interest rates are still very high, and investors are making bets extremely selectively. At a time when some of the world’s most successful data companies trade at a discount vs their previous highs, the market is recognizing that deep learning, GPUs and the data platforms that feed these GPUs are fundamentally changing the relationship between customers, clouds and their data. There’s no surprise that our valuation increase is highly correlated to the world’s leading AI companies, as we’re both seeing massive adoption for data-intensive AI applications across many common customers and AI clouds. On the application side, we also see private company LLM and GenAI application builders also enjoy the same valuation increase.",{"data":15116,"content":15117,"nodeType":12263},{},[15118],{"data":15119,"marks":15120,"value":15121,"nodeType":1907},{},[],"What’s next?",{"data":15123,"content":15124,"nodeType":1921},{},[15125],{"data":15126,"marks":15127,"value":15128,"nodeType":1907},{},[],"As mentioned above – VAST operates its business on a cash-flow positive basis. We’ve managed to build a company which can triple its business annually while not burning through mountains of venture capital. This, in and of itself, is extremely rare. VAST is an independent organization that has built a self-sustaining business powered by innovation, fanatical customer support and an unprecedented AI-driven market opportunity. Without any need for the new capital, this new Series E funding will sit in the bank and collect interest alongside the funding we received from Series B, Series C and Series D.",{"data":15130,"content":15131,"nodeType":1921},{},[15132],{"data":15133,"marks":15134,"value":15135,"nodeType":1907},{},[],"Having said that, there’s still so much to do and we continue to invest as we grow. Zettabytes of data are ripe for refinement, and our unyielding focus is to build out the features and the team behind the VAST Data Platform so that every customer around the world can enjoy its benefits. Over the next twelve months you’ll see a lot more focus on cloud, zero-trust, the AI computational layer and you will see us build out a community of customers and developers who organize around the idea that a new platform concept can breathe life into (new and) old data.",{"data":15137,"content":15138,"nodeType":1921},{},[15139],{"data":15140,"marks":15141,"value":15142,"nodeType":1907},{},[],"As I think about where we are today, I must recognize the effort of the global VAST team and their tireless pursuit of our shared mission. Customer value can’t be created only by the formation of an idea. Successful ideas become successful products in the market when smart and determined teams work hard to transform these dreams into reality. The VAST team is both inspiring to observe and humbling to work with. The future of AI-powered discovery is what drives us, and I’m so looking forward to reading the next chapter of this great story we’re writing.",{"data":15144,"content":15145,"nodeType":1922},{},[15146],{"data":15147,"content":15148,"nodeType":1921},{},[15149,15153,15162],{"data":15150,"marks":15151,"value":15152,"nodeType":1907},{},[],"* also growing on an exponential curve!\n** Goldman Sachs ",{"data":15154,"content":15156,"nodeType":1916},{"uri":15155},"https:\u002F\u002Fwww.goldmansachs.com\u002Fintelligence\u002Fpages\u002Fgenerative-ai-could-raise-global-gdp-by-7-percent.html",[15157],{"data":15158,"marks":15159,"value":15161,"nodeType":1907},{},[15160],{"type":12237},"GenAI 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Some other options I considered for today’s ",{"data":15403,"content":15405,"nodeType":1916},{"uri":15404},"https:\u002F\u002Fvastdata.com\u002Fpress-releases\u002Fvast-unveils-vast-data-platform",[15406],{"data":15407,"marks":15408,"value":15410,"nodeType":1907},{},[15409],{"type":12237},"momentous announcement",{"data":15412,"marks":15413,"value":15414,"nodeType":1907},{},[],"... “The Master Plan, Part Un & Deux”, “Breaking Tradeoffs: Part II”. Having said that, given how many conversations I’ve had over the past few days about collapsing the IT stack for AI, my mind keeps wandering back to universal constructs and the power of stack unification. Get ready for words, this blog is a doozy.",{"data":15416,"content":15417,"nodeType":1921},{},[15418],{"data":15419,"marks":15420,"value":15421,"nodeType":1907},{},[],"\u003Cclears throat, adopts Ted-talk speaking style>",{"data":15423,"content":15424,"nodeType":1921},{},[15425],{"data":15426,"marks":15427,"value":15428,"nodeType":1907},{},[],"All the things.",{"data":15430,"content":15431,"nodeType":1921},{},[15432,15436,15441],{"data":15433,"marks":15434,"value":15435,"nodeType":1907},{},[],"This is the aim of a data platform... to be all the things for data-driven applications (compute services, data services and storage services). Today, we introduced the world to a revolutionary system concept that makes AI computing radically easier, more scalable and more efficient than ever before: ",{"data":15437,"marks":15438,"value":15440,"nodeType":1907},{},[15439],{"type":12411},"The VAST Data Platform",{"data":15442,"marks":15443,"value":15444,"nodeType":1907},{},[],". This is an idea conceived in late 2015 and purpose-built for the age of AI; it’s a concept we then built into a company that was founded the same month and year that OpenAI was founded. ",{"data":15446,"content":15447,"nodeType":1921},{},[15448],{"data":15449,"marks":15450,"value":15451,"nodeType":1907},{},[],"We are VAST Data, and this is the story we’ve wanted to tell you since December of 2015.",{"data":15453,"content":15454,"nodeType":1921},{},[15455],{"data":15456,"marks":15457,"value":15458,"nodeType":1907},{},[],"The idea is relatively simple.",{"data":15460,"content":15461,"nodeType":1921},{},[15462],{"data":15463,"marks":15464,"value":15466,"nodeType":1907},{},[15465],{"type":12326},"Let’s build a global computing and storage platform that organizes data from the natural world, a machine (in software) that enriches and applies structure to unstructured data to give AI applications the tools needed to derive greater insights from data.",{"data":15468,"content":15469,"nodeType":1921},{},[15470],{"data":15471,"marks":15472,"value":15473,"nodeType":1907},{},[],"To truly build such a system, a system engineered for continuous global inference and learning, we needed to throw convention out the window and start from a blank sheet of paper. In doing so, the VAST Data Platform has been conceived as a single monolithic data-driven system that seeks to break so many of the tradeoffs that have led to decades of compromise and point solutions.",{"data":15475,"content":15476,"nodeType":1921},{},[15477],{"data":15478,"marks":15479,"value":15480,"nodeType":1907},{},[],"In this blog, I’ll attempt to articulate the infrastructure tradeoffs that we’ve been working to eliminate now that we are able to unveil our entire Data Platform concept. Keeping with the theme of universal constructs, let’s talk about gravitational collapses.",{"data":15482,"content":15483,"nodeType":12263},{},[15484],{"data":15485,"marks":15486,"value":15488,"nodeType":1907},{},[15487],{"type":12411},"Collapsing Storage Tiers",{"data":15490,"content":15491,"nodeType":1921},{},[15492],{"data":15493,"marks":15494,"value":15495,"nodeType":1907},{},[],"When we started, we realized that deep learning is essentially applied statistics… an approach to computing where vast datasets can fuel learning engines by giving the best representation of the natural world. Flash is an essential component for deep learning, for this reason there is no HDD-based solution that Nvidia allows, for example, for their SuperPOD environments. ",{"data":15497,"content":15498,"nodeType":1921},{},[15499],{"data":15500,"marks":15501,"value":15502,"nodeType":1907},{},[],"When we started VAST, the cost of legacy all-flash infrastructure was 10x the cost of HDD-based alternatives. No one was thinking of flash for the capacity tier. On the other hand, if it would be possible to build archives from flash, there would be no need for any faster storage tiers and it would be possible to just compute right from the archive. At the time, there were many new storage startups working on making all-flash even faster, VAST was the only company making all-flash practically affordable.",{"data":15504,"content":15505,"nodeType":1921},{},[15506,15510,15514],{"data":15507,"marks":15508,"value":15509,"nodeType":1907},{},[],"How do we do it? By combining a new approach to data reduction with an architecture that embraces the idea of using low-cost, laptop-grade flash we’ve built a new approach to data storage infrastructure that is now becoming the file and object foundation of the modern data center… the ",{"data":15511,"marks":15512,"value":3249,"nodeType":1907},{},[15513],{"type":12411},{"data":15515,"marks":15516,"value":15517,"nodeType":1907},{},[],". Formerly referred to as Universal Storage, the efficiency characteristics of this distributed file and object storage system are married with the scale and resilience of the VAST DASE architecture to make it possible to build a single tier of affordable exabyte-scale flash within the data center. When your archive is built from flash, then infrastructure no longer fights against the random read