Power all AI workloads on a single, unified data lakehouse foundation.
MinIO AIStor combines a built-in Iceberg V3 REST Catalog, structured tables in open table formats and S3 object storage in one layer, so every engine reads the same governed data.
Three ways the storage layer holds lakehouses back, and how AIStor fixes each one.
Tables in one system, unstructured data in another, and a separate catalog service to keep in sync. Every copy is one more thing to govern.
Appliance renewals, egress and per-operation fees grow faster than the data, so the budget is hard to forecast.
NameNodes, DataNodes and constant rebalancing, while the skills to run HDFS disappear from the market.
Most lakehouses run object storage, a table format and a separate catalog service that has to be deployed, secured and kept in sync. With the Apache Iceberg V3 REST Catalog built directly into the store, your engines read one governed copy with one less system in between.
Read the architecture, get the brief, or dive into the research.
How the catalog, table format and object store fit together, and why building the Iceberg catalog into the store removes a whole service to run.
The first data store with a built-in Apache Iceberg V3 catalog. Every engine reads one governed copy, on infrastructure you control.
Traditional, generative and agentic AI each need different things from your data infrastructure, but they don't need separate stacks. RAG vs. fine-tuning, MLOps and GPU throughput.
How AIStor meets the five requirements of an AI-ready lakehouse, from a built-in Iceberg REST catalog to OpenSharing for querying on-prem tables in place.
How data teams put AIStor under their lakehouse to consolidate data, retire legacy storage and get ready for AI.
