AIStor helped us turn what was once a fragile, monolithic system into a very forward looking Data Lakehouse that supports a true hybrid cloud.
AIStor’s simplicity is an order of magnitude difference.
— Conor Brennan, Risk IT Lead
Nomura
Why AIStor Outperforms Legacy Approaches
AIStor eliminates cloud costs and legacy infrastructures that hold back your data strategy.
Predictable Economics
Build modern lakehouses on-prem with standard hardware. No egress fees, API charges, or performance tier premiums. 2-5X faster operations across PUT, GET, and LIST workloads versus S3 Express.
Open Standards, Zero Lock-In
Apache Iceberg runs up to 25X faster than Parquet external tables. Your data, your rules, your choice of tools—no proprietary SQL dialects or platform-specific training required.
Replace Legacy Infrastructure
10X better performance than MapReduce. No NameNodes, DataNodes, or complex rebalancing. S3 API integrates with your entire data stack.
Designed for AI
Design for AI workloads from day one. Mixed workloads including ML, vector search, and unstructured data alongside traditional analytics.
Data Lakehouse Solutions
Modernize your data infrastructure on a foundation designed for AI's future.
Build modern, performant lakehouses on-prem with standard hardware and experience immediate cost savings. Predictable amortization with superior S3 Express API performance without the cloud tax. A disaggregated architecture scales storage and compute independently, optimizing each separately.
Stop copying data between training and serving systems. Train and serve models directly on object storage without migration. Store all modalities—video, text, images—in one place with unified data infrastructure for RAG, computer vision, and traditional analytics. Making good architectural choices from the start means increased optionality over time.
A modern lakehouse combines object storage with Apache Iceberg, query engines like Trino or Spark, and a REST catalog layer—either internal like AIStor Tables, or external like Nessie or Polaris. Storage and compute scale independently, and multiple engines can access the same data without migration. Open standards ensure interoperability and prevent lock-in.
A modern lakehouse combines object storage with Apache Iceberg, query engines like Trino or Spark, and a REST catalog layer—either internal like AIStor Tables, or external like Nessie or Polaris. Storage and compute scale independently, and multiple engines can access the same data without migration. Open standards ensure interoperability and prevent lock-in.
19.2 TB/s throughput at 1.0 EiB via a 640-node cluster with 1:1 Clos leaf‑spine fabric and 400 GbE.
Enterprise Security
Customer-managed encryption with granular controls. Faster and more secure data retrieval for successful lakehouse initiatives.
Global Namespace
Instant data availability across locations. Open standards ensure interoperability and prevent lock-in.
Apache Iceberg Integration
Works with almost every component of the modern data stack. Organizations who build on-prem gain ownership of their data and control over how they use it.
Stop Accepting Compromises
Build your data infrastructure on a foundation designed for AI's future. The data lakehouse market is projected to reach $74 billion by 2033. Get cloud-native, software-first scaling with on-prem economics and open standards with enterprise reliability.
.svg)
.avif)