challenges introduced by deep learning and deep data analysis workloads.",{"data":15519,"content":15520,"nodeType":1921},{},[15521],{"data":15522,"marks":15523,"value":15525,"nodeType":1907},{},[15524],{"type":12411},"One tier.",{"data":15527,"content":15549,"nodeType":12292},{"target":15528},{"metadata":15529,"sys":15532,"fields":15539},{"tags":15530,"concepts":15531},[],[],{"space":15533,"id":15535,"type":934,"createdAt":15536,"updatedAt":15536,"environment":15537,"publishedVersion":692,"revision":1136,"locale":673},{"sys":15534},{"type":657,"linkType":658,"id":659},"31iCcf47yjbA7G5E9NuSP2","2023-08-01T16:40:56.157Z",{"sys":15538},{"id":665,"type":657,"linkType":666},{"title":15540,"description":2031,"file":15541},"Diagram 1",{"url":15542,"details":15543,"fileName":15548,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F31iCcf47yjbA7G5E9NuSP2\u002Fd951eefe76cccfb0438a19e4daf2777d\u002Fdiagram_1.webp",{"size":15544,"image":15545},31730,{"width":15546,"height":15547},2005,1030,"diagram 1.webp",[],{"data":15551,"content":15552,"nodeType":12263},{},[15553],{"data":15554,"marks":15555,"value":15557,"nodeType":1907},{},[15556],{"type":12411},"Collapsing Database Tiers",{"data":15559,"content":15560,"nodeType":1921},{},[15561],{"data":15562,"marks":15563,"value":15564,"nodeType":1907},{},[],"The VAST Data Platform offering has taken the market by storm. After selling 10 exabytes of software to power a wide variety of enterprise and research applications, it’s become one of the fastest growing products in IT infrastructure history. In 2019, we were both proud and disappointed that IDC declared that the product was “the storage architecture of the future”... proud because of the prestigious distinction, but disappointed because we knew it could be so much more and we didn’t want the market to make too many preconceptions about who were were to become. ",{"data":15566,"content":15567,"nodeType":1921},{},[15568],{"data":15569,"marks":15570,"value":15571,"nodeType":1907},{},[],"Deep Learning pipelines require a variety of data management tools. Unstructured data is the foundation for storing data that comes from the natural world, and the power of AI is to understand the context of files that have no schema in order to create structure from unstructured data. While file systems are critically important (it’s the only required data system in a SuperPOD, for example), databases are equally important. Unstructured is the raw content, and structure becomes the corresponding that comes from data labeling, data prep and AI inference. ",{"data":15573,"content":15574,"nodeType":1921},{},[15575],{"data":15576,"marks":15577,"value":15578,"nodeType":1907},{},[],"Now - to build a system that can constantly learn, we realized that database services were as critical as unstructured data services and that we needed to challenge fundamental design assumptions of database management systems (DBMSs) in order to create a system that could ingest multi-variant data streams in real-time while also providing the foundation for instantaneous structured queries that could correlate data all the way to the archive. Given the scale we operate at, the concept of a transactional and analytical system needed to be reimagined from the ground up.",{"data":15580,"content":15581,"nodeType":1921},{},[15582],{"data":15583,"marks":15584,"value":15585,"nodeType":1907},{},[],"How do we do it? First, we now offer a tabular format within the system. Tables are simply a new data format presented by the VAST Data Element Store (our storage namespace). This table store can be accessed by a version of SQL we’ve developed (VAST SQL) and also works with other modern query engines such as Apache Spark and Trino. Second, we’ve built a new database architecture that writes and updates small records into a large persistent memory buffer (storage class memory) in record form, and then migrates records into fine-grained columnar database objects that are radically smaller than a classic Parquet or Orc row group. Granularity equals query precision and speed. Since we’ve built a 32KB columnar object that is 4,000x smaller than a conventional table partition, our approach results in the ability to service filtered queries up to 100x faster than legacy data lake approaches. Oh, ACID updates are also easy since our architecture is log-structured (writes in free space) and since it’s easy to update 32KB columnar objects vs re-writing large and versioned Parquet objects. No more database vacuuming, no more worries about updates. ",{"data":15587,"content":15588,"nodeType":1921},{},[15589,15593,15598,15602,15606,15610,15615],{"data":15590,"marks":15591,"value":15592,"nodeType":1907},{},[],"Let’s jump into a time machine and go back to the early ‘70s, the time before data warehouses. If the database systems of the 1960’s and 1970’s were able to transact ",{"data":15594,"marks":15595,"value":15597,"nodeType":1907},{},[15596],{"type":12411},"and ",{"data":15599,"marks":15600,"value":15601,"nodeType":1907},{},[],"scale their data ",{"data":15603,"marks":15604,"value":14162,"nodeType":1907},{},[15605],{"type":12411},{"data":15607,"marks":15608,"value":15609,"nodeType":1907},{},[]," could efficiently handle queries, would technologies such as data warehouses and data lakes ever be needed? Likely not. This is the place we are aiming to get back to by calling our approach to storing and analyzing tabular data ",{"data":15611,"marks":15612,"value":15614,"nodeType":1907},{},[15613],{"type":12411},"the VAST DataBase",{"data":15616,"marks":15617,"value":15618,"nodeType":1907},{},[],". The VAST DataBase is the world’s first transactional system that marries ACID transactional semantics that scale across an nearly-infinite number of tables while enabling the system to query datasets that extend from real-time streams all the way to exabyte scale archives. ",{"data":15620,"content":15621,"nodeType":1921},{},[15622],{"data":15623,"marks":15624,"value":15626,"nodeType":1907},{},[15625],{"type":12411},"One tier.\nOne database.",{"data":15628,"content":15648,"nodeType":12292},{"target":15629},{"metadata":15630,"sys":15633,"fields":15640},{"tags":15631,"concepts":15632},[],[],{"space":15634,"id":15636,"type":934,"createdAt":15637,"updatedAt":15637,"environment":15638,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15635},{"type":657,"linkType":658,"id":659},"40w5Cr5UjfS0nfK1N6fuSH","2023-08-01T16:41:30.643Z",{"sys":15639},{"id":665,"type":657,"linkType":666},{"title":15641,"description":2031,"file":15642},"Diagram 2",{"url":15643,"details":15644,"fileName":15647,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F40w5Cr5UjfS0nfK1N6fuSH\u002Fd7691e7b32c545c77905e952c23976fd\u002Fdiagram_2.webp",{"size":15645,"image":15646},37570,{"width":15546,"height":15547},"diagram 2.webp",[],{"data":15650,"content":15651,"nodeType":12263},{},[15652],{"data":15653,"marks":15654,"value":15656,"nodeType":1907},{},[15655],{"type":12411},"Collapsing Files & Tables",{"data":15658,"content":15659,"nodeType":1921},{},[15660],{"data":15661,"marks":15662,"value":15663,"nodeType":1907},{},[],"For decades, databases have always been independent from the file and object storage systems they’ve been deployed to catalog. Yet, content does not exist in a vacuum, and context has always been managed from some other system of record because no product was ever built to bring together structured and unstructured data into one unified system that could transact, read, analyze and store data all the way to exabyte proportions. Until now.",{"data":15665,"content":15666,"nodeType":1921},{},[15667],{"data":15668,"marks":15669,"value":15670,"nodeType":1907},{},[],"One of the major unifications that happens with the VAST Data Platform is the synthesis of content and context. At a high level, we think of most database services to be extensions of our data catalog concept.",{"data":15672,"content":15673,"nodeType":12397},{},[15674,15684],{"data":15675,"content":15676,"nodeType":12401},{},[15677],{"data":15678,"content":15679,"nodeType":1921},{},[15680],{"data":15681,"marks":15682,"value":15683,"nodeType":1907},{},[],"A social networking site, for example, can store their photos as objects in the VAST DataStore, serve data globally with the VAST DataSpace and keep the enriched metadata in VAST DataBase. In the past, they’d need to build an object storage system, combine this with a CDN and then deploy a series of NoSQL databases to keep track of objects. Now: simple.",{"data":15685,"content":15686,"nodeType":12401},{},[15687],{"data":15688,"content":15689,"nodeType":1921},{},[15690],{"data":15691,"marks":15692,"value":15693,"nodeType":1907},{},[],"A deep learning pipeline requires a series of databases to capture data labels and to build training example indices that are used for managing training pipelines. These systems sit alongside object-based storage archives and parallel scratch file systems for AI scratchpads. Now the DataStore delivers both high-capacity and high performance file access (collapsing tiers) and the DataBase provides the semantic layer for training data that eliminates the need for SQL engines and HDD-based data lakes. The Platform’s Cataloging services ensure the database is never out of sync with the content store. Now: simple.",{"data":15695,"content":15696,"nodeType":1921},{},[15697],{"data":15698,"marks":15699,"value":15700,"nodeType":1907},{},[],"There have been several famous attempts to evolve databases into unstructured databases (my term), and none have succeeded. However, by planning for a unified data management architecture that scales and blurs the lines between transactional analytics, we believe that smart architecture can make the impossible possible.",{"data":15702,"content":15703,"nodeType":1921},{},[15704],{"data":15705,"marks":15706,"value":15708,"nodeType":1907},{},[15707],{"type":12411},"One tier.