Unified Industrial Data. Built for Cost Efficiency.
Expensive, slow, fragmented data silos make it nearly impossible to analyze IoT and Operational Technology (OT) data, and margins erode through downtime, scrap, energy waste, and missed shipments.
AIStor centralizes and analyzes 2-3× more industrial data for the same cost, from edge to enterprise.
High-performance storage for IoT, OT, and manufacturing AI.
Predictive Maintenance
Power ML models that predict asset failures before they impact production, improving uptime and reducing maintenance waste through smarter interventions.
AI-Powered Quality Inspection
Feed GPU inference at line speed for real-time defect detection, replacing manual QA processes across production lines.
Historian Modernization
Consolidate data from OSIsoft PI, AVEVA, Honeywell PHD, and similar systems into scalable, open-format storage that unifies process history for analytics.
IT/OT Convergence
Unify SCADA, MES, and historian data on a single platform, breaking down the siloed environments that prevent cross-system analysis and industrial AI at scale.
How It Works
AIStor provides a unified data layer that centralizes OT data for analytics and AI, without touching the control systems that generate it.
Separate Analytics from Control
AIStor sits alongside the control loop, not inside it, protecting OT safety while unlocking the data.
Data from PLCs, SCADA, historians, and edge gateways replicates via S3
Control systems stay isolated and untouched
Analytics workloads run against the AIStor copy
Edge-to-Core Data Pipeline
Deploys at the edge and replicates to a central store, so replication is built into the storage layer.
Local buffering, preprocessing, and inference at the edge
Centralized aggregation, long-term retention, and cross-plant analytics at core
Active-active, sync, or async replication based on network topology
Sensor-Scale Ingest Performance
Handles the velocity of industrial IoT without metadata bottlenecks or degradation.
Up to 800 Gbps per storage server with linear scaling
Supports kHz vibration sensors, GB/s vision systems, thousands of historian tags/sec
No performance cliffs as data volumes grow
Full-Fidelity Process Data Retention
Retain years of raw sensor data, not downsampled summaries, at sustainable economics.
Erasure coding stores 2-3× more data for the same cost
Predictive maintenance baselines, quality traceability, root cause forensics
Full batch genealogy for recall readiness and compliance
Lakehouse Integration for OT Analytics
Replace proprietary historian query languages with standard SQL on open table formats.
Direct connection to Databricks, Starburst, Trino, Spark, and ClickHouse via S3
AIStor Tables enables Delta Sharing across plants and teams
Open formats eliminate vendor lock-in on the analytics layer
AI Inference at the Edge
Feeds GPU inference engines at line speed for real-time quality inspection on the factory floor.
S3-compatible reads go directly to the inference pipeline
No middleware or protocol translation between storage and GPU
Purpose-built for single-rack, factory-floor AI deployments
MinIO AIStor has enabled us to scale our smart metering infrastructure faster and more efficiently than we imagined. The time savings, simplicity, and performance have been game-changing.
Infrastructure Team
Global Electric Utility Provider
Proven Results
Quantified outcomes from AIStor customer production deployments.
>50% lower TCO and 86% faster deployment vs. Hadoop
A major global electric utility replaced Hadoop with AIStor for their smart meter telemetry data lakehouse—deploying on 90 servers in 10 weeks vs. 240 servers and 14 months estimated for Hadoop, with less than 1 FTE to manage vs. 3 FTEs for the legacy stack.
A global telecommunications leader replaced their Hadoop-based storage with an AIStor-powered data lakehouse spanning 80+ petabytes, decoupling compute from storage to eliminate the inefficiency of scaling both simultaneously—and positioning the enterprise for GPU-driven AI workloads.
AMD built a Data Intelligence Platform on AIStor that automatically links ServiceNow tickets, Jira issues, GitHub commits, telemetry, and infrastructure logs into a graph-aware reasoning layer—eliminating duplicate data stores and enabling both human teams and AI agents to trace issues to root cause across the full engineering lifecycle.
A leading life sciences company scaled to 20+ PB across lab clusters, HPC systems, and public cloud—replacing NAS that couldn't keep pace with 2.2M weekly experiments and continuous streams of microscopy images, while cutting cloud egress costs and accelerating ML pipeline efficiency.
Organizations apply AIStor for industrial and operational analytics across industries.
Financial Services
Data center infrastructure monitoring
Facility management telemetry
Physical security sensor analytics
Telecom
Cell tower equipment health
Network infrastructure telemetry
Edge site environmental monitoring
Life Sciences
Lab equipment sensor data
Cleanroom environmental monitoring
Bioreactor process telemetry
Manufacturing
Predictive maintenance and asset health
Production line quality analytics
Smart meter and energy telemetry
Media
Broadcast equipment monitoring
Studio infrastructure telemetry
Transmission system health
Gaming
Server farm infrastructure monitoring
Edge compute telemetry
Hardware fleet lifecycle analytics
Unified Data. Scalable AI. Protected Margins.
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