MinIO

Enterprise AI isn’t limited by models or compute. It’s limited by how data is organized, accessed, and governed. AIStor Tables unifies tables and objects in a single, high-performance, object-native data store, giving analytics and AI systems direct access to enterprise data. Now generally available. https://bit.ly/45HTjdQ

How should AI agents consume external data? AI and ML solutions engineer Keith Pijanowski and other experts have the answers on when to scrape website data for AI agents or when to integrate data for AI agents through APIs. Read more. https://bit.ly/3Mb6Kwb

NVIDIA continues to redefine accelerated computing with GPUDirect® RDMA for S3-compatible storage, eliminating unnecessary data movement across the stack. https://bit.ly/3Ol9QOL MinIO AIStor builds directly on this innovation, providing high-performance S3 semantics with RDMA acceleration. This work is a natural evolution for the GPUDirect® family and a significant advancement for AI infrastructure.

We’re proud to be included in Forrester’s Object Storage Solutions Landscape, Q1 2026. https://bit.ly/4qwWBIk The report highlights how object storage can accelerate AI outcomes, deliver global data access, and increase resilience at scale. We see this as validation of MinIO’s vision to be the foundation for AI and data, delivering scalable, high-performance infrastructure for AI and analytics workloads.

Apache Iceberg just leveled up along with on-prem data. 📈 https://bit.ly/4r0JyzQ This isn’t a cloud workaround or a bolt-on service, it’s Iceberg done right for enterprises that run data where it lives. See what’s new with AIStor Tables. 👀 #AIStor #AI #IcebergTables #AIStorTables

The MinIO POV: AI isn’t compute-bound anymore. It’s data-starved. For the last decade, AI infrastructure was framed as a compute problem. More GPUs. Bigger clusters. Faster accelerators. That framing is now wrong. The hard limit on AI progress today isn’t how much compute you can buy –it’s how fast you can feed it. Models are ready. GPUs are everywhere. And yet, in real-world deployments, they sit idle, waiting on data. Pipelines choke. Training slows. Inference performance swings wildly depending on how stressed the storage layer happens to be. This is the quiet failure mode of modern AI: plenty of horsepower, no fuel line...

Many federal AI initiatives stall after proof of concept. Not due to model limitations but because of underlying data constraints. The Federal AI Readiness Gap report highlights how infrastructure and operational realities continue to shape AI outcomes in government. Dive into the findings with Cameron Chehreh. https://bit.ly/3Zsfdy7 #FedTech #GovSEC #GovData

MinIO helps Rakuten Mobile's AI and Data team manage mobile data. https://bit.ly/4kts5h2 This system supports a Kubernetes-based network, utilizing our high-performance, container-compatible distributed object storage with cloud-native architecture to enable scalable data handling. The results are humongous.

How do you successfully scale AI? Co-founder & CEO Anand Babu Periasamy breaks it down in Fast Company. https://bit.ly/4qwgWxk AI isn't just about choosing the latest model. It's rethinking how data is stored, moved, and made accessible. As datasets grow, data gravity intensifies—pulling compute toward data and slowing progress. Here are his 3️⃣ practical strategies that turn data gravity from a liability into a competitive advantage.

AIStor Tables delivers a single, high-performance data foundation that enables enterprise analytics and AI to operate on the same trusted data. This is a new data foundation for the AI enterprise. https://bit.ly/49X7fTM