\nOne database.\nOne dataset.",{"data":15710,"content":15730,"nodeType":12292},{"target":15711},{"metadata":15712,"sys":15715,"fields":15722},{"tags":15713,"concepts":15714},[],[],{"space":15716,"id":15718,"type":934,"createdAt":15719,"updatedAt":15719,"environment":15720,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15717},{"type":657,"linkType":658,"id":659},"4qSJKjwTTqK8EEBRAwxq6","2023-08-01T16:42:01.279Z",{"sys":15721},{"id":665,"type":657,"linkType":666},{"title":15723,"description":2031,"file":15724},"Diagram 3",{"url":15725,"details":15726,"fileName":15729,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F4qSJKjwTTqK8EEBRAwxq6\u002F68b2a23b7b3a92e13ab85e1a1eea4c76\u002Fdiagram_3.webp",{"size":15727,"image":15728},41398,{"width":15546,"height":15547},"diagram 3.webp",[],{"data":15732,"content":15733,"nodeType":12263},{},[15734],{"data":15735,"marks":15736,"value":15738,"nodeType":1907},{},[15737],{"type":12411},"Collapsing Namespaces",{"data":15740,"content":15741,"nodeType":1921},{},[15742],{"data":15743,"marks":15744,"value":15745,"nodeType":1907},{},[],"Next, as we thought about deep learning we thought about dataset size and gravity. ",{"data":15747,"content":15748,"nodeType":1921},{},[15749],{"data":15750,"marks":15751,"value":15752,"nodeType":1907},{},[],"Modern deep learning applications learn from data that comes from the natural world. Computer vision (sight), natural language processing (sound), large language models (free text) all combine to create datasets that are so large that customers cannot easily move them across different data centers. How large? VAST works with organizations who have single clusters that reach into the 100PBs scale, and they are now working with us to build toward exabyte-scale. These massive payloads hold the keys to the future by providing the insights that systems can access to learn and infer from data.",{"data":15754,"content":15755,"nodeType":1921},{},[15756],{"data":15757,"marks":15758,"value":15759,"nodeType":1907},{},[],"It’s impossible to easily move petabytes or exabytes of data when you want to ensure global access across a multitude of data centers. Global data replication systems create copies in every data center, which is not necessarily practical when dealing with this level of scale. Generally a single replica copy is sufficient to ensure application availability, and any additional copies are superfluous. On the other hand, cloud-based file and database management tools are more suited to providing global data access by applying caching techniques to make read operations fast at the edge of the network. Reads are easy to handle since the different edges of the network don’t need to worry about consistency or transaction management. Today’s CDN services can have 1,000s of data centers that accelerate reads at the edge - that problem is well solved.",{"data":15761,"content":15762,"nodeType":1921},{},[15763],{"data":15764,"marks":15765,"value":15766,"nodeType":1907},{},[],"The challenge we thought about was the challenge of global transactional consistency enforcement. If you build a federation of systems that all provide the ability to access a global corpus of data, any site that transacts into this data needs to be coordinated across other sites that also want a coherent view of that data. Historically, transactions that happen across a global data namespace need to be synchronized across any number of locations and need to be managed and enforced by some central data owner that is responsible for arbitrating transactions across a global network. While this approach of central transaction management ensures strict consistency, this comes at the cost of transactional performance - since every site then needs to write at the speed of the wide area network as they check their data across the WAN into a remote data owner. While this might work for document management, our conclusion was that a global collection of AI computers could not afford to write at WAN speeds.",{"data":15768,"content":15769,"nodeType":1921},{},[15770,15774,15779],{"data":15771,"marks":15772,"value":15773,"nodeType":1907},{},[],"In an effort to break the tradeoff between strict consistency and high-performance read-write access at the edge of the network, we’ve pioneered a new approach to global data access that we call the ",{"data":15775,"marks":15776,"value":15778,"nodeType":1907},{},[15777],{"type":12411},"VAST DataSpace.",{"data":15780,"marks":15781,"value":15782,"nodeType":1907},{},[]," How do we do it? ",{"data":15784,"content":15785,"nodeType":12397},{},[15786,15796,15806],{"data":15787,"content":15788,"nodeType":12401},{},[15789],{"data":15790,"content":15791,"nodeType":1921},{},[15792],{"data":15793,"marks":15794,"value":15795,"nodeType":1907},{},[],"First, VAST clusters can peer to any other VAST cluster to create a data sharing relationship across global networks. These systems will subscribe to each other’s data with a selective level of granularity, taking the peering down to the path-level (directory-level, bucket-level, table-level). The system provides the ability to create either a syncing relationship between sites or to let any site take a global, writeable snapshot clone of a path in case ",{"data":15797,"content":15798,"nodeType":12401},{},[15799],{"data":15800,"content":15801,"nodeType":1921},{},[15802],{"data":15803,"marks":15804,"value":15805,"nodeType":1907},{},[],"Each site then caches a consistent view of a path’s top-level metadata - such that any site can consistently search through the namespace by starting from a strictly-consistent point. Additional buffering happens for read operations using local caching, global prefetch and tools to pre-warm cache for applications that will require low time-to-first-byte.",{"data":15807,"content":15808,"nodeType":12401},{},[15809],{"data":15810,"content":15811,"nodeType":1921},{},[15812],{"data":15813,"marks":15814,"value":15815,"nodeType":1907},{},[],"Finally, we’ve implemented a new approach to consistency management that eliminates the challenges of global consistency management by rethinking where lock enforcement happens. Inspired by some developments in the web3 space, we’ve invented a new decentralized lock manager that allows transaction state management to flow across a network of clusters and be enforced where the state is changing. This can only be done by bringing lock management all the way to the file, object & table level… and with this decentralized and fine-grained approach to lock management, all of the sites can write as they please while, in the random case of a write collision, we can still enforce strict levels of global consistency. ",{"data":15817,"content":15838,"nodeType":12292},{"target":15818},{"metadata":15819,"sys":15822,"fields":15829},{"tags":15820,"concepts":15821},[],[],{"space":15823,"id":15825,"type":934,"createdAt":15826,"updatedAt":15826,"environment":15827,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15824},{"type":657,"linkType":658,"id":659},"3Cn3OnQOuyY40ygZRFEAUt","2023-08-01T16:43:13.285Z",{"sys":15828},{"id":665,"type":657,"linkType":666},{"title":15830,"description":2031,"file":15831},"Diagram 4",{"url":15832,"details":15833,"fileName":15837,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3Cn3OnQOuyY40ygZRFEAUt\u002Fd9b2b345804d0dd3d1e6f732cf5244e3\u002Fdiagram_4.webp",{"size":15834,"image":15835},98288,{"width":15546,"height":15836},1029,"diagram 4.webp",[],{"data":15840,"content":15841,"nodeType":1921},{},[15842],{"data":15843,"marks":15844,"value":15845,"nodeType":1907},{},[],"When we bring it all together, users just get fast access to data from any point on the network.",{"data":15847,"content":15848,"nodeType":1921},{},[15849],{"data":15850,"marks":15851,"value":15853,"nodeType":1907},{},[15852],{"type":12411},"One tier. \nOne database.\nOne dataset.