VAST Data

Hyperfusion’s Sentinel container integrates the VAST AI OS to deliver sovereign, confidential AI inference wherever power exists 🚀

“The very best defense is informed by the best offense.” For Daniel Bernard and Bartley Richardson mounting that defense shouldn't require moving mission-critical enterprise data outside customer control. In this video, they break down CrowdStrike's partnership with VAST Data around the new DataEnclave confidential computing runtime. Detailing why executing AI-native security models and autonomous agents directly on customer-controlled infrastructure, right where sensitive data already lives, is essential for modern threat prevention 🚀

Sovereignty fails the moment secondary AI artifacts cross jurisdictional boundaries. In a new white paper, Sharon AI, Inc and VAST Data outline how to embed policy enforcement directly into the data layer, from flash to vectors.

Formula 1 US Grand Prix at Circuit of The Americas is almost here! VAST Data is hosting an evening at the Atlassian Williams F1 Team Fan Zone, presented by Kraken, on Saturday October 24 and would love to see some familiar faces from the local tech community. Food, drinks and Austin energy, right in the heart of downtown Join VAST After Hours from 8pm–10:30pm. No race ticket needed. Share this with a colleague or friend in tech who’d enjoy coming along 🏎️ 🏎️

Escalating observability costs and search bottlenecks shouldn’t be the price of scaling Splunk. In part one of a five-part series, VAST Data's Bryan Schramm introduces Infinite Cache for Splunk SmartStore: A unified Block and S3 architecture that reduces storage requirements by at least 70%, increases search performance, and eliminates the partial search results common in high-availability deployments. Watch Episode 1 to see how it operates across bare-metal, virtualized, and Kubernetes environments 🎬

After a busy day at GTC Berlin, step aboard, unwind, and connect 🚢 🇩🇪 Escape the crowded conference floor and join as VAST Data cruises the AI frontier during an exclusive evening along the river Spree. Relax with drinks, light bites, and great conversation and toast to the future of AI. Plus take in Berlin’s iconic skyline at sunset! Reserve Your Spot on the Deck Today: https://lnkd.in/gQgasJ_n

Confidence earned in production 🚀 Hyundai Motor Company's investment follows years of production deployment across their autonomous driving and robotics programs. Through 42dot, its autonomous mobility division and global software center, HKMC replaced fragmented data infrastructure by standardizing ADAS research and LLM development on the VAST AI OS. Having proven the architecture's scale, efficiency, and reliability across petabytes of vehicle fleet data, HKMC's transition from customer to strategic investor underscores the role of the VAST AI OS as the data foundation for physical AI and intelligent mobility.

Scaling the early internet meant moving static content closer to users. Scaling production AI requires moving intelligence. While model training drove the creation of centralized AI factories, real-time inference introduces an entirely different set of operational constraints. Routing split-second decisions back to distant GPU clusters introduces unacceptable latency, pushing compute and models toward a distributed AI grid. In the latest episode of the {SharedEverything} podcast, Akamai Technologies’ Jon Alexander and Ari Weil join host Nicole Hemsoth Prickett to unpack why distributed AI is fundamentally a data engineering problem. The discussion examines how VAST’s global namespace abstracts regional complexity, handling caching, placement, and replication underneath so distributed applications can query live data without creating isolated data silos. ➡️ Swipe through for this episode's highlights and stream the full conversation via the link in the comments.

New episode alert... Akamai was solving distributed infrastructure problems long before anyone was talking about AI inference. Nearly three decades later, those same architectural principles are taking on a whole new significance. In this episode of the Shared Everything podcast I talk with Akamai’s John Alexander and Ari Weil about what happens when AI inference moves beyond centralized GPU clusters and into a distributed AI grid. We dig into the hard engineering problems: where workloads run, how CPUs and GPUs are orchestrated, what latency and data gravity mean for placement, and why getting data to compute (or compute to data) is becoming one of AI’s biggest infrastructure challenges. And as they mention, a global data namespace paired with Akamai’s distributed compute infrastructure offers a compelling approach to making inference work across locations without creating a mess of regional data silos. Listen+read here... https://lnkd.in/eGHinFp7