MinIO Blog

AI has shifted the baseline for infrastructure, from managing petabytes to operating seamlessly at exabyte scale. Agentic systems, long-context language models, simulation pipelines, and large-scale observability now demand storage architectures purpose-built for this data reality. Today we’re thrilled to announce ExaPOD, MinIO’s validated reference architecture for the exascale era: a one-exabyte, highly dense and efficient building block that

As a MinIO Curriculum Engineer, I spend a lot of time creating demos and learning environments for customers. These environments need to be realistic, comprehensive, and ready to showcase MinIO AIStor's capabilities in real-world scenarios. But here's the thing - setting up a proper demo environment manually can take hours of low-value, repetitive work. Let me

How does Exness handle massive data volumes and demanding AI/ML workloads? By moving to an on-prem infrastructure powered by MinIO. From scaling their data lake to managing traffic peaks of 200 Gbps, MinIO supports their AI workflows, disaster recovery, and more.

As AI workloads drive cloud costs through the roof, many companies are rethinking their approach. Moving select AI tasks back on-prem offers a path to predictable costs, improved performance, and stronger data control.

AIStor is a foundational component for creating and executing complex data workflows. At the core of this event-driven functionality is MinIO bucket notifications using Kafka.

In this blog, we will demonstrate how to use MinIO to build a Retrieval Augmented Generation(RAG) based chat application using commodity hardware.