\nOne namespace.",{"data":15855,"content":15856,"nodeType":12263},{},[15857],{"data":15858,"marks":15859,"value":15861,"nodeType":1907},{},[15860],{"type":12411},"Collapsing Clouds",{"data":15863,"content":15864,"nodeType":1921},{},[15865],{"data":15866,"marks":15867,"value":15868,"nodeType":1907},{},[],"Clouding isn’t easy. Every customer we talk with about “doing cloud right” spends much time talking about replatforming applications and going cloud native. Cloud strategies either fail to launch or fail to be agile when customers run up against two laws of gravitation:",{"data":15870,"content":15871,"nodeType":12397},{},[15872,15882],{"data":15873,"content":15874,"nodeType":12401},{},[15875],{"data":15876,"content":15877,"nodeType":1921},{},[15878],{"data":15879,"marks":15880,"value":15881,"nodeType":1907},{},[],"Data has gravity, which is compounded by egress toll gates imposed by cloud vendors",{"data":15883,"content":15884,"nodeType":12401},{},[15885],{"data":15886,"content":15887,"nodeType":1921},{},[15888],{"data":15889,"marks":15890,"value":15891,"nodeType":1907},{},[],"APIs create gravity, by requiring applications to support to platform-specific APIs",{"data":15893,"content":15894,"nodeType":1921},{},[15895,15899,15904],{"data":15896,"marks":15897,"value":15898,"nodeType":1907},{},[],"When we started building ",{"data":15900,"marks":15901,"value":15903,"nodeType":1907},{},[15902],{"type":12411},"the VAST DataSpace",{"data":15905,"marks":15906,"value":15907,"nodeType":1907},{},[],", one of the aims of this effort was to also allow customers to extend namespaces across different public cloud platforms. How do we do it? Today we’re announcing support for AWS, Azure and Google - I’m sure we’ll add others as customers express interest in other cloud environments.",{"data":15909,"content":15910,"nodeType":1921},{},[15911],{"data":15912,"marks":15913,"value":15914,"nodeType":1907},{},[],"One of the leading cloud vendors once asked me why we’re building support for their platform when they already have different point solutions that (in total) compete to varying degrees with our offering. The answer was easy: choice and fit.",{"data":15916,"content":15917,"nodeType":1921},{},[15918],{"data":15919,"marks":15920,"value":15921,"nodeType":1907},{},[],"By adding support for leading public clouds, the DataSpace creates a platform abstraction where applications no longer fight against API or data gravity. You can just run your applications using a common set of data APIs on the platform you choose, whenever you choose. No refactoring. No lock-in. ",{"data":15923,"content":15924,"nodeType":1921},{},[15925],{"data":15926,"marks":15927,"value":15928,"nodeType":1907},{},[],"Now - as for “fit”. What we’re also finding is that many of the toolsets offered in leading cloud platforms (unstructured data systems, structured data platforms) were almost all built in an era that pre-dates deep learning, and they often lean on hard drive-based constructs as well as other architectural decisions that result in all of the tradeoffs VAST is trying to break. So with today’s announcement we’re also happy to be able to talk with customers about running AI applications in cloud against data infrastructure that is fit for purpose.",{"data":15930,"content":15931,"nodeType":1921},{},[15932],{"data":15933,"marks":15934,"value":15936,"nodeType":1907},{},[15935],{"type":12411},"One tier.\nOne database.\nOne dataset. \nOne namespace. \nOne platform.",{"data":15938,"content":15959,"nodeType":12292},{"target":15939},{"metadata":15940,"sys":15943,"fields":15950},{"tags":15941,"concepts":15942},[],[],{"space":15944,"id":15946,"type":934,"createdAt":15947,"updatedAt":15947,"environment":15948,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15945},{"type":657,"linkType":658,"id":659},"2eTaM2xYkTOlCfyoBnsqpX","2023-08-01T16:44:55.796Z",{"sys":15949},{"id":665,"type":657,"linkType":666},{"title":15951,"description":2031,"file":15952},"Diagram 6",{"url":15953,"details":15954,"fileName":15958,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F2eTaM2xYkTOlCfyoBnsqpX\u002F39cc8ae4bf135fe9f37ca10e0d31c38d\u002Fdiagram_6.webp",{"size":15955,"image":15956},51996,{"width":15546,"height":15957},978,"diagram 6.webp",[],{"data":15961,"content":15981,"nodeType":12292},{"target":15962},{"metadata":15963,"sys":15966,"fields":15973},{"tags":15964,"concepts":15965},[],[],{"space":15967,"id":15969,"type":934,"createdAt":15970,"updatedAt":15970,"environment":15971,"publishedVersion":847,"revision":1136,"locale":673},{"sys":15968},{"type":657,"linkType":658,"id":659},"8w4mklXsCF06Py4SHlPUv","2023-08-01T16:44:00.702Z",{"sys":15972},{"id":665,"type":657,"linkType":666},{"title":15974,"description":2031,"file":15975},"Diagram 5",{"url":15976,"details":15977,"fileName":15980,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F8w4mklXsCF06Py4SHlPUv\u002F8b527a286ee673419d7fe59bdd0a4856\u002Fdiagram_5.webp",{"size":15978,"image":15979},59210,{"width":15546,"height":15957},"diagram 5.webp",[],{"data":15983,"content":15984,"nodeType":12263},{},[15985],{"data":15986,"marks":15987,"value":15989,"nodeType":1907},{},[15988],{"type":12411},"Collapsing Application Definitions",{"data":15991,"content":15992,"nodeType":1921},{},[15993],{"data":15994,"marks":15995,"value":15996,"nodeType":1907},{},[],"OK, this last one can take a lot of twists and turns but I’m now in too-verbose territory so I’m going to keep it simple.",{"data":15998,"content":15999,"nodeType":1921},{},[16000],{"data":16001,"marks":16002,"value":16003,"nodeType":1907},{},[],"Since the dawn of computational data analytics (I’m going back to the late 1970s here), enterprise software architecture has largely fallen into one of two camps:",{"data":16005,"content":16006,"nodeType":12397},{},[16007,16017],{"data":16008,"content":16009,"nodeType":12401},{},[16010],{"data":16011,"content":16012,"nodeType":1921},{},[16013],{"data":16014,"marks":16015,"value":16016,"nodeType":1907},{},[],"Event-Centric Systems are largely responsible for capturing and processing data streams as they happen. Event architectures are tightly related to transaction processing and messaging systems and typically limit the ability to analyze data in a system of record. Since data lakes and data warehouses have never possessed the ability to handle scalable transactions, event architectures typically only support correlative analysis of real-time data against a limited data set that can fit in an event queue. Today, Apache Kafka is the standard for open and scalable event processing.",{"data":16018,"content":16019,"nodeType":12401},{},[16020],{"data":16021,"content":16022,"nodeType":1921},{},[16023],{"data":16024,"marks":16025,"value":16026,"nodeType":1907},{},[],"Data-Centric Systems, on the other hand, are designed to capture and harvest insights from large datasets… datasets so large that they would overwhelm a conventional database or event bus architecture. Datasets that need to be queried using tools optimized for deep data analysis (typically columnar data orientation) using data-parallel analytics methods across a wide collection of computers. Popular data-centric programming languages include SQL and R.",{"data":16028,"content":16029,"nodeType":1921},{},[16030],{"data":16031,"marks":16032,"value":16033,"nodeType":1907},{},[],"The implicit tradeoff in all of this is that applications can’t run deep correlative analysis against events that are happening in real-time, nor can deep analytics infrastructure process events in real time. The result is a dichotomy between systems that requires organizations to integrate disparate systems that all step down to their lowest common denominator while also forfeiting the opportunity to marry real-time and big data.",{"data":16035,"content":16036,"nodeType":1921},{},[16037,16041,16046,16050,16059],{"data":16038,"marks":16039,"value":16040,"nodeType":1907},{},[],"Today, we’re announcing the final element of the VAST Data Platform story, ",{"data":16042,"marks":16043,"value":16045,"nodeType":1907},{},[16044],{"type":12411},"the VAST Data Engine",{"data":16047,"marks":16048,"value":16049,"nodeType":1907},{},[],". The engine is the last piece of the ",{"data":16051,"content":16053,"nodeType":1916},{"uri":16052},"https:\u002F\u002Fvastdata.com\u002Fblog\u002Fthe-quest-to-build-thinking-machines",[16054],{"data":16055,"marks":16056,"value":16058,"nodeType":1907},{},[16057],{"type":12237},"‘thinking machine’",{"data":16060,"marks":16061,"value":16062,"nodeType":1907},{},[]," puzzle that refines raw unstructured data into insights that can be discovered by structured query language via the VAST DataBase. The VAST Engine is a computational framework that runs in containers across the VAST DataSpace. More than just a container stack, the Engine introduces support for triggering functions across a dynamically scalable set of CPU, GPU and DPU resources from edge to cloud.",{"data":16064,"content":16065,"nodeType":1921},{},[16066],{"data":16067,"marks":16068,"value":16069,"nodeType":1907},{},[],"The DataEngine will ship in 2024, and we’ll be unveiling a lot more details as the offering gets closer to general availability, but today I’d like to preview a capability that I just call VAST Streams (actual name: TBD). The Engine not only creates a functional programming environment to refine data through deep learning training and inference functions, but it also extends the type of data ingest that the system can support to also provide a Kafka connector that will allow data to be topics to be captured as real-time tables.",{"data":16071,"content":16072,"nodeType":1921},{},[16073],{"data":16074,"marks":16075,"value":16076,"nodeType":1907},{},[],"How do we do it? The DASE architecture is built with a persistent write buffer that allows a batch of messages to be written directly into a VAST table in record form. As mentioned above, a background process then converts these records into a flash-optimized columnar object format that makes it easy to run accelerated queries at any scale. Triggers then call functions as data is streamed into a topic, and event streams just become another source of data that can create a continuous loop of data enrichment and processing in the VAST Data Platform.",{"data":16078,"content":16079,"nodeType":1921},{},[16080],{"data":16081,"marks":16082,"value":16083,"nodeType":1907},{},[],"With this, we hope to break down decades-old application definitions to build systems that can interact with the natural world and create real time associations, realizations and discoveries by comparing new experiences with past learnings.",{"data":16085,"content":16086,"nodeType":1921},{},[16087],{"data":16088,"marks":16089,"value":16091,"nodeType":1907},{},[16090],{"type":12411},"One tier.\nOne database.\nOne dataset. \nOne namespace.\nOne platform.\nOne perpetual learning machine.",{"data":16093,"content":16114,"nodeType":12292},{"target":16094},{"metadata":16095,"sys":16098,"fields":16105},{"tags":16096,"concepts":16097},[],[],{"space":16099,"id":16101,"type":934,"createdAt":16102,"updatedAt":16102,"environment":16103,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16100},{"type":657,"linkType":658,"id":659},"5yexLxG0cuSGywMBWSkmKC","2023-08-01T16:46:01.447Z",{"sys":16104},{"id":665,"type":657,"linkType":666},{"title":16106,"description":2031,"file":16107},"Diagram 7",{"url":16108,"details":16109,"fileName":16113,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5yexLxG0cuSGywMBWSkmKC\u002F67aef93eb69bb77ed5dcc745e2ca3e21\u002Fdiagram_7.webp",{"size":16110,"image":16111},30534,{"width":15546,"height":16112},979,"diagram 7.webp",[],{"data":16116,"content":16117,"nodeType":1921},{},[16118],{"data":16119,"marks":16120,"value":16121,"nodeType":1907},{},[],"We are VAST and this is our vision for the future of computing.\nThis is the VAST Data Platform.",{"data":16123,"content":16124,"nodeType":1921},{},[16125],{"data":16126,"marks":16127,"value":16128,"nodeType":1907},{},[],"Thank you.\n\n- Jeff",[16130,16155,16180,16205,16230],{"metadata":16131,"sys":16134,"fields":16141},{"tags":16132,"concepts":16133},[],[],{"space":16135,"id":12559,"type":14,"createdAt":12560,"updatedAt":12561,"environment":16137,"publishedVersion":12564,"revision":12565,"contentType":16139,"locale":673},{"sys":16136},{"type":657,"linkType":658,"id":659},{"sys":16138},{"id":665,"type":657,"linkType":666},{"sys":16140},{"type":657,"linkType":671,"id":922},{"entryTitle":942,"title":942,"url":12569,"image":16142},{"metadata":16143,"sys":16146,"fields":16151},{"tags":16144,"concepts":16145},[],[],{"space":16147,"id":12577,"type":934,"createdAt":12578,"updatedAt":12579,"environment":16149,"publishedVersion":12582,"revision":5986,"locale":673},{"sys":16148},{"type":657,"linkType":658,"id":659},{"sys":16150},{"id":665,"type":657,"linkType":666},{"title":677,"description":677,"file":16152},{"url":12585,"details":16153,"fileName":12590,"contentType":949},{"size":12587,"image":16154},{"width":12589,"height":12589},{"metadata":16156,"sys":16159,"fields":16166},{"tags":16157,"concepts":16158},[],[],{"space":16160,"id":12598,"type":14,"createdAt":12599,"updatedAt":12600,"environment":16162,"publishedVersion":12603,"revision":1727,"contentType":16164,"locale":673},{"sys":16161},{"type":657,"linkType":658,"id":659},{"sys":16163},{"id":665,"type":657,"linkType":666},{"sys":16165},{"type":657,"linkType":671,"id":922},{"entryTitle":697,"title":697,"image":16167},{"metadata":16168,"sys":16171,"fields":16176},{"tags":16169,"concepts":16170},[],[],{"space":16172,"id":12614,"type":934,"createdAt":12615,"updatedAt":12616,"environment":16174,"publishedVersion":12619,"revision":12620,"locale":673},{"sys":16173},{"type":657,"linkType":658,"id":659},{"sys":16175},{"id":665,"type":657,"linkType":666},{"title":697,"description":697,"file":16177},{"url":12623,"details":16178,"fileName":12627,"contentType":949},{"size":12625,"image":16179},{"width":12589,"height":12589},{"metadata":16181,"sys":16184,"fields":16191},{"tags":16182,"concepts":16183},[],[],{"space":16185,"id":12635,"type":14,"createdAt":12636,"updatedAt":12637,"environment":16187,"publishedVersion":12619,"revision":12620,"contentType":16189,"locale":673},{"sys":16186},{"type":657,"linkType":658,"id":659},{"sys":16188},{"id":665,"type":657,"linkType":666},{"sys":16190},{"type":657,"linkType":671,"id":922},{"entryTitle":12643,"title":12643,"url":12644,"image":16192},{"metadata":16193,"sys":16196,"fields":16201},{"tags":16194,"concepts":16195},[],[],{"space":16197,"id":12652,"type":934,"createdAt":12653,"updatedAt":12654,"environment":16199,"publishedVersion":7093,"revision":12620,"locale":673},{"sys":16198},{"type":657,"linkType":658,"id":659},{"sys":16200},{"id":665,"type":657,"linkType":666},{"title":12643,"description":12643,"file":16202},{"url":12659,"details":16203,"fileName":12664,"contentType":949},{"size":12661,"image":16204},{"width":12589,"height":12663},{"metadata":16206,"sys":16209,"fields":16216},{"tags":16207,"concepts":16208},[],[],{"space":16210,"id":12672,"type":14,"createdAt":12673,"updatedAt":12674,"environment":16212,"publishedVersion":12619,"revision":12620,"contentType":16214,"locale":673},{"sys":16211},{"type":657,"linkType":658,"id":659},{"sys":16213},{"id":665,"type":657,"linkType":666},{"sys":16215},{"type":657,"linkType":671,"id":922},{"entryTitle":797,"title":797,"url":777,"image":16217},{"metadata":16218,"sys":16221,"fields":16226},{"tags":16219,"concepts":16220},[],[],{"space":16222,"id":12687,"type":934,"createdAt":12688,"updatedAt":12689,"environment":16224,"publishedVersion":1727,"revision":12620,"locale":673},{"sys":16223},{"type":657,"linkType":658,"id":659},{"sys":16225},{"id":665,"type":657,"linkType":666},{"title":797,"description":797,"file":16227},{"url":12694,"details":16228,"fileName":12698,"contentType":949},{"size":12696,"image":16229},{"width":12589,"height":12589},{"metadata":16231,"sys":16234,"fields":16241},{"tags":16232,"concepts":16233},[],[],{"space":16235,"id":12706,"type":14,"createdAt":12707,"updatedAt":12708,"environment":16237,"publishedVersion":1727,"revision":12711,"contentType":16239,"locale":673},{"sys":16236},{"type":657,"linkType":658,"id":659},{"sys":16238},{"id":665,"type":657,"linkType":666},{"sys":16240},{"type":657,"linkType":671,"id":922},{"entryTitle":12715,"title":12716,"image":16242},{"metadata":16243,"sys":16246,"fields":16251},{"tags":16244,"concepts":16245},[],[],{"space":16247,"id":12724,"type":934,"createdAt":12725,"updatedAt":12726,"environment":16249,"publishedVersion":12729,"revision":12730,"locale":673},{"sys":16248},{"type":657,"linkType":658,"id":659},{"sys":16250},{"id":665,"type":657,"linkType":666},{"title":657,"description":657,"file":16252},{"url":12733,"details":16253,"fileName":12737,"contentType":949},{"size":12735,"image":16254},{"width":12589,"height":12589},{"metadata":16256,"sys":16259,"fields":16269},{"tags":16257,"concepts":16258},[],[],{"space":16260,"id":16262,"type":14,"createdAt":16263,"updated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Unification Theory of AI Infrastructure","VAST co-founder Jeff Denworth introduces a revolutionary system concept that makes AI computing radically easier, more scalable and more efficient than ever before.",{"metadata":16273,"sys":16276,"fields":16283},{"tags":16274,"concepts":16275},[],[],{"space":16277,"id":16279,"type":934,"createdAt":16280,"updatedAt":16280,"environment":16281,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16278},{"type":657,"linkType":658,"id":659},"1emTF9P88b9AS6UKtvuggw","2023-08-31T00:47:25.173Z",{"sys":16282},{"id":665,"type":657,"linkType":666},{"title":9463,"description":9463,"file":16284},{"url":16285,"details":16286,"fileName":11596,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1emTF9P88b9AS6UKtvuggw\u002F2c682cb8eab65f494a3f8cc766de3b01\u002Fvast-data-platform.jpg",{"size":16287,"image":16288},66932,{"width":1457,"height":1458},{"metadata":16290,"sys":16312,"fields":16323},{"tags":16291,"concepts":16311},[16292,16294,16296,16298,16301,16303,16305,16308],{"sys":16293},{"type":657,"linkType":1502,"id":15330},{"sys":16295},{"type":657,"linkType":1502,"id":12744},{"sys":16297},{"type":657,"linkType":1502,"id":14748},{"sys":16299},{"type":657,"linkType":1502,"id":16300},"partnerNvidia",{"sys":16302},{"type":657,"linkType":1502,"id":4364},{"sys":16304},{"type":657,"linkType":1502,"id":1081},{"sys":16306},{"type":657,"linkType":1502,"id":16307},"productInsightEngine",{"sys":16309},{"type":657,"linkType":1502,"id":16310},"useCaseRag",[],{"space":16313,"id":16315,"type":14,"createdAt":16316,"updatedAt":16317,"environment":16318,"publishedVersion":16320,"revision":2654,"contentType":16321,"locale":673},{"sys":16314},{"type":657,"linkType":658,"id":659},"2EqExcUpYpfKHCarpmHzUh","2024-09-27T08:06:07.536Z","2025-03-17T19:52:08.656Z",{"sys":16319},{"id":665,"type":657,"linkType":666},287,{"sys":16322},{"type":657,"linkType":671,"id":12790},{"title":16324,"slug":16325,"publishedDate":16326,"author":14018,"thumbnailImage":16327,"markdownContent":16359,"socialIcons":16360,"hidden":19,"seo":16486},"The End of the Unstructured Data Era","the-end-of-the-unstructured-data-era","2024-10-01",{"metadata":16328,"sys":16331,"fields":16340},{"tags":16329,"concepts":16330},[],[],{"space":16332,"id":16334,"type":14,"createdAt":16335,"updatedAt":16335,"environment":16336,"publishedVersion":901,"revision":1136,"contentType":16338,"locale":673},{"sys":16333},{"type":657,"linkType":658,"id":659},"p8OE8vmOGlxIcoHaRxicV","2024-09-27T14:34:50.129Z",{"sys":16337},{"id":665,"type":657,"linkType":666},{"sys":16339},{"type":657,"linkType":671,"id":12176},{"entryTitle":16324,"image":16341},{"metadata":16342,"sys":16345,"fields":16352},{"tags":16343,"concepts":16344},[],[],{"space":16346,"id":16348,"type":934,"createdAt":16349,"updatedAt":16349,"environment":16350,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16347},{"type":657,"linkType":658,"id":659},"7LDEXwQwNDs38afwZzh2sP","2024-09-27T14:34:46.516Z",{"sys":16351},{"id":665,"type":657,"linkType":666},{"title":16324,"description":16324,"file":16353},{"url":16354,"details":16355,"fileName":16358,"contentType":1147},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7LDEXwQwNDs38afwZzh2sP\u002F5c0ec01fb525fae5fffb955e5f969a07\u002Fthe-end-of-the-unstructured-data-era.webp",{"size":16356,"image":16357},78530,{"width":1144,"height":1145},"the-end-of-the-unstructured-data-era.webp","Retrieval Augmented Generation. RAG.\n\nI’m going to attempt to push past the marketing speak and focus on a few key areas that I think are really special about what we’re doing in the space of information retrieval for AI applications. Due to the fact that there are several __fundamental__ innovations that change the definition of how enterprises will define terms such as “real time” and “enterprise-ready,” this post is a relatively long one. Buckle up.\n\nTable of Contents:\n- [Introduction to Retrieval-Augmented Generation (RAG) for AI Applications](#Introduction)\n- [Today’s Challenges with RAG](#TodaysChallengesWithRag)\n- [The VAST Data InsightEngine with NVIDIA](#InsightEngine)\n- [The End of the Unstructured Data Era](#TheEndOfTheUnstructuredDataEra)\n\n\u003Ca id=\"Introduction\">\u003C\u002Fa>In August of 2020, a team at Facebook first presented a paper on [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.11401). This paper introduced an idea of how to augment the information retrieval of an AI model with additional data that a model could query and retrieve from external data sources. Four years later, the market has now developed the tools and ecosystem required to establish RAG as the ‘killer app’ that can usher in the value of AI to enterprises looking to embrace faster knowledge-sharing and decision-making.\n\n\u003Ca id=\"TodaysChallengesWithRag\">\u003C\u002Fa>So what is the challenge that enterprise RAG-based systems are attempting to solve?\n\nAs organizations start to bring AI agents into their environment, it’s clear that there are things that an agent can and does know, and there are things trained AI models can’t yet accomplish. Some challenges include:\n\n- AI models are trained and fine-tuned periodically, so they may not be equipped with the latest and greatest information needed to power a business in real time.\n\n- Generative AI models rely on the accuracy of generative predictions - and they can hallucinate when they rely solely on patterns within their training dataset.\n\n- Models are also bound by the information they’ve been trained on, and optimized for the amount of memory needed in the machines you can deploy AI agents on. Therefore, AI teams will often look to minimize the size of the model and augment AI applications with external data sources (versus doing much much larger training and fine-tuning work and then fitting inference models into expensive, memory-rich machines).\n\n- Finally, AI models are not typically designed to enforce enterprise access control or permissions. Therefore, it can be impractical to train them on large enterprise datasets that have a wide variety of access permissions, since users cannot be given unfettered access to the information these models gain. Conversely, it’s inefficient to train a model per enterprise user on just the data they are permitted to see. Even if you could, permissions change more frequently than models do.\n\n\u003Cp style=\"text-wrap: pretty\">Fortunately, AI retrieval provides the answer to the above problems and paves the way for general enterprise adoption of agentic AI applications. A generalized workflow looks as follows:\u003C\u002Fp>\n\u003Cp style=\"margin-left: 2.25rem; text-indent: -2.25rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">1.\u003C\u002Fspan> An AI inference model, typically an LLM, will receive a request\u002Fprompt from some user\u003C\u002Fp>\n\u003Cp style=\"margin-left: 2.25rem; text-indent: -2.25rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">2.\u003C\u002Fspan> The model will attempt to answer the question itself, if possible, and then resort to going to external data sources when the response’s confidence score is not high enough\u003C\u002Fp>\n\u003Cp style=\"margin-left: 2.25rem; text-indent: -2.25rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">3.\u003C\u002Fspan> At this point, the model can retrieve external data typically from one of two sources\u003C\u002Fp>\n\u003Cp style=\"margin-left: 4rem; text-indent: -2rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">a.\u003C\u002Fspan> If the answers live inside an organization’s unstructured data, this will have been indexed by some AI embedding model and the context from enterprise file and object storage systems will be then stored and indexed in a vector database\u003C\u002Fli>\u003C\u002Fp>\n\u003Cp style=\"margin-left: 4rem; text-indent: -2rem;\">\u003Cspan style=\"color: var(--color-vastBlueDarker); font-size: 1.9rem; font-weight: 700;\">b.\u003C\u002Fspan> Alternatively, structured data that exists within enterprise data warehouses can be easily queried by an AI model using SQL\u003C\u002Fli>\u003C\u002Fp>\n\n![The End of the Unstructured Data Era](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F16Hk7EEpb2EBEdPmW8MaHY\u002Ff32d8ab5545386d401f66ae9a0d8b29a\u002Fthe-end-of-the-unstructured-data-era-1.webp)\n\nRetrieval solves for many of the key drawbacks of AI models within the enterprise.\n\n| AI Model Challenge | Retrieval Solution |\n| ---------- | ---------- |\n| Models are trained and fine-tuned periodically – their data may not be current. | Retrieval systems can source data from real-time or near-real-time sources. |\n| Models can hallucinate. | Retrieval allows AI models to not have to know everything, and to rather just serve as interpreters of queries that can source facts. |\n| Big and smart models can be expensive to deploy. | Retrieval allows for AI models to stay small while augmenting them with massive data volumes that are semantically indexed. |\n| AI models typically can’t enforce granular permissions. | Retrieval aligns the query output with individual user permissions, enabling data isolation across sensitive enterprise datasets. |\n\nOK... so, we’ve solved enterprise AI and everything’s right in the world… right? No.\nWhile there are several different approaches to retrieval in the market, none has passed our sniff test so far.\n\n__Problem #1: Real Time Is Not Really Real Time__\n\nMany vendors in the real-time RAG space talk about their ability to answer queries quickly, and don’t address that the data they’ve indexed is the product of some periodic batch process that isn’t current with what’s happening in real time in an organization.\n\nWhile datasets for retrieval can be updated more frequently than a model is fine-tuned, this process is not yet able to feed information to AI models in real time.\n- Vector embeddings are created from file data only periodically, often on a batch schedule.\n- File system ACLs are not atomic with vector databases, also synced only periodically.\n- Enterprise data warehouses (EDWs) struggle to ingest data in real time, so real-time data starts in an event bus and is difficult to query against until it is ETL’d into an EDW, which is also done as a periodic batch process.\n\nAnd all of this source data can change… data doesn’t only get expanded, data can be removed from the enterprise knowledgebase. This makes the role of retrieval systems even more complicated when they can and do expose context that is suddenly out of date or inaccurate.\n\nConsider, finally, that a human is only one type of user of an AI model. AI agents are now being combined to execute complex AI workflows and enhance each other. If a business is beginning to run at the speed of AI, it’s unthinkable that AI engines should wait minutes or days for up-to-date business data.\n\u003Cp style=\"text-wrap: pretty\">Why is building scalable + real-time systems hard? It’s the architecture (I’ll come back to this).\u003C\u002Fp>\n\n__Problem #2: Vector Database Scalability Trades Scale for Performance__\n\nAs we poll our average Fortune 500 customers, putting aside the large hyperscalers, we find customers that are sitting on 100s of petabytes of unique enterprise data. In order to appropriately index this data using AI and data vectorization tools, we’ll need systems that can store and index trillions of vector embeddings and be able to search on them in real time (in order to preserve a quality user experience). This is not the design point that the early vectorDB efforts were built around - as they rushed to get out solutions for chatbots that front-ended small datasets such as PDF document stores.\n\nSure, people today may say… ‘I only have to curate a small number of documents and feed them into a vector DB, since the embedding models require hand-holding’. At VAST, we’re not worrying about the state of AI-based data indexing as it is today. We’re watching the exponential improvements in embedding models and seeing a time in the not-too-distant future where these tools will be of a caliber that they can be used to index the entirety of an enterprise knowledge base and even help to curate enterprise data. At that point, hyper-scale vector stores and search tools are table stakes.  \n\nThe trillion-way vector problem is already a reality for large AI builders like OpenAI and Perplexity that have gone and indexed the internet. This is the scale of product we want to build… for everyone.\n\nFinally, you won’t just need to read from vector search engines in constant time — you’ll also need to be able to create, store and index embeddings in real time. At this point, we find ourselves back in the place we were with problem #1… architecture is a core limiter for being able to scale datasets and transacting into them with high performance when technologies lean on the classic shared-nothing systems designs.\n\n__Problem #3: It’s All Just Kinda Messy__\n\nRAG pipelines involve many steps:\n- Set up a system alongside your primary file and object storage systems that can support AI-based indexing, since legacy file systems don’t handle AI parallel processing well. If you want to use a cloud-based data platform, just copy all of your enterprise file systems into some cloud storage bucket.\n- Then build some GPU cluster to periodically scan and create these embeddings, with all of the workflow orchestration and gluecode to make this automatic.\n- Then build a vector database that can store and index these embeddings, as well as help build knowledge graphs. Stamp these out into silos of information insight as you hit scaling walls.\n- Remember, to save money, you should also tier your embeddings off to some slow object storage with the intent of never having to query against that data.\n- Then build an enterprise data warehouse to support your Text-to-SQL queries.\n- But to do this, you’ll also need to build an event bus that captures real-time event streams since the data warehouse is not transactional, hoping you don’t need to correlate the two at the same time.\n- Then try your best to establish unified policy enforcement across a bunch of independent systems and hope that permissions don’t fall too far out of sync that anyone notices.\n- And finally, spend the next three years trying to tune this infrastructure so that you can get as close to real time as possible so that your business isn’t continually getting served old or stale data.\n\n![Before InsightEngine](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1fCkPWGVWwoy5qS9iDfv94\u002F8b202d4f288fdded1941f7a04bfc3047\u002Fbefore.gif)\n\n##### AI Retrieval Problems: The Big Takeaway\n\nWith respect to these pipelines… I believe the world has been doing the wrong thing.\n\nThe enterprise just wants more intelligence from file and object storage. I think of vectors and knowledge graphs as just higher forms of file system metadata… why wouldn’t we want this to run natively from within the file system if it was possible? This would solve the problems of data and metadata atomicity and make it easy for AI agents to make data conversational, talking to storage almost as a new protocol. In order for this to happen, a file system would need support for real-time transactional and analytical metadata analytics, a job engine, event triggers, etc.  Well, that’s a data platform.\n\nAnd why wouldn’t we want real-time transactional data warehouses?  \nOf course we do. It’s just not been possible… until now.\n\n![VAST InsightEngine With NVIDIA](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5w9s4lVrMgDog3W8v5YDiK\u002F9fc7d0fa5ffb5970fbb1fd6f1ba57ede\u002Fvast-insightengine-nvidia.webp)\n\n\u003Ca id=\"InsightEngine\">\u003C\u002Fa>\n#### Introducing the InsightEngine\n\nThis is why, today, we’re delighted to take the covers of our collaboration with an AI leader and introduce the VAST Data InsightEngine with NVIDIA.\n\nVAST InsightEngine with NVIDIA is an all-encompassing system to support AI Retrieval that allows organizations to ingest all of their structured and unstructured data streams in real time; to index them using AI embedding tools in real time; and to serve answers to agentic applications with real-time search tools that work at any scale, and with the enterprise security that comes naturally from building intelligence into an enterprise data foundation. It’s fast, scalable, secure and simple.\n\nVAST InsightEngine with NVIDIA is designed to run [NVIDIA NIM](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fai\u002F) microservices, part of the [NVIDIA AI Enterprise](https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fproducts\u002Fai-enterprise\u002F) software platform, natively from within the VAST Data Platform, leveraging VAST containers that support NVIDIA accelerated computing for AI acceleration. Inside the system, real-time workflows are built into the [VAST DataEngine](..\u002F..\u002Fplatform\u002Fdataengine#) that leverage native application triggers that automatically create embeddings on data and update vector indexes as new unstructured data comes into the system. To start, we’ll support NVIDIA NeMo Retriever NIM microservices that create embeddings from text and video, and others will be added in the future.\n\nNow, it’s one thing to create a separate storage space for all of your AI Retrieval data… this, to us, is crazy. This is why, from the earliest days, we’ve been working on building an enterprise scale-out NAS and object storage system (among other things) that is Universal in nature. We don’t want customers to take their enterprise data to some GPUs — we want them to bring GPUs to their enterprise data that is on AI-ready infrastructure… so that intelligent applications can benefit from getting the broadest access to all of an organization’s unstructured data.\n\n![VAST DASE NIMS NVIDIA](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F5hSGdxw2J6ToA4BrpJzICc\u002F42ac135f7f0e6c9701af690b922e8c24\u002FVAST-DASE-NVIDIA-NIMS.webp)\n\nTo enable semantic search of this unstructured data that lives in the [VAST DataStore](..\u002F..\u002Fplatform\u002Fdatastore#), we’re proud to announce that the [VAST DataBase](..\u002F..\u002Fblog\u002Fblurring-the-lines-between-event-driven-and-data-driven-architectures) has been extended to support a new data type: vector embeddings. Thanks to the scalability of the [VAST DASE architecture](..\u002F..\u002Fwhitepaper\u002F#TheDisaggregatedSharedEverythingArchitecture), we can now build table stores that support trillions of vectors, making it possible to index an entire enterprise. But scale is only one challenge — we also needed to make these data structures easy to update and query in real time.\n\nAs many of you know, VAST has been working with [Similarity algorithms](..\u002F..\u002Fblog\u002Fbreaking-data-reduction-trade-offs-with-global-compression) for almost 10 years now (to optimize data reduction and make flash as affordable as HDD storage), and it turns out that these same algorithms are now very popular to help with searching large vector spaces. The VAST DASE architecture pioneers new ideas around transactional parallelism and provides all of the system’s containers the ability to share a common, real-time view of a namespace organized across low-cost, low-latency flash. The result is a system that makes it possible to transact millions of events per second and to update and search indices in milliseconds.\n\n![VAST DASE 2024](\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F6A8CeAOEvcOUIsnoRt9sOr\u002F629992ade5df231d4e6166499c442cc8\u002Fvast-dase.webp)\n\nSecurity-wise, the platform will pull permissions and policy data up into the VAST DataBase, such that every vector search happens against permissions data that is atomically synced with the data source. No more do you need to sync independent permissions systems. Permissions are enforced and updated in real time thanks to the transactional nature of the VAST DataBase and unified data management.\n\nFinally, for SQL-to-Text operations, we’re also proud to announce that the VAST DataBase now also supports a Kafka-compatible event broker. This is a seemingly small footnote on an otherwise very long blog… but the effects of this are going to be profound across the data science community as we take the world to a place where you no longer need an external event bus to capture enterprise event streams. Now, you can stream directly into VAST DataBase tables, where event data is then instantly part of a (predominantly) columnar query.\n\n\u003Ca id=\"TheEndOfTheUnstructuredDataEra\">\u003C\u002Fa>\n#### The End of the Unstructured Data Era\n\nLooking into the future, AI will become increasingly embedded into core business applications. File systems and object storage, alone, force IT teams to build cumbersome retrieval pipelines and wrestle with the complexity of stale data, stale permissions and a lot of integration and gluecode headaches. Most importantly, these pipelines are not working as fast as AI engines enable us to. The idea of a standalone file system is fading as new business priorities need more from data infrastructure.\n\nAt VAST, we want to store, index and serve the world’s data to tomorrow’s engines of discovery.  To get there, our simple mission is to eliminate infrastructure constraints, bottlenecks and tradeoffs. This mission forces us to think further up the stack, and to challenge the conceptions of how systems should be built to realize the greatest gains. VAST InsightEngine with NVIDIA is the culmination of what we’ve been working toward for almost a decade and underscores the need for unstructured data to be treated as a first-class citizen in the age of Enterprise AI. But unstructured data, alone, is not enough.\n\n\u003Cvideo autoplay loop muted poster=\"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1giyUEcaPo9m0SvSxM5YxY\u002Ff46de3ba18a833d5233f975f3e9c21bf\u002FInsight-Engine-Gif_comp.webp\" style=\"border-radius: 20px\">\n  \u003Csource src=\"\u002F\u002Fvideos.ctfassets.net\u002F2f3meiv6rg5s\u002F2lJKo9dNuBR8xdkMuRZA4U\u002F85a470ba8f1bd5bcef811f0852c0baf9\u002FInsight-Engine-Gif_comp.mp4\" type=\"video\u002Fmp4\" \u002F>\n\u003C\u002Fvideo>\n\nFiles, objects, tables, triggers… and now AI workflows that run natively in our Engine.\nWe look forward to sharing more as we get closer to product launch in early ’25.\n\nThanks for reading.\n\nJeff\n",[16361,16386,16411,16436,16461],{"metadata":16362,"sys":16365,"fields":16372},{"tags":16363,"concepts":16364},[],[],{"space":16366,"id":12559,"type":14,"createdAt":12560,"updatedAt":12561,"environment":16368,"publishedVersion":12564,"revision":12565,"contentType":16370,"locale":673},{"sys":16367},{"type":657,"linkType":658,"id":659},{"sys":16369},{"id":665,"type":657,"linkType":666},{"sys":16371},{"type":657,"linkType":671,"id":922},{"entryTitle":942,"title":942,"url":12569,"image":16373},{"metadata":16374,"sys":16377,"fields":16382},{"tags":16375,"concepts":16376},[],[],{"space":16378,"id":12577,"type":934,"createdAt":12578,"updatedAt":12579,"environment":16380,"publishedVersion":12582,"revision":5986,"locale":673},{"sys":16379},{"type":657,"linkType":658,"id":659},{"sys":16381},{"id":665,"type":657,"linkType":666},{"title":677,"description":677,"file":16383},{"url":12585,"details":16384,"fileName":12590,"contentType":949},{"size":12587,"image":16385},{"width":12589,"height":12589},{"metadata":16387,"sys":16390,"fields":16397},{"tags":16388,"concepts":16389},[],[],{"space":16391,"id":12598,"type":14,"createdAt":12599,"updatedAt":12600,"environment":16393,"publishedVersion":12603,"revision":1727,"contentType":16395,"locale":673},{"sys":16392},{"type":657,"linkType":658,"id":659},{"sys":16394},{"id":665,"type":657,"linkType":666},{"sys":16396},{"type":657,"linkType":671,"id":922},{"entryTitle":697,"title":697,"image":16398},{"metadata":16399,"sys":16402,"fields":16407},{"tags":16400,"concepts":16401},[],[],{"space":16403,"id":12614,"type":934,"createdAt":12615,"updatedAt":12616,"environment":16405,"publishedVersion":12619,"revision":12620,"locale":673},{"sys":16404},{"type":657,"linkType":658,"id":659},{"sys":16406},{"id":665,"type":657,"linkType":666},{"title":697,"description":697,"file":16408},{"url":12623,"details":16409,"fileName":12627,"contentType":949},{"size":12625,"image":16410},{"width":12589,"height":12589},{"metadata":16412,"sys":16415,"fields":16422},{"tags":16413,"concepts":16414},[],[],{"space":16416,"id":12635,"type":14,"createdAt":12636,"updatedAt":12637,"environment":16418,"publishedVersion":12619,"revision":12620,"contentType":16420,"locale":673},{"sys":16417},{"type":657,"linkType":658,"id":659},{"sys":16419},{"id":665,"type":657,"linkType":666},{"sys":16421},{"type":657,"linkType":671,"id":922},{"entryTitle":12643,"title":12643,"url":12644,"image":16423},{"metadata":16424,"sys":16427,"fields":16432},{"tags":16425,"concepts":16426},[],[],{"space":16428,"id":12652,"type":934,"createdAt":12653,"updatedAt":12654,"environment":16430,"publishedVersion":7093,"revision":12620,"locale":673},{"sys":16429},{"type":657,"linkType":658,"id":659},{"sys":16431},{"id":665,"type":657,"linkType":666},{"title":12643,"description":12643,"file":16433},{"url":12659,"details":16434,"fileName":12664,"contentType":949},{"size":12661,"image":16435},{"width":12589,"height":12663},{"metadata":16437,"sys":16440,"fields":16447},{"tags":16438,"concepts":16439},[],[],{"space":16441,"id":12672,"type":14,"createdAt":12673,"updatedAt":12674,"environment":16443,"publishedVersion":12619,"revision":12620,"contentType":16445,"locale":673},{"sys":16442},{"type":657,"linkType":658,"id":659},{"sys":16444},{"id":665,"type":657,"linkType":666},{"sys":16446},{"type":657,"linkType":671,"id":922},{"entryTitle":797,"title":797,"url":777,"image":16448},{"metadata":16449,"sys":16452,"fields":16457},{"tags":16450,"concepts":16451},[],[],{"space":16453,"id":12687,"type":934,"createdAt":12688,"updatedAt":12689,"environment":16455,"publishedVersion":1727,"revision":12620,"locale":673},{"sys":16454},{"type":657,"linkType":658,"id":659},{"sys":16456},{"id":665,"type":657,"linkType":666},{"title":797,"description":797,"file":16458},{"url":12694,"details":16459,"fileName":12698,"contentType":949},{"size":12696,"image":16460},{"width":12589,"height":12589},{"metadata":16462,"sys":16465,"fields":16472},{"tags":16463,"concepts":16464},[],[],{"space":16466,"id":12706,"type":14,"createdAt":12707,"updatedAt":12708,"environment":16468,"publishedVersion":1727,"revision":12711,"contentType":16470,"locale":673},{"sys":16467},{"type":657,"linkType":658,"id":659},{"sys":16469},{"id":665,"type":657,"linkType":666},{"sys":16471},{"type":657,"linkType":671,"id":922},{"entryTitle":12715,"title":12716,"image":16473},{"metadata":16474,"sys":16477,"fields":16482},{"tags":16475,"concepts":16476},[],[],{"space":16478,"id":12724,"type":934,"createdAt":12725,"updatedAt":12726,"environment":16480,"publishedVersion":12729,"revision":12730,"locale":673},{"sys":16479},{"type":657,"linkType":658,"id":659},{"sys":16481},{"id":665,"type":657,"linkType":666},{"title":657,"description":657,"file":16483},{"url":12733,"details":16484,"fileName":12737,"contentType":949},{"size":12735,"image":16485},{"width":12589,"height":12589},{"metadata":16487,"sys":16490,"fields":16500},{"tags":16488,"concepts":16489},[],[],{"space":16491,"id":16493,"type":14,"createdAt":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Learn how real-time data retrieval and scalable vector databases enable faster, smarter decisions in the RAG era.",{"metadata":16503,"sys":16506,"fields":16513},{"tags":16504,"concepts":16505},[],[],{"space":16507,"id":16509,"type":934,"createdAt":16510,"updatedAt":16510,"environment":16511,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16508},{"type":657,"linkType":658,"id":659},"2zCkVvH5DeSYQOUgTKaPZb","2024-09-27T14:35:50.199Z",{"sys":16512},{"id":665,"type":657,"linkType":666},{"title":16324,"description":16324,"file":16514},{"url":16515,"details":16516,"fileName":16519,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F2zCkVvH5DeSYQOUgTKaPZb\u002F53ba2d70d5cd55babab846383f985870\u002Fthe-end-of-the-unstructured-data-era.jpg",{"size":16517,"image":16518},66107,{"width":1457,"height":1458},"the-end-of-the-unstructured-data-era.jpg",{"metadata":16521,"sys":16524,"fields":16533},{"tags":16522,"concepts":16523},[],[],{"space":16525,"id":16527,"type":14,"createdAt":16528,"updatedAt":16528,"environment":16529,"publishedVersion":940,"revision":1136,"contentType":16531,"locale":673},{"sys":16526},{"type":657,"linkType":658,"id":659},"3w1u8UA6SKSLTP5IBZNDez","2026-04-21T16:53:47.004Z",{"sys":16530},{"id":665,"type":657,"linkType":666},{"sys":16532},{"type":657,"linkType":671,"id":1437},{"title":14015,"description":16534,"image":16535,"autoGenerateSocialImage":19,"generateTargetImageUsingAi":19,"noindex":19},"VAST Data hits a $30B valuation with Series F funding. Learn how our business model and AI Operating System are redefining the $100T AI infrastructure market.",{"metadata":16536,"sys":16539,"fields":16546},{"tags":16537,"concepts":16538},[],[],{"space":16540,"id":16542,"type":934,"createdAt":16543,"updatedAt":16543,"environment":16544,"publishedVersion":847,"revision":1136,"locale":673},{"sys":16541},{"type":657,"linkType":658,"id":659},"3CWHV7aqI3AKs9DZUMaOlv","2026-04-21T16:53:43.063Z",{"sys":16545},{"id":665,"type":657,"linkType":666},{"title":14015,"description":14015,"file":16547},{"url":16548,"details":16549,"fileName":16552,"contentType":1460},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F3CWHV7aqI3AKs9DZUMaOlv\u002F989129361364a6cfd9899991ff927199\u002FSocial_Series-F-Blog_VAST-Data__1_.jpg",{"size":16550,"image":16551},66005,{"width":1457,"height":1458},"Social_Series-F-Blog_VAST-Data (1).jpg",{"metadata":16554,"sys":16557,"fields":16566},{"tags":16555,"concepts":16556},[],[],{"space":16558,"id":16560,"type":14,"createdAt":16561,"updatedAt":16561,"environment":16562,"publishedVersion":847,"revision":1136,"contentType":16564,"locale":673},{"sys":16559},{"type":657,"linkType":658,"id":659},"2ogh8HO2LSWiLvsx2cl8Hh","2026-06-16T17:51:57.108Z",{"sys":16563},{"id":665,"type":657,"linkType":666},{"sys":16565},{"type":657,"linkType":671,"id":1437},{"title":16567,"description":16568,"image":16569,"autoGenerateSocialImage":19,"generateTargetImageUsingAi":19,"noindex":19},"AI Didn't Create Data Gravity. 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